Light and Emissions

Home , Kerosene lamp

LIGHT AND EMISSIONS: DEVELOPING A TEST FOR FUEL-BASED LANTERNS by URSULA BRYN GRUNWALD B.A., University of Colorado Boulder, 2020 B.S., University of Colorado Boulder, 2020

A thesis submitted to the Faculty of the Graduate School of the University of Colorado in partial fulfillment

of the requirement for the degree of Master of Science Department of Mechanical Engineering 2020

Committee Members:

Michael Hannigan

Jana Milford

Julie Steinbrenner

Grunwald, Ursula Bryn (M.S., Mechanical Engineering)

Lighting and Emissions: Developing a Test for Fuel Based Lanterns

Thesis directed by Professor Michael P. Hannigan

Abstract

Fuel-based lanterns are used in much of the developing world, with an estimated 1.3 billion people relying on it for their daily light. However, kerosene is expensive, dangerous, and does not produce much useful lux, which is limiting to students, who might want to be able to study at night. Much research into the problem of lighting in developing countries stresses the need and importance of transitioning everyone to emissions-free, low energy using LED lanterns.

However, this may take many years, indicating that there is a need to improve fuel-based lanterns while access is expanded. There is a test for testing different iterations of cookstoves in terms of their efficiency and emissions, but no equivalent test exists for lanterns. This work devised a standardized test to compare different components of lanterns, using a four stage test that includes a clean start, off period, dirty start, and "simmer" in order to reflect different stages of lantern use. Components of the lantern can be modified and the testing protocol can be utilized to identify the influence of the variable on lantern performance, such as the wick material, lantern type (hurricane versus simple wick), or level of cleaning. In the case of this work, the test was utilized to study different lantern fuel types, these being kerosene, paraffin lamp oil, and a kerosene-0.5% ethanol by weight mixture. A two-way ANOVA was then performed on the resulting tests to determine the influence of fuel type and testing stage on lighting efficiency, light stability, and carbon monoxide emission factors per gram of fuel.

Analyzing the tests found a replicable pattern for all fuels in terms of carbon dioxide emissions, and calculations were performed to understand the efficiency of the fuel, the stability of the lux output, and the emissions as a function of their useful light. Using the test, it was determined that in levels above 0.5% by mass, ethanol is too unstable to be added to kerosene and compromises the light output and stability. However, below 0.5%, ethanol somewhat reduces exposure to

ii carbon dioxide and carbon monoxide while not compromising light stability or efficiency. Such tests are important for determining what types of fuels or best practices could be prescribed while access to more efficient and less polluting forms of light is expanded.

iii

Acknowledgements

I would like to express my gratitude to:

• My adviser, Dr. Michael Hannigan, for his guidance and patience with developing the testing procedure, even when nothing about it seemed to make sense.

• Evan Coffey, for his advice and guidance through the more complicated aspects of standardizing the test, as well as calculating the emission factors.

• My friends, for their immense patience as I repeatedly explained the same three things.

• My partner, Gage Froelich, for being patient with the constant, lingering smell of kerosene while I was working on the testing procedure.

Contents

CHAPTER 1: INTRODUCTION AND BACKGROUND ...... 1

1.1 Motivation ...... 3

1.2 United Nations Development Goals ...... 4

1.3 Development of the Research ...... 5

CHAPTER 2: LITERATURE REVIEW ...... 6

2.1 Light ...... 6

2.2 Lighting as a Service ...... 7

2.3 Light Quantity ...... 9

2.4 Light Quality ...... 10

2.5 Kerosene Lamps ...... 11

2.6 Indoor Air Quality ...... 15

2.7 Ventilation...... 18

2.8 Water Boiling Tests...... 20

2.9 Gaps in the Research ...... 22

2.10 Goals of the Research ...... 23

CHAPTER 3: METHODOLOGY ...... 25

v 3.1 Materials ...... 25

3.2 Development of Methodology ...... 28

3.2.1 Initial Tests ...... 28

3.2.2 Half-Hour Tests ...... 30

3.2.3 Electric Lanterns ...... 33

3.3 Final Methodology ...... 34

3.3.1 Set Up ...... 35

3.3.2 Clean Start ...... 36

3.3.3 Dirty Start ...... 37

3.3.4 Simmer Phase ...... 38

3.3.4 Analysis and Calculations ...... 38

CHAPTER 4: APPLICATIONS OF THE TESTING PROTOCOL ...... 42

4.1 Initial Tests Before Standardized Test ...... 42

4.2 Standardized Test - Kerosene ...... 44

4.3 Standardized Test - 0.5% Ethanol ...... 51

4.4 Standardized Test - Lamp Oil...... 58

4.5 Standardized Test - 1% Ethanol ...... 65

vi CHAPTER 5: FINDINGS DISCUSSION ...... 69

5.1 Test Design and Performance ...... 69

5.2 Two-Way ANOVA ...... 70

5.2.1 Efficiency ...... 72

5.2.2 Light Stability ...... 75

5.2.3 Carbon Monoxide Emission Factors Per Lux ...... 78

5.3 Conclusions ...... 80

CHAPTER 6: CONCLUSIONS ...... 82

6.1 Summary of Thesis Achievements ...... 82

6.2 Applications ...... 83

6.3 Future Work ...... 84

Bibliography ...... 86

Appendix ...... 91

vii List of Tables

Table 1. Summary of the 1% ethanol tests………………………………………………………68 Table 2. ANOVA table from lux efficiency tests………………………………………………..73 Table 3. ANOVA table from light stability tests. ……………………………………………….76 Table 4. ANOVA table from CO EF per lux tests……………………………………………….78

viii

List of Figures

Figure 1. Recommended lux for different tasks...... 7 Figure.2. Electromagnetic spectrum, with the visible light spectrum highlighted (Franson 2012). 8 Figure 3. Improvements in test score (fall to spring) of students in classrooms with better daylighting ...... 11 Figure 4. National Ambient Air Quality Standards (adapted from the EPA) (Berstein et al 2008)...... 16 Figure 5. 2001 ASHRAE standards for outdoor air requirements for ventilation of residential facilities (ASHRAE, 2003)...... 19 Figure 6. Temperature during the three phases of the water boiling test (Figure credit: Nordica 21 Figure 7. Dietz 76 Original Oil Burning Lantern, which was used during the test...... 26 Figure 8. The PCE-174 lux meter from PCE Instruments...... 27 Figure 9. Example of the set up for cardboard markings...... 27 Figure 10. Lux comparison between ethanol and kerosene from initial tests...... 29 Figure 11. Visual lux comparison between ethanol and kerosene from initial tests...... 30 Figure 12. Lux from a half hour test of kerosene testing...... 31 Figure 13. Initial testing setup during the half-hour phase...... 32 Figure 14. Lux from a half hour of 10% ethanol and 90% kerosene mixture...... 33 Figure 15. Lux from an LED electric lantern...... 34 Figure 16. Example of the testing set up just before conducting a test...... 36 Figure 17. Demonstration of the equipment positioning during the test...... 37 Figure 18. Lux from 100% being used as a lantern fuel...... 43 Figure 19. CO2 from 100% ethanol being used as lantern fuel...... 43 Figure 20. Lux from a fuel mixture that was 90% kerosene and 10% ethanol...... 44 Figure 21. Lux from all kerosene tests...... 45 Figure 22. Efficiency of the kerosene lantern tests (lux/gram fuel)...... 45 Figure 23. Standard deviation of lux during the kerosene test (30 second intervals)...... 46 Figure 24. CO2 in the emissions plume during the kerosene tests in ppm...... 47 Figure 25. CO in the emissions plume during the kerosene tests in ppm...... 48 Figure 26. CO2 emission factors from the kerosene tests (g pollutant/kg fuel)...... 49 Figure 27. CO emission factors from the kerosene tests (g pollutant/kg fuel)...... 49 Figure 28. Average CO2 concentration in ppm from kerosene, divided by average lux...... 50 Figure 29. Average CO concentration in ppm from kerosene, divided by average lux...... 51 Figure 30. Lux from 0.5% ethanol...... 52 Figure 31. Efficiency of the 0.5% ethanol lantern tests (lux/gram fuel)...... 52 Figure 32. Standard deviation of lux during the 0.5% ethanol test (30 second interval)...... 53 Figure 33. CO2 emissions in ppm in the emissions plume from all 0.5% ethanol tests...... 54 Figure 34. CO emissions in ppm in the emissions plume from all 0.5% ethanol tests...... 55 Figure 35. CO2 emission factors from the 0.5% ethanol tests (g pollutant/kg fuel)...... 56 Figure 36. CO emission factors from the 0.5% ethanol tests (g pollutant/kg fuel)...... 56 Figure 37. Average CO2 concentration in ppm from 0.5% ethanol, divided by average lux...... 57 Figure 38. Average CO concentration in ppm from 0.5% ethanol, divided by average lux...... 58 Figure 39. Lux from paraffin lamp oil...... 59 Figure 40. Efficiency of the paraffin lamp oil lantern tests (lux/gram fuel)...... 60

ix Figure 41. Standard deviation of lux during the lamp oil test (30 second interval)...... 61 Figure 42. CO2 in the emissions plume from paraffin lamp oil...... 61 Figure 43. CO in the emissions plume from paraffin lamp oil...... 62 Figure 44. CO2 emission factors from paraffin lamp oil (g pollutant/kg fuel)...... 62 Figure 45. CO emission factors from paraffin lamp oil (g pollutant/kg fuel)...... 63 Figure 46. Average CO2 concentration in ppm from lamp oil, divided by average lux...... 63 Figure 47. Average CO concentration in ppm from lamp oil, divided by average lux...... 64 Figure 48. Lux from a 1% ethanol mix...... 65 Figure 49. Standard deviation of lux during the 1% ethanol test (30 second intervals)...... 66 Figure 50. CO2 emissions from 1% ethanol mix...... 67 Figure 51. CO emissions from 1% ethanol mix...... 67 Figure 52. Standard ANOVA results table...... 71 Figure 53. Results from two-way ANOVA on light efficiency. (Tests by factor: kerosene (10), lamp oil (3), 0.5% ethanol (3), clean start (16), dirty start (16), simmer (16))...... 73 Figure 54. Interaction plot between fuel type and test stage of lux efficiency tests...... 74 Figure 55. Results from two-way ANOVA on light stability. (Tests by factor: kerosene (10), lamp oil (3), 0.5% ethanol (3), clean start (16), dirty start (16), simmer (16))...... 76 Figure 56. Interaction between test phase and fuel type for light stability...... 77 Figure 57. Results from two-way ANOVA on carbon monoxide emission factors. (Tests by factor: kerosene (10), lamp oil (3), 0.5% ethanol (3), clean start (16), dirty start (16), simmer (16))...... 79 Figure 58. Interaction plot between fuel type and test phase for the two-way ANOVA on CO EF per lux...... 80

x CHAPTER 1: INTRODUCTION AND BACKGROUND

Lighting is a vital part of success in the modern world. Without indoor light, it becomes exceedingly hard to perform tasks such as homework, indoor chores, or after-hours work required for a small business to perform. Yet parts of the world still lack consistent access to electricity, and thus it becomes much harder for individuals to work, read, or interact late at night, limiting the hours in the day that can be used for tasks.

A sixth of humanity spends upwards of $40 billion yearly on lighting, yet only gets 0.1% as much illumination as is common in developed countries (Mills 2013). 95% percent of the population that lacks consistent access to electricity are in Sub-Saharan Africa and developing

Asia, often in very rural areas (Uppari, Popescu, and Netessine 2017). This means that low- income individuals without access to an electric grid spend between as much as 100 to 1,000 times per unit of light as do people who are connected to the grid, and often are utilizing fuels that are high in greenhouse gas emissions (Mills 2015). As such, the costs of lighting keep individuals trapped in a cycle of poverty, and they are often exposed to dangerous, polluting light sources that are not only unhealthy for the planet, but associated with respiratory and other health risks for humans.

Kerosene lamps are an outdated means of providing light and are heavily relied upon in areas where there is no access to a grid (Uppari, Popescu, and Netessine 2017). They are expensive and produce a great deal of black carbon, which has a large impact on the climate.

Previous research has effectively shown the health impacts of utilizing kerosene lamps for long periods of time indoors – they contribute to respiratory ailments and have detrimental health costs, as well as potentially not providing suitable light for performing tasks such as reading.

They are also a fire risk and are likely to contribute to an estimated two million deaths per year, due to burns and carbon dioxide exposure (Uppari, Popescu, and Netessine 2017). Kerosene is a

1 clear fuel, and while it can be easily discolored to prevent confusion, it is sometimes consumed by children who mistake it for water, which leads to poisoning (Mills 2013). However, kerosene lanterns are still frequently used in developing countries, often due to the lower upfront cost.

Propane is another fuel that has been pushed, largely because it is cleaner-burning than kerosene, but propane lanterns are more expensive to purchase and operate than the standard kerosene exposed-wick lamp, which has limited their adoption (Mills 2013). Propane causes similar respiratory issues to kerosene, and it must be handled carefully. Both kerosene and propane tend to be available in markets that can be several miles away from residences, which takes time away from people as they walk to fetch additional fuel (Lam et al 2013). Biogas is another type of fuel that could hypothetically replace kerosene, and it could be locally produced from some types of native vegetation. Due to variation in production and types of plants used in the gas development, biofuels might not be as flammable as other liquid fuel sources (Chakraborty and

Sarkar 2017). Research into biofuel in lanterns has found that it burns without smoke and a given amount of biofuels often sustain illumination longer than kerosene, which could be useful in economic terms (Chakraborty and Sarkar 2017). However, all burning of fuels exposes individuals to carbon dioxide, particulate matter, and other components that could contribute to chronic health issues.

Solar-powered LED lights with rechargeable batteries are often floated as a solution to this problem, but the upfront expense, lack of education surrounding the use of solar-powered lights, and access to maintenance knowledge in case the light is damaged can hinder their adoption

(Uppari, Popescu, and Netessine 2017). Although LED lights are often more cost-effective over their lifetime, once fuel costs and maintenance are accounted for, the upfront expense is often enough to act as a deterrent (Mills 2013). As a result, many people continue to rely on fuel-based lanterns. A complete and total switch from fuel-based lanterns to LED and solar powered lanterns would likely result in optimal health outcomes and increase the amount of illumination users can utilize, but as it stands, fuel-based lanterns will continue to be relied upon for the

2 foreseeable future. As such, it is vital to understand the impact of different fuel types on emissions, light output, and economics.

1.1 Motivation

This work was inspired in part by a year I spent living in the Philippines from 2013 to 2014.

The Philippines is a developing country, where about 800,000 households use kerosene lanterns for light (PATH, Foundation Philippines, 2018). The Philippines is also disaster-prone, and is particularly impacted by typhoons, which can cause flash floods that sweep coastal villages into the sea and leave communities without electrical power for weeks on end. Along with the households that commonly use kerosene as a source for lighting, many other households keep lanterns on hand for such disaster scenarios.

In 2013, Typhoon Haiyan struck the Philippines, and was one of the strongest typhoons ever recorded. Winds reached 195 mph, and in 2013, was the fourth strongest cyclone ever recorded

(Fischetti, 2013). Haiyan had an estimated economic impact of $5.8 billion, with six million workers losing their source of income. Fishing communities were severely impacted, with the storm destroying 30,000 boats and personal equipment (Aljibe, 2018). Over seven thousand people died, 27,000 were injured, and more than four million displaced (Aljibe, 2018). At the time,

I was living in Bacolod City, on Negros Island, attending classes as part of the Rotary international student exchange program. The storm disrupted power, leaving my area without electricity for over a week. Fuel based lamps and a generator were required for power at my host family’s house until the power was restored, which highlighted the relative stability and accessibility of light I was accustomed to in the United States. Many areas had not fully recovered their infrastructure before Typhoon Ruby struck a year later.

The incident highlighted to me the importance of emissions-free and stable light. Kerosene

3 lighting is dangerous, and many people are sent to the hospital with injuries or killed due to burns from kerosene lights. Lights can tip over and set houses on fire, and the emissions are harmful to breathe. As such, finding ways to expand access to clean, affordable lighting that could be reliant in a disaster zone or expand opportunities for families later into the evening has become important to me. Such easy access to light is a blessing that can and should not be forgotten.

1.2 United Nations Development Goals

In September 2015, the United Nations’ General Assembly adopted the 2030 Agenda for

Sustainable Development, which included seventeen Sustainable Development Goals, which emphasized "leaving no one behind (United Nations 2015). Goal Number 7 is Affordable and

Clean Energy, which includes sections on using energy efficient appliances and light bulbs

(United Nations 2015). The United Nations notes that energy is central to development, and that encouraging signs exist that energy is becoming both more sustainable and widely available.

However, it is also noted that more focused attention is needed to improve access to such technologies, particularly in sub-Saharan Africa. Light is a vital part of the modern world, and expanding access to safe, inexpensive light would not only help with Goal Number 7, but also with Goal Number 13 of Climate Action, as the fuels that are often used are dangerous for the environment and release a great amount of greenhouse gases. Improving light access would help with Goal Number 4 as well, which is Quality Education, as safer, cheaper light would make it easier for students and childhood to study later into the evening or complete their work. In addition, improving lighting access would also help with Goal Number 3, which is Good Health and Well Being. Lantern fuels are often dangerous, sending millions to the hospital with burns, and the combustion of such lanterns can be harmful to human health and welfare. Overall, improving access to safer lighting would help alleviate poverty and offer many a better quality of life.

4 1.3 Development of the Research

In the beginning, it was assumed that the thesis would be more concerned with the elements of a lanterns, rather than how to test them. This would include elements such as the fuel type, wick material, and the lanterns itself, as well as "step-up" lanterns, such as propane lanterns.

However, when testing commenced, it became clear that the plan would need to be modified, as much was learned about light along the way - such as how much distance impacts the lux output of the lantern, as well as aspects such as decline in the light output over time, and how raising the wick can impact the lux output of the lantern over time. As such, the research became more focused on finding the most suitable way to test the lanterns and concentrated more and more on the development of the testing procedure. As a result, the thesis work focuses more on the creation of a standardized test to compare lanterns in a quantitative manner.

5 CHAPTER 2: LITERATURE REVIEW

This thesis draws on areas of light, emissions, and fuel, which requires an understanding of an array of distinct subjects to understand the significance of the work. The work also spans the disciplines of indoor air pollution and ventilation, as well as some of the health impacts of indoor air pollution, and the water boiling test, which is used in understanding the impact of cookstoves in developing countries. The following literature review discusses the topics in further detail.

2.1 Light

Much of the world still lacks consistent access to light, with an estimated two billion people lacking electricity. This means that after sunset, they experience virtually nothing but darkness

(Peon et al 2005). Access to light is linked to literacy, and a lack of light makes it difficult to perform evening activities, such as evening studies by children, which presents a significant barrier to human development (Peon et al 2005).

Light can influence several aspects of human life, ranging from alleviating seasonal depression, to improving night-shift worker performance, to weight gain in premature infants and to regulating melatonin (Brown 1994). Light/dark cycles regulate human behavior, in other words. Most light is measured on how much light falls on a horizontal work surface, not the amount of light that is available to a retina. Figure 1 shows recommended lux for different tasks.

Figure 1. Recommended lux for different tasks.

For settings such as classrooms, it is generally recommended that educational spaces require lighting levels of approximately 150 lux, as measured by the amount of light on the students’ desks (Salaray et al 2015). Children’s eyes have lenses that are more transparent and pupils that are larger than adults, which results in children being more sensitive to light levels than adults

(Akacem, Wright and LeBourgeois 2018). As a result, children can see adequately in light levels that are too low for adults (Gislen, Gustafsson and Kroger 2008).

2.2 Lighting as a Service

When discussing lighting as an energy service, it is important to understand the difference between lumens and watts. Watts are a measurement of energy use, rather than brightness, whereas lumens are a measurement of the brightness of a light source (Bradford 2018). Lux is the illuminance, or the amount of light cast onto a surface, which can also be thought of as light intensity within a specific area (Bradford 2018). One lux is equal to one lumen per square meter.

7 Visible light is a form of electromagnetic radiation and is defined as the wavelengths that are visible to most human eyes (Lucas 2015). As seen in Figure 2, visible light falls in the electromagnetic spectrum between infrared and ultraviolet light. It has wavelengths of about

740nm to 380nm.

Figure.2. Electromagnetic spectrum, with the visible light spectrum highlighted (Franson 2012).

One of the most important factors of visible light is color, which is both an inherent property of light and an artifact of the human eye (Lucas 2015). Objects do not "have" color, but rather give off light that "appears" to be a color. Light at the lower end of the visible spectrum, with wavelengths of about 740nm, is seen as red, whereas light at the upper end of the spectrum, with a wavelength of 380nm, is seen as violet. Green in the middle of the spectrum is seen as green, and all other perceived colors are a mixture of the three previously listed colors.

Objects radiate energy dominated by shorter wavelengths when they are heated, which is perceived as changing colors (Lucas 2015). The process of turning heat energy into light energy

8 is called incandescence, and incandescent light is produced when hot matter releases a portion of its thermal vibration energy as photons (Lucas 2015). When a fire burns, chemical energy is released in the form of light and heat (Spring and Davidson n.d.). The burning fuel, such as kerosene, emits gases that are heated by the chemical energy that is generated during combustion, which causes atoms in the gas to incandesce. Electrons in the gas atoms are promoted to higher energy levels by the heat, and light is released in the form of photons when the electrons fall back to their ground state (Spring and Davidson n.d.). Originally, tar and rags were employed to be used as early torches, but this method eventually gave way to the use of oil lamps (Spring and Davidson n.d.). Oil lamps and candles were commonly used until the nineteenth century, in which natural gas lighting became widespread throughout Asia, Europe, and the United States (Spring and Davidson n.d.). Early gaslights operated by producing a jet of burning gas, which was later modified by fitting it with a mantle, which dispersed the flame and emitted a brighter light (Spring and Davidson n.d.).

2.3 Light Quantity

Lumens are the SI unit of luminous flux. A lumen is equal to the amount of light emitted per second in a unit solid angle of one steradian from a uniform source of one candela. In other words, lumens are equal to the brightness of a light source (Blevin and Steiner 1975). A 60-Watt incandescent bulb, for example, outputs 800 lumens. In contrast, a kerosene lamp outputs one to six lumens per square meter (lux). A candle gives off about 13 lumens. A living space of around

250 square feet would require about 5,000 lumens as a primary light source. If the furniture or walls are dark, an additional ten lumens per square foot are needed.

Insufficient or inappropriate light exposure can disrupt human standards, such as melatonin production, which can present adverse effects for performance, safety and health (Bellia, Bisegna

9 and Spada 2011). Too much light pollution at night, for example, has created a problem known as "light pollution" - which is particularly problematic with the rise of white LED lights (Falchi et al 2011). It was determined that a rise in LED lighting for nighttime purposes might be suppressing melatonin production in humans, which interferes with the circadian clock and cause alertness, contributing to sleep and metabolic disorders (Falchi et al 2011). The nighttime increase of light intensity is likely contributing to negative impacts on human health.

2.4 Light Quality

Light quality cannot be directly measured, but there are about eleven factors that influence lighting quality. These include aspects of quantity, distribution, flare, spectral power distribution, daylight, directionality, and dynamics of the light. There is no consensus of what lighting quality is, which can make it difficult to determine what light qualifies as good. However, when attempting to adjust one of the factors, others invariably diminish as a result.

In the early 2000s, a non-image-forming receptor was found in the human eye, which impacts human health and wellbeing (Schmift and Kofuji 2008). Burning words and candles have a color temperature of around 1800K, which is high in red and yellow. Incandescent bulbs have color temperatures of around 2400K – less blue light, and more yellow and red wavelengths. White LEDs, however, tend more towards the blue spectrum. This can make the light look harsh, and increases glare – LED lights are concentrated, and have a high blue contract

(Falchi et al 2011). This can result in pupillary constriction in the eyes. Additionally, blue light scatters more in the human eye than do lights with more red or yellow wavelengths, which can damage the retina if the light is bright enough. Blue lights can also influence circadian rhythms

10 in humans. White LED lights, due to the concentration of blue light waves, suppresses melatonin, which interferes with sleep. As such, the American Medical Association recommends for correlated color temperatures (CCT) for light below 3000K (Falchi et al 2011).

Human eyes are accustomed to daylight. Some studies have examined the impact of natural light on the performance of students in classrooms and studied if their math and reading scores improved when the room was lit by natural light, rather than overhead lights (Heschong 2002).

Figure 3 shows the improvements in test scores of students with more daylighting.

Figure 3. Improvements in test score (fall to spring) of students in classrooms with better daylighting (Heschong 2002).

Students with more daylighting in their classroom were found to progress 20% faster on math tests and 26% on reading tests in one year when compared to those with the least daylight

(Heschong 2002). Such tests indicate that the light quality, not just the quantity of light, might be important for improving performance and health.

2.5 Kerosene Lamps

Kerosene is both a massive expense for many families in developing countries and has serious consequences in terms of emissions. Since 1.3 billion people around the world do not have consistent access to electricity, kerosene is often what is used. Fuel based lighting, such as a

11 simple kerosene lamp, do not have many benefits. In terms of lighting, it often only offers around 1 lux at a distance of one meter, whereas it is recommended that 150 lux is used for studying and about 300 lux for living spaces (Mills 2005). Studies have found that performance on tasks is related to light, with performance going up with increased light levels (Joseph 2006).

Since many people utilize kerosene lamps as their main light source in the evenings, and lack of light can be a hindrance to children working to complete homework in these areas, it’s important to understand the full range of impacts of kerosene lamps and potential mitigation or improvements.

The health impacts of kerosene are significant. Kerosene lamps emit a significant amount of black carbon, which is estimated to be about 270,000 tons per year (Tedsen 2013). Kerosene lamps can impair lung function and increases the risk of respiratory disease, cancer, eye problems and diseases such as tuberculosis (Tedsen 2013). The lamps are also fire and safety risks – due to the flammability of kerosene, there is a high risk of burns and accidents (Tedsen

2013). Kerosene is also both explosive and poisonous, which has additional problems (Lam,

2012). The black carbon emitted has a significant climate impact, which leads to obvious health problems – the 270,000 tons emitted per year has a warming equivalent of 240 million tons of carbon dioxide (Tedsen 2013). The simple wick lamps, which use a rope or a cloth wick produce the greatest amount of black carbon emissions, while hurricane lamps or pressurized mantle lamps emit substantially less (Tedsen 2013). Kerosene does not emit similar levels of black carbon when used for cooking, and the emission rate is approximately that of wood cooking

(Tedsen 2013). Simple wick lamps also produce high levels of PM2.5 that violate general guidelines for exposure, while hurricane lamps reduce exposure to PM2.5 and PM10, but will experience elevated levels of 0.02–0.3µm particle concentrations, which is particularly a

12 problem when the kerosene lamps are used indoors (Apple et. al 2009).

The light that kerosene lamps produce is also not proportionate to the amount of harm caused, and kerosene lamps are not efficient at producing visible light. Around 0.6% of the radiation from the kerosene flame is within the visible range of 380 - 750 nm as compared with nearly 44% from the sun (Starr et. al 2010). Human eyes and their photopic sensitivity register the light of the lantern as being 0.096% efficient and the sunlight being 15% efficient (Starr et. al

2010). Electric lights are more efficient than non-electric lights, and the energy consumption of a non-electric light is 65 times higher than that of the electric light (Nieuwenhout 1998). Solar lanterns have many times the efficiency of kerosene lanterns in terms of the luminous efficiency, making them an overall superior choice to the poor output of the kerosene lamps (Mills 2003).

Kerosene lamps are also exceedingly expensive. The fuel for lighting alone can cost up to

20% of a family’s income, and the simple wick lamps were the most expensive out of all kerosene lamps (Mills 2003). Compact florescent lights and LED lanterns are much less expensive, and in recent years, the costs have dropped tremendously for things such as solar- powered LED lanterns with significant battery life. Even in the early 2000s, kerosene lamps operated at a significant disadvantage to LED systems. The cost-benefit analysis tilts further in the favor of LED lights when the optical quality of the lights is compared, since the quality and quantity of the kerosene lamps is so low in comparison to LED lanterns (Mills 2003). However, solar lanterns and LED lights have a relatively high upfront cost, which can deter individuals from choosing to invest in them (Mills 2003). Kerosene lamps have an upfront lower cost and pay what appears to be manageable weekly payments for the fuel required to power it (Mills

2003). Although solar lanterns are cheaper in terms of economics, healthcare, and climate

13 implications over the long run, the initial cost can serve as enough of a deterrent to dissuade people (Mills 2003). In 2010, a solar lantern would need four years to have a payback period compared with operating a kerosene-fueled hurricane lamp. In poorer areas, where people have both limited income and understanding of solar power, this can serve to push people away from investing in another option.

There is previous work examining the energy outputs and emissions produced by kerosene lanterns. Lawrence Berkeley National Laboratory tested the energy utilization of four kerosene lanterns - three of which had a flat-wick hurricane-style design, with the other lantern being a simpler "oil-style" lantern with a cylindrical wick (Mills 2003). Testing in this case consisted of burning the lanterns for long durations of time to test wick consumption, with analysis examining liters of fuel used per hour, lumen output, and efficiency. Lumens were measured in a radius around the lantern. This provides a useful overview of the efficiency of different lanterns, but the useful of the light - the task it could be used for - is unspecified, rather than being used for performing a certain task (i.e. reading, cooking). Other work compared the geometry of lux from a fuel-based lantern to a white light emitting diode (WLED) (Evans 2005). Gonio- photometry is an established method for evaluating electric light sources and provides a quantitative analysis of light distribution in various directions (Evans 2005). This test operated the lanterns for approximately ten hours, and revealed significant soiling on the lantern’s chimney, which decreased the overall lux and interfered with the dispersion of the light (Evans

2005). Such testing provides useful insight into fuel-based lanterns, but there are other factors that could influence a lantern’s performance - the cleanliness of the wick, whether the light level is to be maintained, etc. Individuals also might use the lanterns differently than how they are being tested in laboratory conditions, which could impact the lux output, as well as the buildup

14 of soot on the inside of the lantern. Such factors are also important to consider when devising testing schema. Other tests have examined the particulate levels from lanterns and have found that the use of fuel-based lanterns results in concentrations that exceed ambient health guidelines

(Apple et al 2010). The study tested several types of kerosene lanterns, such as simple wick or hurricane lanterns, and found that simple wick lamps alone would expose individuals to PM2.5 concentrations an order of magnitude greater than ambient health guidelines (Apple et al 2010).

Hurricane lamps reduce the exposure to PM2.5 and PM10 by an order of magnitude. Such work shows that the choice of light can not only impact the lux output of the lantern, but also the surrounding air quality, with associated health impacts.

2.6 Indoor Air Quality

Indoor air quality has been shown to have influence on rates of respiratory disease, allergies, and overall performance. Americans spend approximately 22 hours indoors every day, wherein they can experience chronic, low-level exposure to carbon monoxide, carbon dioxide, sulfur dioxide, passive smoke and volatile organic compounds (Bernstein et al 2008). Figure 4 shows National Ambient Air Quality Standards (NAAQS), set by the US Environmental

Protection Agency in order to protect outdoor air quality and improve human and health and welfare.

Figure 4. National Ambient Air Quality Standards (adapted from the EPA) (Berstein et al 2008).

However, there are no national standards for indoor air quality (Bernstein et al 2008). Some governmental agencies provide guidelines, but these generally take the form of recommendations for the control and elimination of sources, rather than achieving pollution levels below some specified air concentration, as with the NAAQs (Bernstein et al 2008).

Globally, indoor air pollution is a major contributor to the global burden of disease and is the second biggest environmental contributor to ill health (Franklin 2007). In the United States, it is often recommended to improve indoor air ventilation or to install filtration systems to help keep the air clean and minimize health impacts (Fisk and Rosenfeld 1997). Poor air quality is also devastating for children, who bear a disproportional burden of disease due to their indoor air

16 pollution (Franklin 2007). 93% of children around the world live in environments with air pollution levels about the World Health Standard, and one in four deaths of children under five is related to environmental problems (World Health Organization 2018). Poor air quality, particularly elevated levels of particulate matter, is linked with increased infant mortality (Heft-

Neal et al 2018). PM2.5 concentrations above minimum exposure levels were found to be responsible for 22% of infant deaths across 30 countries in a 2018 study and contributed to

449,000 additional infant deaths in 2015 (Heft-Neal et al 2018). In homes using kerosene as a heater, studies have found measurably higher pollution, such as PM2.5 and associated components (Ruiz et al. 2010). Homes using any form of combustion over electric heaters had much higher ultrafine particles and nitrogen dioxide levels (Ruiz et al. 2010). Nitrogen dioxide is linked to pneumonia and other respiratory infections in young children (World Health

Organization 2018). Particulate matter may also cause systematic inflammatory and immunological responses and elevate respiratory risks (World Health Organization 2018.

Indoor exposure to particulate matter is associated with increased health problems related to the respiratory system (Bernstein et al 2008). A study conducted of 421 houses in Italy found a positive correlation between indoor particulate matter exposure and asthma and bronchitis symptoms, particularly during the winter (Bernstein et al 2008). Indoor nitrogen dioxide exposure is also associated with enhancing asthmatic reactions to inhaled allergens. In the United

States, NO2 levels are elevated due to poor ventilation and frequent use of gas stoves (Bernstein et al 2008). Carbon monoxide is another pollutant caused by the combustion of organic material, and often is emitted from gas appliances (Bernstein et al 2008). Studies have found that CO, when compared to other indoor pollutants like NO2 or ozone, was the strongest predictor of elderly patients being hospitalized for congestive heart failure (Bernstein et al 2008).

17 Such exposure to indoor air pollution can cause building-related illnesses, such as "sick- building" syndrome. This is used to describe buildings where an excess about the expected numbers of occupants report symptoms of headache, skin or eye irritation, fatigue, or sore throat that occur when inside the building and are alleviated by being away from the building

(Bernstein et al 2008). Over time, the chronic exposure to indoor air pollution can result in severe health consequences for the building occupants.

2.7 Ventilation

Ventilation is an important part of indoor air quality, as proper ventilation can improve indoor air quality, control indoor humidity, and decrease airborne contaminants (EPA 2020).

Ventilation removes air pollutants originating inside of the building, and supplies outdoor air, which has been found to subjectively improve the indoor air quality and prevent the accumulation of moisture inside the building (Won 2004). Testing has shown that the performance of office workers can be greatly improved by removing common sources of air pollution, or by increasing the rate at which cleaner outdoor air was supplied (Wyon 2004).

Improving the ventilation rates reduced effects in office workers such as headaches (Wyon

2004). Adequate ventilation can reduce instances of "sick-building" syndrome, which is linked with problems such as asthma, lack of focus, headaches, fatigue, or allergies (Daisey, Angell and

Apte 2003).

When examining the impact of cookstoves, for example, it has long been understood that cooking with open fires indoors is problematic for human health and welfare. Studies in rural communities, such as those in Nepal, have found that there is an 80% deficit in ventilation as per

18 the minimal rate of ventilation recommended by the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) (Parajuli, Lee and Shrestha 2016). ASHRAE

Standards 62-2001 recommends an air change rate of 5L/s in kitchens on an intermittent basis, for example, or 12L/s on a continuous basis, and recommends that mechanical ventilation systems be used to accomplish this (ASHRAE 2003). More standards can be seen in Figure 5.

Figure 5. 2001 ASHRAE standards for outdoor air requirements for ventilation of residential facilities (ASHRAE,

2003).

In addition, the tests examining ventilation in the rural mountain households of Nepal found that often, the positioning of the chimney is in such a position to cause backflow, which would further increase the deficit in ventilation (Parajuli, Lee and Shrestha, 2016). The authors of the study prescribed a greater focus on ventilation as a means of controlling indoor air pollution in rural communities. Other studies examining the impact of cooking indoors with biomass in urban

Bangladesh have found that improved natural ventilation lowered the risk of children under five years old developing acute lower respiratory illnesses (ALRI), which is the leading cause of death for children in that age range (Murray et al, 2011). Windows appeared to be important in reducing the risk of ALRI, and such results suggest that improvements in natural ventilation might

19 lead to reductions in mortality among young children from ALRI (Murray et al, 2011). This literature indicates that ventilation is a vital part of indoor air quality, with important consequences in terms of human health and welfare. Given that lanterns emit a great deal of pollutants, ventilation is an important facet of understanding their impact, especially when used indoors.

2.8 Water Boiling Tests

The water boiling test (WBT) is a laboratory-based test, which is utilized to analyze how efficiently a stove uses fuel to heat water in a cooking pot, and the quantity of emissions produced while cooking (Clean Cooking Alliance 2014). It is a standardized test for cookstoves, meant to limit variability and control conditions. With initial variations of the test, fuel consumption and fuel emissions were a primary driver. However, later research determined that pollution produced from solid fuel use often have health and environmental impacts. The test was devised to allow for the testing of such emissions simultaneously with the measurement of fuel efficiency. Figure 6 demonstrates the different phases of the water boiling test.

Figure 6. Temperature during the three phases of the water boiling test (Figure credit: Nordica

MacCarty).

The initial phase is the cold-start phase, where the stove is at room temperature, and uses fuel from a pre-weighed bundle of fuel to boil a measured quantity of water in a standard pot.

Once the water is boiling, the tester replaces the hot water with a fresh pot of ambient water to perform the second phase. The second phase is the hot start phase and is conducted while the stove is still hot. Again, the tester uses a pre-weighed bundle of fuel to boil a measured quantity of water. This allows for the user to identify differences in performance between a stove when it is hot and when it is cold. The third phase is the simmer phase, which provides the amount of fuel required to simmer a measured amount of water at just below boiling for a period of forty- five minutes, which simulates the long cooking required for cooking legumes or pulses. A full

WBT always includes all three test phases.

While conducting the test, testers can use low-cost sensors to measure carbon monoxide

(CO), particulate matter (PM) and carbon dioxide (CO2) concentrations in the stove’s exhaust.

21 Other air pollutants can be measured as well, but the initial three are the most common ones for analysis. Once the test is completed, the tester can compare the concentration of the pollutants that are emitted by the fuel over the course of the test, as well as the amount of fuel used in order to boil water during each phase of the test. The latter serves as the efficiency, which helps testers understand how much fuel will be needed in order to get a useful output - this being the boiling water.

2.9 Gaps in the Research

As can be seen from the literature review, there is a great deal of work that has been conducted in light for developing countries, as well as the effects and causes of indoor air pollution. There also has been much work into a significant energy source and air pollution source in developing countries, this being the water boiling test. However, there is no similar test for lanterns, or other means of providing light, particularly one that contrasts emissions from different fuel tests.

Given that 1.3 billion people worldwide do not have consistent access to electricity, it follows that many people utilize fuel-based lanterns or combustion to obtain light. The water boiling test was devised as a means of objectively comparing cookstoves, based off their fuel efficiency and power output (i.e. time to boil water). There exists nothing similar for light, and as such, it can be difficult to objectively compare the different fuel-based lighting sources individuals might choose to use. Such a gap in the research is important to address.

There is also a question on the energy service of lanterns, as well as their conversion

22 technologies. Much work into kerosene examines the health impacts of the lanterns, as well as the economics of them and their environmental harms, but there is little work that objectively examines the work output of the lanterns, versus other alternatives, as well as the energy conversion technology. As discussed in Section 2.0.3, there has been some previous testing into fuel-based lanterns - largely performed by Evan Mills through the Lawrence Berkeley National

Laboratory. The tests and results have clarified that kerosene lanterns are expensive, dangerous, and inefficient when compared to other forms of light, such as LED lanterns. However, the tests have largely been done in a laboratory, where the lantern is burned until all wick is gone. There has not been much attention paid to the output of light in terms of how it provides an energy service. The work, while extremely effective in providing background information on lanterns and their lux output, has not led to recommendations on how to best use the lanterns as they exist currently. While it would be optimal if all households that currently lack access to electricity could use LED/electric lighting, there will be a transition period as lighting options and access is expanded. As such, kerosene and other fuel-based lanterns will continue to be used for the foreseeable future. As such, while it is useful to test their limitations, providing a test to find the recommended best practices for their use is a good mid-point in the process of phasing out fuel- based lanterns entirely. As such, providing a uniform, standardized test that could be used to efficiently test the lantern and could be modified to hone in on specific components of the lantern

(wick type, fuel type, lantern build) would be useful for investigating such best practices and recommendations.

2.10 Goals of the Research

23 The literature review and the gaps in the research leave some interesting areas to explore.

This work attempts to address some of the gaps in the research, with an end goal of creating a standardized test with which to quantitatively compare lanterns. The test would be able to be used to compare aspects of lanterns that could be modified, such as fuel type, wick, or the lantern body itself. Testing would then be done, using the standardized procedure, in order to understand how different fuels impact emissions and lux output, while keeping the type of lantern and the type of wick the same.

Another goal of the research would be to determine which types of calculations are the most useful when examining lanterns. This included finding the most useful ways to compare the emissions - these being carbon dioxide and carbon monoxide - per lux and per amount of fuel used, as well as what could be used as a means of quantifying the efficiency of the lanterns and the stability of the light output. Similarly to the water boiling test, as described in Section 2.0.6, the test and calculations would be able to help the tester understand the efficiency of the lantern and the stability of the lux.

24 CHAPTER 3: METHODOLOGY

As the ultimate product of this thesis has resulted in a procedure for testing lanterns, the methodology is the most complicated and most vital part of the research. The development of such a methodology required much trial and error, as there was no existing test for lighting tests, and there was little research available to understand the tradeoff between light output and factors such as efficiency of the light source, the stability of the light, or the emission factors. As such, it took a great deal of time and testing to uncover a procedure that could be used as an objective way of comparing the outputs of lanterns in a means similar to how they are utilized.

3.1 Materials

The following list of materials were utilized during the experiments, although they were refined or added to as time went along. As stated previously, the test procedure became the focus of the thesis, as there existed no standardized test for comparing lanterns.

• Fuel-based lantern (Dietz 76 Original Oil Burning Lantern) (Figure 7)

Figure 7. Dietz 76 Original Oil Burning Lantern, which was used during the test.

• Hannigan Lab low-cost Y-pod air quality monitor, with CO2 infrared sensor, SD card reader, temperature and humidity sensor, and CO sensor.

• PCE-174 lux monitor (Figure 8)

Figure 8. The PCE-174 lux meter from PCE Instruments.

• Laptop equipped with PCE-174 software

• Kerosene, ethanol, lamp oil/paraffin

• Wick (cotton, flat)

• Kitchen scale

• Blackout cloth

• Cardboard with markings for equipment placement (Figure 9)

Figure 9. Example of the set up for cardboard markings.

• Ruler to measure wick before and after tests.

3.2 Development of Methodology

Development of the methodology was a complicated process with several stages, which will be further described in the following sections. It begins with the initial stages, when very little was understood about how light from a fuel-based lantern worked, and proceeds through the fine-tuning and development of a more standardized test.

3.2.1 Initial Tests

Initial tests were extremely small, on the scale of fifteen minutes. At first, it was not understood how much fuel the tests would require, nor how much wick. It was also not understood how light output could change over the course of the testing duration. In addition, the lux meter was not suitably stabilized, nor accurately standardized from test to test. The lux meter was affixed in approximately the same location on the top of a box but was not provided stability in order to ensure that it would not be loosened or moved, should its mount be jostled. As lux measurements can differ dramatically depending on how far the lux meter is from the light source, ensuring that it is in a stable position from test to test is vital for replicability.

As such, in the initial tests, the lantern was lit for twenty minutes, and allowed to burn frequently. Wick was adjusted as needed, with no eye for standardization of how much additional wick was raised, and the lantern was weighed before the test and after to understand fuel consumption. Initially, the lantern wick was also not allowed more than fifteen minutes to soak in the fuel before the test was conducted, as it was not understand that an hour was required for the wick to fully absorb the fuel so the flame did not merely burn the wick. The lantern was

28 placed next to a standardized eye test, to provide a standardize visual representation of how much useful light the lantern was emitting. The chosen tasks that the light was meant to model was reading - that is, the test was mimicking a child or an adult using the lantern to read a piece of paper by night, and thus needed to be able to produce enough light to see the page fully.

Initially, the same piece of paper was used for every test, and not switched out even if the paper was smudged with soot. As such, the initial tests were full of gaps. However, the tests still revealed important data and insights. As can be seen from Figure 10, there is a clear difference in lux output between kerosene as a lantern fuel as compared to ethanol.

Figure 10. Lux comparison between ethanol and kerosene from initial tests.

Ethanol produces little or no useful light, averaging 0.21 lux over an eighteen-minute period, compared to the 42.6 lux being produced by the kerosene lantern over the same time period. U.S. guidelines recommend 160 lux for school environments, and while neither lantern fuel achieves that, it was evident that ethanol was particularly unsuited to the task of providing useful light. Figure 11 visually demonstrates the difference of light in between the two fuel options.

Figure 11. Visual lux comparison between ethanol and kerosene from initial tests.

Kerosene, in the left image, provides markedly more useful light than the ethanol test. The provided eyechart can be mostly seen, while it is much more difficult to see the eyechart when ethanol is used. The initial tests did thus reveal that there was a marked difference in lux output among fuel types. However, it also revealed that light varied dramatically over the course of a test, even if the environment is stable (i.e. indoors, no ventilation on to interfere with the flame).

3.2.2 Half-Hour Tests

From the initial tests, it was determined that fifteen minutes was an inadequate amount of time to understand the full light output of a lantern. The next round of testing involved increasing the time to half an hour. Wicks were adjusted on a more standardized basis (i.e. every five minutes), although not to the same height. As can be seen from Figure 12, there is more of a definitive pattern to the lux output than during the initial stages.

Figure 12. Lux from a half hour test of kerosene testing.

The lux appears to settle within a range of lux values, which it maintains for a long period of time (i.e. five or more minutes), which can be referred to as the equilibrium lux output. It is evident that over time, as the wick is raised, the equilibrium lux output slowly decreased.

Additionally, test 1 has an initially large lux output, which slowly decreases to zero. However, while test 3 has a smaller lux output initially, the equilibrium lux output is unvaried by the end of the test. At this point during the testing procedure development, the equipment was still not yet placed in a similar position. This can be seen in Figure 13, which shows how the setup was more approximate and harder to standardize.

Figure 13. Initial testing setup during the half-hour phase.

This presented difficulties in comparing tests to one another. The wick was being raised a set amount at set times, however. Similarly to the initial testing, although there were large gaps in the methodology, useful data was still uncovered during the process. As can be seen in Figure

14, ethanol compromises the stability of the lux output, even when added in relatively small amounts.

Figure 14. Lux from a half hour of 10% ethanol and 90% kerosene mixture.

The light output is characterized by sharp spikes in light output, as well as a sharp decline towards a rapidly declining lux equilibrium.

3.2.3 Electric Lanterns

Eventually, the question came up whether the lux meter was calibrated properly, due to the variation in lux output that was evident, even when testing conditions (height of the lux meter, distance of the lantern to the paper, etc.) were controlled. In order to understand this, an LED lantern was tested as well. The lantern was turned on and set in front of the eyechart, similarly to tests with a fuel-based lantern. Every fifteen minutes, the lantern was moved and then it was attempted to put the lantern to as close of its original position as could be possibly accomplished.

This would allow for some understanding on how position influenced the lux output, as well as the smoothness and stability of the lux. It was assumed that the LED lantern would have a relatively stable lux output and would not see the declines that the fuel-based lanterns did over time. As can be seen in Figure 15, the LED lantern still experiences a decline in lux output over time.

Figure 15. Lux from an LED electric lantern.

The lux output is also not smooth, but instead flickers, making the lux output more jagged than expected. The lux also differs when the lantern is set back after its move every fifteen minutes - sometimes raising, sometimes lowering - indicating that without a specifically marked out location, it is difficult to ensure that the lantern is in the same spot just from observation.

This was vital to testing, as it confirmed the need to mark out the location for all equipment in order to ensure continuity between tests.

3.3 Final Methodology

Eventually, it was determined that in addition to standardizing the time, location and placement of all items must also be kept the same from test to test. Lux is defined as lumens over meters squared, and as such, distance can impact the amount of lux measured immensely. A piece of cardboard was used to ensure all equipment was placed in the same location for every test, with markings denoting where each piece - lantern, lux meter stand, eyesheet test, etc. - were to be placed.

34 As stated in Section 2.0.7, the energy service that the light was providing that was to be tested in the course of this test was reading. The test setup centers on the eyechart, which is to stimulate an individual using the light provided by the lantern to read late at night - similarly to children using a kerosene lantern to do schoolwork. As such, the user needs a stable light (few sudden spikes or decreases in overall lux output), with preferably high lux output. The efficiency of the light - the average lux provided per gram of fuel - is also important, as individuals might be using the lantern for extended periods of time.

Testing takes place in discreet steps to simulate how a user might be utilizing the lantern. The initial start is conducted with a previously unused wick, whereas a later phase utilizes the wick after it has already been burned. End users will not change out the wick for every instance of using the lantern unless a very small amount of wick remains, although they will likely raise somewhat to access clean wick.

The full procedure is as follows.

3.3.1 Set Up

Empty out fuel-based lantern completely. Measure a new length of wick - approximately nine to twelve inches. Record the length and set into lantern. Weigh lantern and record weight.

Fill the lantern base with the fuel mixture to be tested and measure weight again. Set aside and allow the fuel one hour to absorb into the wick length. Simultaneously, set up the emissions pod to allow for calibration. Locate the tip of the air quality sensor over the location were the lantern will be placed during the test, with approximately a foot distance between the top of the wick and the inlet of the air quality monitor.

Should the lantern be one that has been used for previous tests, ensure that all holes are clean before starting. The chimney of the lantern and the glass that surrounds the flame can be

35 scrubbed out with a toothbrush and a small amount of water mixed with baking soda. Soot is not water soluble, so baking soda or soap will be required to ensure that the lantern is fully cleaned.

In order to test the amount of soot remaining in the small holes, take a flashlight and shine it through a specified hole into the lux meter, recording the data. This can be used to compare the amount of soot before each test - too much of a buildup can lead to the smothering of the flame, hindering the light output and influencing the emissions that are emitted. An example of the testing setup can be seen in Figure 16.

Figure 16. Example of the testing set up just before conducting a test.

It is preferred to conduct the test in an indoor space, preferably one with a vent but few windows. Windows will need to be covered with something so that the lux meter reads no ambient light, as this will interfere with the testing results. Blackout cloth can be used to cover all windows to ensure that the testing space is suitably dark.

3.3.2 Clean Start

Ensure that the lux meter is recording data and that the room is dark (lux meter should read

36 zero ambient lux). Raise the lantern wick a specified amount - all testing referenced henceforth raised it approximately one centimeter. Light the lantern and place it in the specified place in front of the sheet of paper. Demonstration of the equipment positioning during the test. Figure 17 demonstrates the relative position of the lantern to the paper and the lux meter during the clean start testing phase.

Figure 17. Demonstration of the equipment positioning during the test.

Allow the lantern to burn for fifteen minutes, without touching the wick, even if the lantern burns out. At fifteen minutes, blow out the lantern and weigh. Allow the lantern to rest in darkness for a further fifteen minutes, keeping the lux meter and the air quality probe running.

3.3.3 Dirty Start

37 After a fifteen-minute cool down period, raise the wick somewhat (0.5 centimeter), but ensure that the top of the wick is still burned from the initial round of testing. Light the wick again and move into the specified position at the top of the eye chart again, to as close the same position as during the clean start as possible. Allow the lantern to burn for a further fifteen minutes, and do not touch the wick during that time, even if the lantern burns out. At the end of that period, weigh the lantern before quickly moving it back into position to resume the last phase of the test.

3.3.4 Simmer Phase

The final section is the "simmer" phase, in which the tester is attempting to keep the lux output as steady as possible - ± 2.5 lux around a given value. Use the average of the lux output during the last five minutes of the dirty start phase. If the lux output of the last five minutes is 25 lux, the user will try to not allow the lantern to fall below 23.5 lux. A downward drop is more of a problem than a sudden spike - more light often means more ability to see, whereas if the lux is dropping lower than the average value, it could compromise the user’s ability to complete whatever task they are attempting to use the light for. This phase of the test takes a further fifteen minutes. Upon completion of the testing time, blow out the lantern and weigh, recording the final weight. Run the emissions probe for another fifteen minutes in order to understand how quickly the emissions begin to settle following the test. Remove the wick from the lantern and measure to see how much of the wick was consumed during the testing process - during tests with ethanol, for instance, nearly the entire wick was consumed, while in tests with kerosene, often only three or four inches would be used at most.

3.3.4 Analysis and Calculations

With all phases of the test complete, the test results can be analyzed. The carbon dioxide

38 and carbon monoxide measurements needed to be converted from voltage signals to parts per million. This was done using the following formulas:

퐶푂2푝푝푚 = (푠푖푔푛푎푙 ∗ 7.032) − 726

퐶푂푝푝푚 = (푠푖푔푛푎푙 ∗ 9.849푒 − 5) − 0.04

Efficiency of the light was determined to be another useful factor to consider, as the amount of lux output could vary from fuel type. This is effective in figuring out the usefulness of the light - that is, the amount of lux provided per gram of fuel consumed. Some fuel types might produce a great deal of lux, for example, but consume more fuel in order to do so, rendering the efficiency relatively low. Conversely, a fuel might not produce much lux, but also not be as consumed as rapidly. In order to calculate the efficiency of the light, the user must have the amount of fuel consumed during the fifteen-minute phase of the test, as well as the average of the lux emitted during that time.

Efficiency = average lux during phase/grams of fuel used during phase

The emission factors are also an important calculation to conduct in order to understand the grams of a pollutant emitted per kilogram of fuel burned. Emission factors are important to consider, as they are a representative value that relates the quantity of the pollutant released

(such as carbon dioxide or carbon monoxide) with an activity associated with the release of the pollutant - in this case, the burning of the lantern fuel. The calculation is as follows:

First, begin with calculating the modified combustion efficiency. To do this, one must average the carbon dioxide and carbon monoxide emission plumes over each phase of the test - the clean start, the dirty start, and the "simmer", and then subtract out the background emission plumes. During the clean start, for example, the tester would subtract an average of the CO2 and

39 CO emissions plumes from the ten minutes before the test began. That way, the emission factor calculation is only examining the emissions produced as a result of the fuel itself, and not existing pollutants.

Background subtracted CO2 = CO2 during phase - CO2 during last ten minutes of previous phase

Background subtracted CO = CO during phase - CO during last ten minutes of previous phase

With this, the modified combustion efficiency can be calculated. It will need to be done for both carbon dioxide and carbon monoxide.

Modified combustion efficiency for CO2 emission factor

퐵푎푐푘푔푟표푢푛푑 푠푢푏푡푟푎푐푡푒푑 퐶푂2 휂퐶푂2 = 퐵푎푐푘푔푟표푢푛푑 푠푢푏푡푟푎푐푡푒푑 퐶푂2+퐵푎푐푘푔푟표푢푛푑 푠푢푏푡푟푎푐푡푒푑 퐶푂

Modified combustion efficiency for CO emission factor

퐵푎푐푘푔푟표푢푛푑 푠푢푏푡푟푎푐푡푒푑 퐶푂 휂퐶푂 = 퐵푎푐푘푔푟표푢푛푑 푠푢푏푡푟푎푐푡푒푑 퐶푂2+퐵푎푐푘푔푟표푢푛푑 푠푢푏푡푟푎푐푡푒푑 퐶푂

This can then be used to calculate the carbon dioxide and carbon monoxide emission factors. The modified combustion efficiency will change during each phase of the test and will need to be calculated for all phases. The emission factor calculation requires the amount of a fuel that made up of carbon, expressed as a decimal. In the case of kerosene, this was estimated to be

82%.

푔 44 푔퐶푂 퐸퐹 = 휂퐶푂 ∗ 100 ∗ % 푐푎푟푏표푛 ∗ = 2 퐶푂2 2 푘푔 12 푘푔 푓푢푒푙

40 푔 28 푔퐶푂 퐸퐹 = 휂퐶푂 ∗ 100 ∗ % 푐푎푟푏표푛 ∗ = 퐶푂 푘푔 12 푘푔 푓푢푒푙

In addition, it was also determined that a useful measurement to examine in the context of the test would be emission factors divided by the average lux produced. This could be then compared across tests, to determine if a certain fuel would result in more exposure for a given amount of lux. The calculation is as follows:

CO2 EF-lux = CO2 EF during test phase/average lux during test phase CO EF - lux = CO EF during test phase/average lux during test phase

Such calculations can be used to better understand the overall impact of the different factors and how it influences the emissions and the lux in a more granular way than just the emission concentrations in parts per million, or the overall lux. In order to fully understand what factors influence lantern performance and in what ways, such calculations can provide vital insight.

Later sections will describe that the emission factors were determined to not be the most useful insight with understanding different fuel types, but the calculations should still be performed when comparing different lantern factors in order to have a full picture of what influences lanterns.

41 CHAPTER 4: APPLICATIONS OF THE TESTING PROTOCOL

The testing protocol went through several stages of development before arriving at the final design. As explained in later sections, the initial protocols were more exploratory of the lantern and light output, and much less rigorous. As testing proceeded, the protocol developed into a more regulated and linear model with set periods to mimic certain real-world applications and uses of lanterns. As there were no initial tests for testing light output as compared to emissions, the development of the procedure was trial-and-error, adjusted to try and understand how users might be utilizing their lanterns.

4.1 Initial Tests Before Standardized Test

Initially, testing included 100% ethanol tests, as well as 10% and 15% ethanol tests, as

described in Section 3. These were compared with tests of 100% kerosene. Testing took place over

twenty minutes, and wicks were measured before and after every test. Wick length was adjusted

as needed, not on the basis of time or keeping to a certain level of lux. It became clear that

ethanol was unsuitable as a lighting fuel. As demonstrated by Figure 18, the lux output for 100%

ethanol was extremely low, and often negligible to the point of not registering on the lux meter.

Figure 18. Lux from 100% being used as a lantern fuel.

Moreso, as demonstrated by Figure 19, the carbon dioxide emissions plume was also high,

demonstrating that there was no net benefit to using ethanol as a lighting fuel.

Figure 19. CO2 from 100% ethanol being used as lantern fuel.

Even if the emissions were lower, the lack of visible lux from the lantern would mean that this fuel source is not applicable for lighting purposes. The next fuel mixture tested was one that was 10% ethanol and 90% kerosene by volume. As can be seen in Figure 20, the lux output of

43 the test was wildly unstable and characterized by sudden spikes as the wick was adjusted upwards, before quickly deteriorating.

Figure 20. Lux from a fuel mixture that was 90% kerosene and 10% ethanol.

It can be noted that over time, the equilibrium value that the lux returns to after the initial spike from the wick being adjusted upwards slowly diminishes as well, leveling off towards zero.

Upon viewing this result, it was determined that ethanol was too volatile a fuel to attempt a higher percentage in an ethanol-kerosene mixture.

4.2 Standardized Test - Kerosene

Kerosene, as stated in Section 2, is a commonly utilized fuel for lighting purposes in the developing world and is used by millions of people. As such, it is a useful fuel to begin with for testing out the developed procedure. Figure 21 demonstrates the lux from all the kerosene tests done utilizing the new procedure.

Figure 21. Lux from all kerosene tests.

The ten tests span a twenty-lux range, averaging around 25 lux. As can be seen, the lux output is relatively stable during the clean start period, when the lantern is equipped with a new, unused wick. Following the fifteen minute "off" period, the dirty start period is characterized by somewhat more instability, with a similar curve as can be seen in the first part of the test. In the

"simmer" portion of the test, where users are trying to keep the light as stable as possible (±2.5 lux), there is consistently more variability, with lux spiking to up to ten lux higher than the equilibrium value. The average amount of lux output per gram of fuel varies widely during the test, as can be seen in Figure 22.

Figure 22. Efficiency of the kerosene lantern tests (lux/gram fuel).

45 This is also known as the efficiency of the fuel, as noted in Section 3.3.5. In each test, it can be noted that during a test, the values tend to cluster around each other in a given test - that is, if one test has a low value of lux/gram kerosene during the clean start, it will also likely not be much higher or lower during the other two stages of the test. Of course, there is variability, with some tests having more of a range than other tests. Most of the tests tended to consume, on average, five grams of fuel per fifteen-minute period of testing. Another useful way to examine the lux output of the lanterns, as well as the stability of the light, is to examine the standard deviation of the lux. Using data in thirty second intervals, Figure 23 shows the standard deviation of the lux output over time.

Figure 23. Standard deviation of lux during the kerosene test (30 second intervals).

For this to be calculated, the standard deviation of the lux across thirty seconds was taken as a rolling interval throughout the entire duration of the test. As instability increases, the standard deviation increases well, as there is increased variation in lux values during each thirty second duration. This served as a useful means of simplifying the stability calculation. As can be seen in the figure, there are large spikes during the transition periods between phases, when the lantern is either being blown out or lit again, but during the clean start and dirty start phases, the light output is relatively stable, with no spikes or real variation. However, during the "simmer" phase,

46 there is much more variability, indicating that the light output is much less stable during this phase, and trying to maintain a light level increases the variation that can be seen in the light output. Figure 24 shows the carbon dioxide emissions plume during the test in parts per million

(ppm).

Figure 24. CO2 in the emissions plume during the kerosene tests in ppm.

There is a very clear trend that demonstrates the difference phases of the test. The initial fifteen minutes are the clean start, and there is a clear and visible rise in the CO2 emissions plume during that time. Following that, there is the fifteen-minute "cool-down" phase, and it can be noted that the emissions plume declines dramatically. This is followed by the dirty start phase, and the emissions plume immediately begins to rise again. The plume rises further during the final, "simmer" phase, when the wick is being adjusted often. Finally, during the final fifteen minutes, when the lantern has been blown out, there is a clear slope as the emissions plume declines. Such a replicable pattern during the testing phases indicates that there is a standard form for the emissions plume to take. Such a pattern is not as obvious with the carbon monoxide emissions plume as it is with the carbon dioxide emissions plume, however. As can be seen in

Figure 25, there is not as clear a pattern as can be seen in Figure 24.

Figure 25. CO in the emissions plume during the kerosene tests in ppm.

The carbon monoxide emission plume declines somewhat during the off period and rises during the dirty start and the "simmer" phase, but it is not as visible as the trend with carbon dioxide. Carbon monoxide emission plume levels do appear to be relatively steady during the test, however. Emission factors are another way to demonstrate the emissions in terms of grams of pollutant per kilogram of fuel used from the tests and compare them. Emission factors are a representative value that attempts to relate the quantity of a pollutant released to the atmosphere with an activity associated with the release of that pollutant - that is, the amount of lantern burning would release a given quantity of the pollutant. Figure 26 demonstrates the range of the emission factors for carbon dioxide during the three phases of the test.

Figure 26. CO2 emission factors from the kerosene tests (g pollutant/kg fuel).

As can be seen from the figure, in the clean start phase, there is a wider range of values for the emission factor, but the average emission factor is lower than that of the average emission factor during the dirty start phase or during the "simmer" phase. The also trends upwards, and the range of values shrinks for the last two phases. Figure 27 displays the emission factors for carbon monoxide during the three phases of the test.

Figure 27. CO emission factors from the kerosene tests (g pollutant/kg fuel).

As with Figure 26, there is a broader range of values during the clean phase. The range then

49 decreases across the next two stages of the test, while the average value of carbon monoxide emission factor decreases, with the values during the "simmer" phase being in a relatively tight range and with a lower average compared to during the clean start phase. A more useful way of examining the emissions of the lantern might be in the emission plume concentrations per unit of light, as displayed in Figure 28.

Figure 28. Average CO2 concentration in ppm from kerosene, divided by average lux.

This was done by using the background subtracted carbon dioxide emissions plume during each phase of the test and dividing it by the average lux emitted during each phase. As can be seen in the figure, the highest amount of carbon dioxide emissions plume per unit of lux comes during the "simmer" phase, while the clean start and dirty start phase appears to have about the same average amount of carbon dioxide per lux, although the clean start has a wider range.

Examining the average concentration of carbon monoxide in the emissions plume per lux reveals that the lowest amount of carbon monoxide in the emissions plume per lux appears to be in the dirty start phase, with the clean start and "simmer" phase being roughly equivalent, as seen in

Figure 29.

Figure 29. Average CO concentration in ppm from kerosene, divided by average lux.

From the tests and data collected, it was determined that it was more useful to examine the emission concentrations in the emissions plume divided by the lux produced, rather than examining the emission factors, as the different fuels produced both different emission concentrations and lux.

This data indicates that, at least for kerosene, the test reveals some replicable patterns in terms of lux and emissions - both when analyzed in terms of the overall trends and in terms of the emission factors. As such, it provides a useful baseline in which to compare other types of lighting fuels.

4.3 Standardized Test - 0.5% Ethanol

As ethanol and kerosene mixtures appeared to be volatile over 5%, it was determined that it would be insightful to test a mixture composed of majority kerosene and a small portion of ethanol by weight. As such, kerosene was weighed out, and then 0.5% ethanol was added into the mixture. The test was then conducted according to the procedure.

As can be seen in Figure 30, the lux produced was more stable than the lux from higher

51 mixtures, such as those that can be seen in Figure 20 or Figure 18.

Figure 30. Lux from 0.5% ethanol.

However, there is significant instability in light output visible during the "simmer" phase - it is within the range noted in the kerosene tests (see Figure 21). As such, there is not a significant difference in the lux output between pure kerosene and a mixture with 0.5% ethanol.

Figure 31 demonstrates the average output of lux per gram of fuel used in the tests, or the efficiency of the lantern as a lux source.

Figure 31. Efficiency of the 0.5% ethanol lantern tests (lux/gram fuel).

52 Similarly to with kerosene (Figure 22), individual tests tend to not vary too much from one another - values for test one are relatively similar and not shockingly higher or lower - but this can vary between different tests. The dirty start phase appears to have slightly higher average lux per gram fuel, but not significantly so from the other phases of the test.

Similarly to kerosene, as seen in Figure 23, Figure 32 shows the standard deviation of the lux during the course of testing, which is a useful shorthand for the stability of the light.

Figure 32. Standard deviation of lux during the 0.5% ethanol test (30 second interval).

As with kerosene, the lux is relatively stable during the clean and dirty start phases, with much more variability during the "simmer" phase, wherein the wick is being continuously adjusted in an attempt to keep the light output stable. Similarly to kerosene, this demonstrates that 0.5% ethanol is a relatively stable light source. Similarly, the output of carbon dioxide emissions in the emissions plume from 0.5% ethanol follows a similar pattern to that of pure kerosene, as can be seen in Figure 33.

Figure 33. CO2 emissions in ppm in the emissions plume from all 0.5% ethanol tests.

As in Figure 24, there is a clear increase in carbon dioxide emissions in the emissions plume during the clean start phase and dirty start phase, with a gradual increase during the "simmer" phase. The carbon dioxide emissions in the emissions plume from pure kerosene have a higher peak - the kerosene tests had a maximum value of 1411.7 ppm of CO2 in the emissions plume, while the 0.5% ethanol tests had a maximum value of 1060.1 ppm in the emissions plume - a difference of 351.5 ppm. However, only three tests were done for the 0.5% ethanol, compared to ten for kerosene, so a greater range of tests might return a similar maximum carbon dioxide concentration. Figure 34 show the carbon monoxide emissions in the emissions plume from the

0.5% ethanol tests.

Figure 34. CO emissions in ppm in the emissions plume from all 0.5% ethanol tests.

Similarly to Figure 27, the pattern for carbon monoxide emissions in the emissions plume during the test is not as apparent as it is for kerosene. There is a slight decline in the concentrations during the off period, followed by increases when the lantern is lit again, but the pattern is not as clear. Kerosene also emitted more carbon dioxide in the emissions plume during the testing, with a maximum of 7.7 ppm, compared to the 0.5% ethanol mixture’s maximum level of 3.5 ppm.

As can be seen in Figure 35, the emission factors for carbon dioxide are more varied during the clean start and dirty start phases than those of kerosene.

Figure 35. CO2 emission factors from the 0.5% ethanol tests (g pollutant/kg fuel).

In Figure 28, the range of emission factors for carbon dioxide is very broad in the initial clean start phase when using kerosene, but the range tightens as the average raises through the

"simmer" phase. With 0.5% ethanol, the range continues to be broad through the dirty start phase, and the range only shrinks during the "simmer" phase.

In Figure 36, the range of emission factors for carbon monoxide is broad during the clean start phase when using kerosene as the test fuel, and the average slowly decreases as the testing phases progress, while the range tightens.

Figure 36. CO emission factors from the 0.5% ethanol tests (g pollutant/kg fuel).

56 As with Figure 35, the range of values for carbon monoxide emission factors remains broad for both the clean start and dirty start phase, and the range only decreases in the final "simmer" phase.

Figure 37 shows the average carbon dioxide concentration in parts per million in the emissions plume divided by average lux during each stage of the test.

Figure 37. Average CO2 concentration in ppm from 0.5% ethanol, divided by average lux.

As can be seen from the figure, the average CO2 concentration in the emissions plume per unit lux is roughly the same during the first two stages of the tests, if a little higher during the dirty start phase. However, during the "simmer" phase, there is much greater output of emissions in the emissions plume per lux emitted, which indicates that continuously adjusting the light upwards leads to greater emissions when compared to the first two phases of the test. The pattern is not as clear when it comes to carbon monoxide emission concentrations in the emissions plume per lux, as seen in Figure 31.

Figure 38. Average CO concentration in ppm from 0.5% ethanol, divided by average lux.

In the dirty start phase, for example, two of the tests have relatively low CO concentrations in the emissions plume per lux, whereas one test experienced much higher CO ppm per lux.

More testing would likely need to be done to determine if there is any pattern to be seen in carbon monoxide emission concentrations per lux when using 0.5% ethanol.

These tests indicate that, if ethanol is present in low amounts, it could potentially mitigate some level of carbon dioxide and carbon monoxide emissions during use, while not compromising the lux output too severely. However, this is initial results, and further data analysis - such as the performance of a two-factor ANOVA test, which can assess the influence of different factors on the outcome - will be necessary for statements with any amount of certainty.

4.4 Standardized Test - Lamp Oil

Paraffin lamp oil is another common choice as a fuel for fuel-based lanterns. It is within the same family as kerosene but has been further refined so it burns more cleanly than kerosene does. As a result of the additional refining, paraffin releases fewer impurities into the air, and will be virtually smokeless with minimal odor. However, paraffin by itself is not very liquid, and

58 often has solvents added to make it more liquid. As can be seen from Figure 4.22, the lamp oil lux is more varied in the clean start stage than what was seen in Figure 4.4.

Figure 39. Lux from paraffin lamp oil.

The lamp oil is prone to more spikes and instability, particularly during the "simmer" phase during the last fifteen minutes of the testing period.

Compared to kerosene and 0.5% ethanol, lamp oil has a consistently low average lux per gram of fuel and thus a relatively low lux-fuel efficiency, as seen in Figure 40.

Figure 40. Efficiency of the paraffin lamp oil lantern tests (lux/gram fuel).

The average output of lux per gram of fuel is below 4 lux per gram of fuel and lacks some of the variation seen in Figure 22 and Figure 31. This indicates that while lamp oil is a relatively stable choice and lacks the instability that is more common in pure kerosene or an ethanol mixture, the output will generally be comparatively low in terms of lux.

In contrast to kerosene and 0.5% lamp oil, analyzing the lamp oil on the basis of the standard deviation of the lux, as seen in Figure 41, shows markedly more instability in the light output on thirty-second intervals.

60 Figure 41. Standard deviation of lux during the lamp oil test (30 second interval).

The light flickers more during every period of the test than was seen with 0.5% ethanol (Figure 32), with the instability becoming even more notable during the "simmer" phase, with the lux experiencing sharp spikes. This indicates that lamp oil might present less stable light than kerosene or 0.5% ethanol, as well as less light overall, as seen in Figure 40. As can be seen in

Figure 42, the carbon dioxide emissions in the emissions plume follow the same pattern that was visible with kerosene and 0.5% ethanol (Figure 21 and Figure 33).

Figure 42. CO2 in the emissions plume from paraffin lamp oil.

Lamp oil does appear to burn cleaner than full kerosene, with a maximum CO2 concentration of 1067.2 ppm in the emissions plume. However, similarly to 0.5% ethanol, only three tests were conducted with the lamp oil, compared to ten for kerosene - were more tests to have been conducted, it is conceivable that similar emission levels in the emissions plume would be seen. As with kerosene and 0.5% ethanol, the pattern for carbon monoxide in the emissions plume is not as apparent as that of carbon dioxide as seen in Figure 43.

Figure 43. CO in the emissions plume from paraffin lamp oil.

Test 3 is also notable, as there was a spike in the beginning of the test, followed by a steady decline in concentration of CO in the emissions plume for the remainder of the test. The other two tests display some level of activity that is correlated with the phases of the test itself. Figure

44 shows the carbon dioxide emission factors for the paraffin lamp oil.

Figure 44. CO2 emission factors from paraffin lamp oil (g pollutant/kg fuel).

62 Figure 45 shows the carbon monoxide emission factors for paraffin lamp oil.

Figure 45. CO emission factors from paraffin lamp oil (g pollutant/kg fuel).

Similarly to the pattern displayed by kerosene in Figure 4.10, the range is broadest and highest during the clean start phase, before decreasing in the later stages.

Figure 46 shows the average carbon dioxide concentration in the emissions plume divided by average lux during each phase of the test.

Figure 46. Average CO2 concentration in ppm from lamp oil, divided by average lux.

The average CO2 in the emissions plume per lux is higher than that of 0.5% ethanol, with a

63 maximum difference 0f 17.92 CO2ppm in the emissions plume per lux during the "simmer" stage. The values are more in line with those experienced by kerosene but are still on the high end. More tests might have shown a similar range to kerosene, however, but this result indicates that lamp oil might produce more carbon dioxide emissions per unit light than other fuel options.

Similarly to kerosene and 0.5% ethanol, the average carbon monoxide emissions in the emissions plume per lux do not have a clear pattern, as seen in Figure 47.

Figure 47. Average CO concentration in ppm from lamp oil, divided by average lux.

While lamp oil seems to have relatively high carbon dioxide emissions in the emissions plume per lux, the amount of carbon monoxide concentrations in the emissions plume per lux is much smaller, which indicates that while lamp oil might not produce a great amount of stable lux and has higher CO2 emissions in the emissions plume, it might be a good choice if carbon monoxide is a concern.

Overall, the lamp oil tests demonstrate that while lamp oil is meant to be a cleaner variation of kerosene, it might perform worse in terms of light output, stability, and carbon dioxide emissions per lux. More testing would be required for definitive statements, however, as well as

64 further data analysis to understand the influence of different factors on the outcomes.

4.5 Standardized Test - 1% Ethanol

Previous testing, such as those described in Section 3.2.1, revealed that ethanol mixtures over 10% are extremely volatile and do not produce stable light output. In Section 4.3, a fuel mixture that contained 0.5% ethanol was tested, and found some level of stability during the clean start and dirty start phases, although the "simmer" phase was volatile and prone to spikes in light output.

A single test was done on a 1% ethanol mixture to see if a small change would provide noticeably different results in terms of light output or emissions. Similarly to the 0.5% ethanol mix, the 1% ethanol mix was 1% of the total weight of the fuel mixture. As can be seen in Figure

48, the lux output of the lantern differs dramatically than that displayed in Figure 30, which showed the lux output from the 0.5% ethanol mixture.

Figure 48. Lux from a 1% ethanol mix.

The decline in lux output, which is common across all fuel types, begins much sooner in each phase and is very steep. In the clean start phase, as the lantern wick cannot be raised during

65 the duration of the test, the lantern drops below an output of 5 lux and trends towards zero quickly. The same step decline in light output is noticeable during the dirty start phase. During the "simmer", the light output spikes dramatically up with every raise of the wick, only to quickly drop down. The lantern burns out at 56 minutes, four minutes before the end of the

"simmer" phase of the test, as the entire wick was consumed during the duration of the test. As only one test was conducted for the 1% ethanol fuel mixture, it is hard to draw many conclusions on the stability of the lux. However, Figure 49 demonstrates that the lux is very unstable during the "simmer" phase, and experiences huge jumps when the lantern is lit, indicating the inherent volatility of the fuel mixture.

Figure 49. Standard deviation of lux during the 1% ethanol test (30 second intervals).

The pattern of carbon dioxide emissions in the emissions plume follows the similar pattern to tests with other fuels, with a sharp rise during the clean start phase, followed by a drop during the off period, and then a jump at the start of the dirty start phase as can be seen in Figure 50.

Figure 50. CO2 emissions from 1% ethanol mix.

The climb of emissions during the "simmer" phase is notable, however, and is likely caused by the continual need to raise the wick in order to maintain illumination levels.

As can be seen in Figure 51, carbon monoxide concentrations in the emissions plume follow the pattern of the test phases to some degree.

Figure 51. CO emissions from 1% ethanol mix.

Similarly to Figure 4.17, there is an increase in CO concentrations in the emissions plume during the clean start, and a drop off during the off period, followed by a rise during the dirty

67 start phase. However, the spike of carbon monoxide output in the emissions plume during the

"simmer" phase stands in sharp contrast and is potentially due to the need to continually raise the wick during the 1% ethanol test in order to maintain illumination.

As there was only one test conducted, it was determined that the most useful way to summarize the data would be in a table. The summary of the 1% ethanol tests is available in

Table 1. CO2 Emissions/ CO Emissions/ Testing Phase Lux Per Gram Lux Lux Clean Start 13.7 0.048 3.03 Dirty Start 10.4 0.009 4.01 Simmer 19.6 0.038 3.91 Table 5. Summary of the 1% ethanol tests.

As displayed in the table, 1% ethanol releases a great amount of CO2 emissions, in terms of ppm, per each unit of light. However, the amount of carbon monoxide per unit of light is relatively low. The lux per gram, which is used as the efficiency for this test, is also rather low, and is more in line with lamp oil’s output than that of 0.5% ethanol. Overall, such tests indicate that adding ethanol in greater amounts to kerosene than 0.5% might be detrimental to the overall performance of the lantern - while it does appear to lower overall exposure to emissions, the light stability and output are too low to be truly useful for tasks such as reading, which would require more stable and brighter light.

68 CHAPTER 5: FINDINGS DISCUSSION

The findings and analysis presented in Section 4.5 show several interesting points to discuss, both in terms of the performance of the test design, as well as in the differences between the fuel types that were tested.

5.1 Test Design and Performance

Through trial-and-error, a test design that could be used to quantitatively compare and contrast the emissions and light output of a lantern and its fuel was designed. The test was only used to judge fuel types, tested within the same lantern, with the same type of wick, but the test could be easily used to test a multitude of factors, such as wick type, lantern design, or how levels of cleaning influence lantern performance. It is unclear if the lantern would be suitable for testing gas lanterns, such as those that use propane, but it reveals some interesting aspects of light stability over time.

As was shown in the discussion on electric lantern testing in Section 3.2.3, there appears to be an inherent instability to light over time. Although the light output from the LED electric lantern appeared to be more stable than the light coming from the fuel based lantern and did not seem to have the same problems with flickering that the fuel based lantern did, the light was still not as stable as it appeared, and also decreased moderately over time. Testing revealed that in lighting, there are natural variations in the light output over time.

It is unclear how the test could be modified to understand lighting with non-liquid fuels, such as with propane, but kerosene and other liquid fuel lanterns are still commonly utilized in many parts of the world. The test does demonstrate a quantitative means of comparing different

69 lanterns and their fuel types, which could then be used to create a list of best practices for different types of lanterns. For example, when examining Figure 4.9, which shows the CO2 emission factors during the kerosene lantern tests, it can be seen that the clean start has a wider range of CO2 emissions, but the overall average is lower than that for the dirty start. This could indicate that, in order to lower overall exposure to CO2, the lantern users should trim the wick somewhat to expose new, unburned wick before lighting it again.

5.2 Two-Way ANOVA

This test also appeared to reveal some interesting differences in the different types of liquid fuels used in the lantern during the testing period. Such tests offer insight into what type of fuel is useful for what purposes, as well as the trade-offs between different types of fuel - which is vital when it comes to understanding lanterns and their best practices.

In order to understand which factors were more influential for a given metric - such as efficiency or light stability - a two factor ANOVA test was performed, as it was of interest to compare a continuous outcome across two different factors. ANOVA is useful for determining if one or more factors has any influence on the outcome, as well as which of multiple factors is the most influential. In addition, two-way ANOVA can be utilized to probe interaction between the different variables, and whether that interaction has an effect on the outcome. A null hypothesis would be that the variables would not be influential and that variation in results is due to effects of random chance.

The ANOVA test results will be presented in a standard table format, as seen in Figure 52.

Figure 52. Standard ANOVA results table.

In the standard table, “df” stands for “degrees of freedom”, and it is the maximum number of logically independent variables (values that have the freedom to vary in the data sample). The

Sum of Squares measures the deviation of data points away from the mean value, where a higher sum of squares indicates a larger degree of variability in the dataset. Conversely, a lower result indicates that the data does not vary from the mean greatly. The F-test is used to determine whether the variability between group means is larger than the variability of the observations within the groups. It is a ratio of two mean squares. Finally, the table includes the p-value. This is the area to the right of the F-statistic, and it is the probability of observing a result as big as the one obtained in the experiment, assuming the null hypothesis is true. Lower p-values are indications of strong evidence against the null-hypothesis – in the case of this work, that the lighting fuel or test stage does not have an influence on outcomes.

There are a few purposes with performing a two-way ANOVA. One is that the test can be used to understand if there is an interaction between the two independent variables on the dependent variables. Another reason is that the two-way ANOVA test can be used to more fully understand two different variables and their individual impacts on the outcome. The two factors considered were fuel type - kerosene, 0.5% ethanol, or paraffin lamp oil - and test stage- clean, dirty, and "simmer". Given that only one test was performed, 1% ethanol was omitted from the analysis. The test performed indicated that 1% ethanol was an unstable and not a very bright light source and ended up burning out before the end of multiple testing periods. As such, while further testing could be performed, it will be left out of this discussion.

71 The two variables considered were the fuel type (kerosene, paraffin lamp oil, or 0.5% ethanol) and testing stage (clean start, dirty start, or “simmer”). Examining the interaction term of the two-way ANOVA can inform whether the effect of one of the independent variables on the dependent variables is the same for all values of the other independent variable. For example, it may be that paraffin lamp oil dims while it burns while kerosene retains a steady light output.

The null hypothesis for such a test would be that the various lighting factors that are being tested

– lighting efficiency, stability, and emission factors per gram of fuel – would not be dependent on changes of fuel or testing stage, and any change would be due to random chance.

The two-way ANOVA allowed for understanding which factors - fuel type or test stage - were most important when considering outcomes. Throughout the study, we use a 0.10 significance level or 90% confidence level for deciding if a factor leads to significant variation in a particular experiment. The two-way ANOVA analysis will be discussed in the following subsections, but the full code is available in the appendix.

5.2.1 Efficiency

Efficiency is an important metric of combustion. In the water boiling test, efficiency is based on the amount of fuel consumed, as well as the time required to heat the water to a boil.

For the lantern test, efficiency takes the form of the light output (lux), divided by the amount of fuel required to maintain that light level, during each phase. The two-way ANOVA indicates whether the testing phase or fuel type significantly impacts the efficiency of the lux, along with whether there is an interaction between these two factors. The null hypothesis for this would be that neither fuel type nor testing stage would have an influence on the outcome, with any changes in lighting efficiency being due to chance. Table 2 shows the results in the standard

ANOVA table format.

Sum of Squares Df MSq F-value Pr(>F) Fuel Type 46.88 2 23.438 8.546 0.000836 Test Phase 2.92 2 1.458 0.532 0.5919 Fuel Type:Test Phase 5.46 4 1.366 0.498 0.737 Residuals 106.96 39 2.743 Table 2. ANOVA table from lux efficiency tests.

Figure 53 shows the outcome of the two-way ANOVA test performed for the light efficiency.

Figure 53. Results from two-way ANOVA on light efficiency. (Tests by factor: kerosene (10), lamp oil (3), 0.5% ethanol (3), clean start (16), dirty start (16), simmer (16)).

For fuel-type, there was an F-value of 8.546, with a Pr(> F) equal to 0.000836, which indicates that there is a 0.08% chance that the probability of randomly observing a value greater than the observed value of 8.547 if the null hypothesis were correct. For test phase, the F-value is

0.532 and the Pr(>F) is 0.591, which indicates that there is a 59% probability of observing a greater value than the observed F-value if the null hypothesis is true.

These results indicate that out of the two different factors, different types of fuel lead to significantly different light output when testing the light efficiency. The p-value for the fuel type is 0.000836, and since this is level than our significance level of 0.1, this indicates that the type of fuel is significant when it comes to lighting efficiency. However, the p-value for the test phase is 0.591, which indicates that testing phase does not statistically affect lighting efficiency at our

73 significance. The p-value for the interaction between fuel type and test phase is 0.737, which does not indicate that the relationship between test phase and light efficiency depends on the fuel type.

The determination of significance also depends on the number of tests performed. In total, sixteen tests were performed for this – ten using kerosene as the fuel, and then three each with paraffin lamp oil and 0.5% ethanol. Each of the test phases was repeated sixteen times, which does not support a finding that test phase had a significant effect on lighting efficiency. More tests would be required to determine what effects, if any, could be found.

Figure 54 shows the interaction between the fuel types and the stages of the lux efficiency tests.

Figure 54. Interaction plot between fuel type and test stage of lux efficiency tests.

74 The lines showing the simmer phase and the clean phase are almost parallel across all three test fuels, which indicates a nonsignificant interaction. For a “dirty start”, the interaction lines are almost parallel with the clean start line for kerosene and lamp oil, which also indicates a nonsignificant interaction. However, for the dirty start and simmer phase, there is a crossover for kerosene and lamp oil. The interaction between the fuel type and the test stage does not appear to be statistically significant as well.

Examining the averages of the lux produced per unit of fuel had indicated a difference between the fuel types, with paraffin lamp oil producing low lux per gram of fuel when compared to kerosene or 1% ethanol. However, performing the two-factor ANOVA separates the influence of the testing stage from the process, and demonstrates that fuel type is the more statistically significant variable when it comes to the efficiency of the lantern.

Should maximum light efficiency be the desired end goal of the user, it could thus be more important to consider the type of fuel being used, rather than the "testing" stage - that is, whether the wick is fresh, previously burned, or trying to maintain a certain level of lux for a certain period of time.

5.2.2 Light Stability

The stability of the lux output of the fuel is an important factor to understand. In Section 4, the standard deviation of the lux on a thirty second interval was taken, with the assumption that the standard deviation would be a good indicator of the stability of the light source. The average rolling standard deviation for the test phase then divided by the average for that test section, resulting in the coefficient of variation or the relative stability of the light. Higher numbers would indicate a broader range of lux values and indicate that the light source might not be stable. A null hypothesis for this factor is that neither test phase nor fuel type has an influence on the overall stability of the light. Table 3 shows the results of the two-way ANOVA test for light

75 stability.

Sum of Squares Df MSq F-value Pr(>F) Fuel Type 0.494 2 0.247 4.656 0.0154 Test Phase 3.507 2 1.735 33.049 4.02e-9 Fuel Type:Test Phase 0.786 4 0.1965 4.703 0.0120 Residuals 2.069 39 0.0531 Table 3. ANOVA table from light stability tests.

Figure 55 shows the outcome of the two-way ANOVA test performed for the light efficiency.

Figure 55. Results from two-way ANOVA on light stability. (Tests by factor: kerosene (10), lamp oil (3), 0.5% ethanol (3), clean start (16), dirty start (16), simmer (16)).

For fuel-type, there was an F-value of 4.656, with a Pr(>F) equal to 0.0154, which indicates that there is a 1.5% chance that the probability of randomly observing a value greater than the observed value of 4.656 if the null hypothesis were correct. For test phase, the F-value is 33.049 and the Pr(>F) is 4.02e-9, which indicates that there is a near-zero probability of observing a greater value than the observed F-value if the null hypothesis is true. Given this, the test phase has a statistically significant effect on the lighting stability. The p-value for the test phase is

4.02e-9, which indicates that the type of fuel is significant when it comes to lighting stability.

The p-value for the interaction between fuel type * test phase is 0.012, which indicates that there is a statistically relevant interaction.

76 The same number of tests were analyzed for lux stability as for lighting efficiency - ten using kerosene as the fuel, and then three each with paraffin lamp oil and 0.5% ethanol. Each of the test phases was repeated sixteen times. More tests would indicate the significance of fuel type and its effect on the lighting efficiency.

Figure 56 shows the interaction plot for lux stability between the fuel type and the test stage.

Figure 56. Interaction between test phase and fuel type for light stability.

As can be seen from the figure, there is crossover between the clean and dirty stage stages for 0.5% ethanol and lamp oil. The interaction is highlighted by the differences in the relative shape pf the three lines – “simmer” goes down for the lamp oil but not the other two fuels, which

77 shows there is a positive impact on light stability by the combination of lamp oil and the

“simmer” phase. In comparison to the overall light efficiency, in which the fuel type was the only statistically significant factor, the stability of the light is dependent on the test phase and fuel type factors, and there is some level of statistically relevant interaction between fuel type and test stage. As can be seen in Figure 5.4, when examining the light stability by test stage, there is greater variability seen during the "simmer" stage. There is variation seen when examining the fuel type and the light stability, but the test stage appears to be the more statistically significant variable.

5.2.3 Carbon Monoxide Emission Factors Per Lux

The following will discuss solely the carbon monoxide emission factors per lux - that is, the grams of carbon monoxide emitted per kilogram of fuel burned, divided by the average lux produced over each stage of the test. This would clarify the efficiency of the process - carbon monoxide is a result of incomplete combustion, so higher carbon monoxide emission factors per unit of lux would indicate a less complete combustion reaction and the production of more carbon monoxide per kilogram of fuel. If the fuel source is producing a great deal of lux and relatively low carbon monoxide emission factors, this indicates that the process is more complete and that the user is getting more "useful work" from the lantern - that is, more light is being produced. The null hypothesis would be that neither fuel type nor test stage has an effect on the carbon monoxide emission factor per lux, with any variation being due to random chance. Table

4 shows the results of the two-way ANOVA, presented in the standard table.

Sum of Squares Df MSq F-value Pr(>F) Fuel Type 0.0394 2 0.19679 1.691 0.1976 Test Phase 0.0617 2 0.0308 2.650 0.0833 Fuel Type:Test Phase 0.0239 4 0.005985 0.514 0.7256 Residuals 0.4539 39 0.0116 Table 4. ANOVA table from CO EF per lux tests.

Figure 57 shows the outcome of the two-way ANOVA test performed for the carbon

78 monoxide emission factor per lux.

Figure 57. Results from two-way ANOVA on carbon monoxide emission factors. (Tests by factor: kerosene (10), lamp oil (3), 0.5% ethanol (3), clean start (16), dirty start (16), simmer (16)).

For fuel-type, there was an F-value of 1.691, with a Pr(>F) equal to 0.1976, which indicates that there is a 19.76% chance that the probability of randomly observing a value greater than the observed value of 1.691 if the null hypothesis were correct. For test phase, the F-value is 2.650 and the Pr(>F) is 0.0833, which indicates that there is an 8.33% probability of observing a greater value than the observed F-value if the null hypothesis is true. The p-value for the interaction between fuel type * test phase is 0.7256, which indicates that there is no statistically significant interaction. Sixteen tests total were also performed – again, ten using kerosene as the fuel, and then three each with paraffin lamp oil and 0.5% ethanol, with each of the test phases being repeated sixteen times. As such, more tests should be performed, but such results indicate that the null hypothesis is likely true, and neither testing stage nor fuel type has an effect on carbon monoxide emission factors per lux.

Figure 58 shows the interaction plot for carbon monoxide emission factors per lux between the fuel type and the test stage.

Figure 58. Interaction plot between fuel type and test phase for the two-way ANOVA on CO EF per lux.

As can be seen from the figure, there is cross over between the clean and dirty stages for ethanol, as well as for the dirty stage and “simmer” stage for paraffin lamp oil, which indicates an interaction effect. The ANOVA results show a high probability that the observed F-value could occur by chance, which indicates that the observed variations are likely due to random chance rather than any significant influence due to the factors themselves.

5.3 Conclusions

In the course of performing the two-factor ANOVA, a few correlations were determined.

There is a statistically significant link between fuel type and efficiency, which indicates that

80 certain fuels might provide better efficiency in terms of lux per gram than other types of fuels.

There is also a statistically significant link between test stage and relative stability, as determined by a rolling average of the standard deviation of the lux over a section divided by the average for this section. This indicates that, for light stability, there might be a correlation between behaviors such as starting with a clean wick or attempting to maintain a certain level of lux and the overall stability of the light. This test also indicated some interaction between fuel type and test phase, which indicates that the light stability might depend slightly on fuel type, with more statistical dependence on the testing stage.

There was no statistical difference between testing phase or fuel type for carbon monoxide emission factors per lux. Future testing might indicate that certain behaviors - cleaning the lantern more thoroughly, for example, or a different type of wick - would have a statistical relationship with this dependent variable.

As stated in Section 4, ten tests were performed with using kerosene as a fuel, and three were performed for 0.5% ethanol and paraffin lamp oil. More robust testing would likely reveal more variation in the tests, but two-factor ANOVA can still provide useful insight into the statistically significant variables.

81 CHAPTER 6: CONCLUSIONS

6.1 Summary of Thesis Achievements

This thesis has examined light output as compared to emissions from fuel-based lanterns and has devised a replicable test for comparing and contrasting different lanterns utilizing different types of liquid fuels against one another. Based off of the water boiling test commonly used to test cookstoves, this test provides a controlled and consistent way to test the lux output of lanterns in a consistent manner, and compare this useful output with the emissions produced by the combustion of the liquid fuels. It furthermore provided calculations and analysis that can be used to understand the efficiency of the light source, its stability, and the emissions that are produced during burning. The different stages of the test shed insight into how light and emissions are influenced by the state of the wick - whether it has been burned previously or not - which will likely be vital knowledge with providing "best practices" for lanterns and their various fuels.

A two-way ANOVA was utilized to probe the efficiency, lux stability, and carbon monoxide emission factors per gram of fuel used. While some interactions and significant factors were discovered - such that test stage is statistically significant when it comes to lux stability - more tests would need to be performed in order to determine whether the results are replicable, or just the influence of random chance. Only sixteen tests were analyzed for the two-way

ANOVA - ten being done with kerosene, three with paraffin lamp oil, and three more with the

0.5% ethanol mixture. While this is a useful start, more tests would be needed to probe the full depths of the effect of changing the variables and the influence on the outcomes. However, the

ANOVA tests did demonstrate that there are factors that are likely being influenced by the variation of either the test stage or the fuel type.

Previous work has discussed in broader terms how kerosene lighting is harmful for human

82 health and welfare, as well as for the environment and in terms of economics. Most work therefore recommends a push to increase access to and education surrounding LED based lighting. While this is ultimately a useful solution and should be considered when studying the problem of lighting in developing areas, fuel-based lanterns are likely to remain in circulation for years to come. As such, even as researchers work towards increasing access to emission-free and cheaper forms of lighting, it should be understood how different types of fuels or lantern configurations can potentially improve outcomes in terms of the service provided, emissions, or the economics surrounding the fuel used.

This thesis has indicated, for example, that recommending that users add small amounts of ethanol to the kerosene fuel might see better light output and overall lower carbon dioxide exposure. Such information could be used to make a best-practices recommendation. The test could be used to shed light on other aspects of lantern configuration and its impact on the light output or emissions, such as the wick, lantern types, or level of cleaning. This will prove useful in further understanding light in the developing world and determining paths forward to ensure emissions-free and useful light access for all.

6.2 Applications

As stated in Section 6.1, fuel-based lanterns will likely be continued to be used in the developing world for years to come. Such a test can help researchers and end users find the healthiest and most useful way to utilize their fuel-based lanterns, in order to minimize overall exposure to emissions and to maximize the amount of light produced. The combination of test and calculations provides insight into the efficiency and stability of light, which is useful context beyond just the emissions produced by burning the fuel or the total output of lux.

83 Similarly to the water-boiling test, this test was devised as a means for comparing and contrasting different lanterns against one another. The test could be used for testing the types of lanterns commonly utilized in developing areas, in order to understand the useful light output of the lanterns, as compared to the emissions, and guide decision-making towards a lantern and fuel mixture that appropriately balances the need for useful light with the harm that significant air pollutants can cause to human health and welfare. Such research would likely be useful in making recommendations for best-practices and understanding how configurations for the lantern can influence the output of emissions and lux.

6.3 Future Work

There remains much work that can be done in this topic. This test devised a test for measuring light and emissions from liquid-fuel based lanterns but has not found a way to compare such lanterns to other light sources, such as those that use propane or electricity. Future work could use the procedure described in Section 3 as a baseline and modify it for a test that could be used for gas-based lanterns or electric ones, which could then be used to compare the overall light output, as well as the cost of operating each type of lantern.

This thesis also only compared different fuels with the same type of lantern and the same type of wick. Future tests could utilize the testing procedure to compare different fuels, wicks, and lanterns to gain a more holistic view of how the different components effect the light output and the emissions. This section of testing used the same lantern for all tests, as well as the same type of wick, and only varied in terms of the fuel used. It is possible that changing out the wick, the type of lantern, or varying the level of cleanliness will have an influence on the lux output or the emissions. Such future work could be conducted with variations of those aspects, which will provide a fuller picture on the best way to operate a fuel-based lantern.

84 This work also did not fully examine the economics of lanterns or their fuels. As described in Section 6.1, this test was mostly concerned with the output of light from the lantern with different types of fuels, as well as with the overall emissions produced. The test could be expanded upon to include a cost analysis of running the lantern, examining the amount of fuel and wick used in the test, as well as the initial cost of the lantern, to estimate the cost of running the lantern for a family or a user in the developing world. This could then lead to understanding on which fuel type or wick will be the most affordable for people, as well as providing the most light and the least amount of emissions.

Much work has been done in the area of lighting in the developing world, but this test offers a means of expanding the understanding of how light is used, as well as the best practices in order to ensure human health and welfare.

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90 Appendix

Lighting Efficiency ANOVA

[1] :install.packages(" ggpubr") packageVersion("g gpubr")

package 'ggpubr' successfully unpacked and MD5 sums

checked The downloaded binary packages are in C:\Users\bryng\AppData\Local\Temp\RtmpWeE8Ys\downloaded_pa ckages [1] '0.4.0'

https://stat.ethz.ch/R-manual/R-devel/library/stats/html/interaction.plot.html

[2] :require(graphics)

options(repr.plot.width=6, repr.plot.height=6)

[3] :with(df, { interaction.plot(Fuel.Type, Test.Phase, Lux.gram.fuel, fixed=TRUE) } )

[4] :xx<-ordered(Lux.gram.fuel) interaction.plot(xx, Fuel.Type, Test.Phase, fixed=TRUE, col=2:3, leg.bty=. ‹→"o") interaction.plot(xx, Fuel.Type, Test.Phase, fixed=TRUE, col=2:3, type="p") })

Error in parse(text = x, srcfile = src): :4:1: unexpected '}' 3: interaction.plot(xx, Fuel.Type, Test.Phase, fixed = TRUE, col = 2:3, type =. ‹→"p") 4: } ˆ Trace back:

[ ]:df=read.csv( 'Copy of Anova-data.csv')

We’ve read in the the data into a “dataframe”. If we examine the data we see that the fuel types have been labeled ethanol, kerosone and lamp. This corresponds to 0.5% ethanol, ful kersone and lamp oil.

[16] :summary(df)

Test.Number Lux.gram.fuel Fuel.Type Test.Phase Min. : 1.000 Min. : 2.430 ethanol : 9 clean :16 1st Qu.: 2.000 1st Qu.: 4.065 kerosene:30 dirty :16 Median : 3.000 Median : 5.715 lamp : 9 simmer:16 Mean : 4.188 Mean : 5.665 3rd Qu.: 6.250 3rd Qu.: 7.040 Max. :10.000 Max. :10.380

[17] :summary(df)

[18] :fuel_type=df$Fuel .Type test_phase=df$Tes t.Phase lux=df$Lux.gra m.fuel

[19] :kable(table(fuel_type,test_phase))

| | clean| dirty| simmer| |:------|-----:|-----:|------:| |ethanol | 3| 3| 3| |kerosene | 10| 10| 10| |lamp | 3| 3| 3|

[20] :kable(table(fuel_type,test_phase))

| | clean| dirty| simmer| |:------|-----:|-----:|------:| |ethanol | 3| 3| 3| |kerosene | 10| 10| 10| |lamp | 3| 3| 3|

[21] :theMeans<-aggregate(lux, by=list(fuel_type,test_phase), FUN=mean) names(theMeans)[names(theMeans)=="x"]<-"Mean" theSDs<-aggregate(lux, by=list(fuel_type,test_phase), FUN=sd) StdDev<-theSDs$x kable(cbind(theMeans,StdDe v))

|Group.1 |Group.2 | Mean| StdDev| |:------|:------|------:|------:| |ethanol |clean | 6.853333| 0.9889557| |kerosene |clean | 6.086600| 1.9748812| |lamp |clean | 4.043333| 1.4423707| |ethanol |dirty | 8.143333| 2.1552339| |kerosene |dirty | 5.602800| 1.4341821| |lamp |dirty | 4.280000| 1.6250846| |ethanol |simmer | 6.276667| 2.0243600| |kerosene |simmer | 5.616500| 1.6192866| |lamp |simmer | 3.356667| 0.6576727|

[22] :par(mfrow=c(1,2)) options(repr.plot.width=10, repr.plot.height=4) boxplot(Lux.gram.fuel~Test.Phase,data=df, xlab="Test Phase", ylab="lux/g fuel", main="Lux Per Gram of Fuel") boxplot(Lux.gram.fuel~Fuel.Type,data=df, xlab="Fuel Type", ylab="lux/g fuel", main="Lux Per Gram of Fuel")

Now I run a 1-way anova on

XX and YY This is ∑i xi

[23] :fit<- aov(lux~fuel_ty pe ) summary(fit)

Df Sum Sq Mean Sq F value Pr(>F) fuel_type 2 46.88 23.438 9.144 0.000465 *** Residuals 45 115.34 2.563 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

[24] :fit<- aov(lux~test_ph ase) summary(fit)

Df Sum Sq Mean Sq F value Pr(>F) test_phase 2 2.92 1.458 0.412 0.665 Residuals 45 159.30 3.540

[25] :res.aov3<-aov(lux~fuel_type*test_phase, data=df)

[26] :res.aov3<-aov(lux~fuel_type+test_phase+fuel_type:test_phase, data=df)

[27] :summary(res.aov3)

Df Sum Sq Mean Sq F value Pr(>F) fuel_type 2 46.88 23.438 8.546 0.000836 *** test_phase 2 2.92 1.458 0.532 0.591904 fuel_type:test_phase 4 5.46 1.366 0.498 0.737283 Residuals 39 106.96 2.743 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

[28] :fit<- aov(out~fuel_type*test_ph ase) summary(fit)

Error in eval(predvars, data, env): object 'out' not found Traceback:

1. aov(out ~ fuel_type * test_phase)

2. eval(lmcall, parent.frame())

3. eval(lmcall, parent.frame())

4. stats::lm(formula = out ~ fuel_type * test_phase, singular.ok = TRUE)

5. eval(mf, parent.frame())

6. eval(mf, parent.frame())

7. stats::model.frame(formula = out ~ fuel_type * test_phase, drop.unused. ‹→levels = TRUE)

8. model.frame.default(formula = out ~ fuel_type * test_phase, drop.unused. ‹→levels = TRUE)

9. eval(predvars, data, env)

10. eval(predvars, data, env)

[ ]:

Light stability command

[3] :df=read.csv( 'Light stability.csv')

[4] :df

97 Test.Number Lux.stability Fuel.Type Test.Phase 1 0.2230634 kerosene clean 1 0.1460631 kerosene dirty 1 1.1169939 kerosene simmer 2 0.2535277 kerosene clean 2 0.4674954 kerosene dirty 2 1.8434112 kerosene simmer 3 0.2593271 kerosene clean 3 0.6828644 kerosene dirty 3 1.2509386 kerosene simmer 4 0.2811199 kerosene clean 4 0.5417415 kerosene dirty 4 1.2368236 kerosene simmer 5 0.2623477 kerosene clean 5 0.5258944 kerosene dirty 5 1.0645860 kerosene simmer 6 0.3505007 kerosene clean 6 0.4189824 kerosene dirty 6 0.6009369 kerosene simmer 7 0.2702874 kerosene clean 7 0.3767601 kerosene dirty 7 0.7362557 kerosene simmer 8 0.3616030 kerosene clean 8 0.4366355 kerosene dirty 8 1.2011343 kerosene simmer 9 0.3516476 kerosene clean 9 0.3245485 kerosene dirty 9 0.9680011 kerosene simmer 10 0.1632211 kerosene clean 10 0.2909638 kerosene dirty 10 1.1080176 kerosene simmer 1 0.2410546 ethanol clean 1 0.2243327 ethanol dirty 1 0.4983570 ethanol simmer 2 0.2620084 ethanol clean 2 0.1914070 ethanol dirty 2 0.8259311 ethanol simmer 3 0.2761734 ethanol clean 3 0.2139928 ethanol dirty 3 0.3595424 ethanol simmer 1 0.8321681 lamp clean 1 0.2133519 lamp dirty 1 0.7993560 lamp simmer 2 0.2547747 lamp clean 2 1.0337123 lamp dirty 2 0.5284413 lamp simmer 3 0.7442660 lamp clean

98 3 0.1838793 lamp dirty 3 0.8333894 lamp simmer [5] :require(graphics)

options(repr.plot.width=6, repr.plot.height=6)

[6] :with(df, { interaction.plot(Fuel.Type, Test.Phase, Lux.stability, fixed=TRUE) } )

99 [7] :summary(df)

Test.Number Lux.stability Fuel.Type Test.Phase Min. : 1.000 Min. :0.1461 ethanol : 9 clean :16 1st Qu.: 2.000 1st Qu.:0.2613 kerosene:30 dirty :16 Median : 3.000 Median :0.3979 lamp : 9 simmer:16 Mean : 4.188 Mean :0.5548 3rd Qu.: 6.250 3rd Qu.:0.8060 Max. :10.000 Max. :1.8434

[8] :fuel_type=df$Fuel .Type test_phase=df$Tes t.Phase lux=df$Lux.stab ility

[9] :kable(table(fuel_type,test_phase))

| | clean| dirty| simmer| |:------|-----:|-----:|------:| |ethanol | 3| 3| 3| |kerosene | 10| 10| 10| |lamp | 3| 3| 3|

[10] : kable(table(fuel_type,test_phase))

| | clean| dirty| simmer| |:------|-----:|-----:|------:| |ethanol | 3| 3| 3| |kerosene | 10| 10| 10| |lamp | 3| 3| 3|

[11] :theMeans<-aggregate(lux, by=list(fuel_type,test_phase), FUN=mean) names(theMeans)[names(theMeans)=="x"]<-"Mean" theSDs<-aggregate(lux, by=list(fuel_type,test_phase), FUN=sd) StdDev<-theSDs$x kable(cbind(theMeans,StdDe v))

100 |:------|:------|------:|------:| |ethanol |clean | 0.2597455| 0.0176684| |kerosene |clean | 0.2776646| 0.0624701| |lamp |clean | 0.6104029| 0.3111033| |ethanol |dirty | 0.2099108| 0.0168381| |kerosene |dirty | 0.4211949| 0.1491919| |lamp |dirty | 0.4769812| 0.4823684| |ethanol |simmer | 0.5612768| 0.2394761| |kerosene |simmer | 1.1127099| 0.3338631| |lamp |simmer | 0.7203956| 0.1671059|

[12] :par(mfrow=c(1,2)) options(repr.plot.width=10, repr.plot.height=4) boxplot(Lux.stability~Test.Phase,data=df, xlab="Test Phase", ylab="lux", main="Light Stability (Lux)") boxplot(Lux.stability~Fuel.Type,data=df, xlab="Fu el Type", ylab="lu x", main="Light Stability (Lux)")

Now I run a 1-way anova on XX

and YY This is ∑i xi We will now analyze the light stability as a function of fuel type: fit<- aov(lux~fuel_ty

101

pe ) summary(fit)

Df Sum Sq Mean Sq F value Pr(>F) fuel_type 2 0.494 0.2470 1.747 0.186 Residuals 45 6.362 0.1414

Because the F-value is a 1.747, the Pr(>F) is 0.186, meaning there is a 18.6% chance that the actual F- value is higher than 1.747. Thus, we would say that the light stability is not statistically dependent on the fuel type. Now we are running a test comparing the light stability to the test phase.

[13] :fit<- aov(lux~test_ph ase) summary(fit) Df Sum Sq Mean Sq F value Pr(>F) test_phase 2 3.507 1.7535 23.56 9.99e-08 *** Residuals 45 3.349 0.0744 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

The F value is 23.56, Pr(>F) is less than 0%, meaning there is a 0% chance that the actual F value is higher than 23.56. Thus we would say that the light stability is not statistically dependent on test phase, given the number of tests done.

[14] :f it<-aov(lux~fuel_type*test_phase) summary(fit)

Df Sum Sq Mean Sq F value Pr(>F) fuel_type 2 0.494 0.2470 4.656 0.0154 *

test_phase 2 3.507 1.7535 33.049 4.02e-09 *** fuel_type:test_phase 4 0.786 0.1965 3.703 0.0120 * SignifResidu. codesals: 0 '***' 0.00391 '**2.069' 0.0 1 '0.0531*' 0.05 '.' 0.1 ' ' 1 ---

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CO EF per lux

103

ï..Test.Number CO.emission.factor.per.lux FuelType Test.Phase 1 0.013749620 kerosene clean 1 0.081581080 kerosene dirty 1 0.088914210 kerosene simmer 2 0.164995442 kerosene clean 2 0.201661096 kerosene dirty 2 0.151245822 kerosene simmer 3 0.320824471 kerosene clean 3 0.018332827 kerosene dirty 3 0.004583207 kerosene simmer 4 0.146662615 kerosene clean 4 0.187911476 kerosene dirty 4 0.155829029 kerosene simmer 5 0.270409197 kerosene clean 5 0.123746582 kerosene dirty 5 0.096247341 kerosene simmer 6 0.187911476 kerosene clean 6 0.187911476 kerosene dirty 6 0.164995442 kerosene simmer 7 0.160412235 kerosene clean 7 0.142079408 kerosene dirty 7 0.119163375 kerosene simmer 8 0.274992404 kerosene clean 8 0.123746582 kerosene dirty 8 0.109996961 kerosene simmer 9 0.105413755 kerosene clean 9 0.091664135 kerosene dirty 9 0.142079408 kerosene simmer 10 0.274992404 kerosene clean 10 0.119163375 kerosene dirty 10 0.105413755 kerosene simmer 1 0.455112428 ethanol clean 1 0.049956953 ethanol dirty 1 0.055915122 ethanol simmer 2 0.109996961 ethanol clean 2 0.119163375 ethanol dirty 2 0.109996961 ethanol simmer 3 0.109996961 ethanol clean 3 0.600400081 ethanol dirty 3 0.215410716 ethanol simmer 1 0.265825990 lamp clean 1 0.077914514 lamp dirty 1 0.068748101 lamp simmer 2 0.247493163 lamp clean 2 0.128329788 lamp dirty 2 0.169578649 lamp simmer 3 0.037582295 lamp clean

104 3 0.007333131 lamp dirty 3 0.000916641 lamp simmer [1] :summary(df)

ï..Test.Number CO.emission.factor.per.lux Fuel.Type Test.Phase Min. : 1.000 Min. :0.0009166 ethanol : 9 clean :16 1st Qu.: 2.000 1st Qu.:0.0909767 kerosene:30 dirty :16 Median : 3.000 Median :0.1237466 lamp : 9 simmer:16 Mean : 4.188 Mean :0.1492980 3rd Qu.: 6.250 3rd Qu.:0.1879115 Max. :10.000 Max. :0.6004001

[2] :fuel_type=df$Fuel.Type test_phase=df$Test.Phase lux=df$CO.emission.factor .per.lux

[3] :kable(table(fuel_type,test_phase))

| | clean| dirty| simmer| |:------|-----:|-----:|------:| |ethanol | 3| 3| 3| |kerosene | 10| 10| 10| |lamp | 3| 3| 3|

[4] :kable(table(fuel_type,test_phase))

| | clean| dirty| simmer| |:------|-----:|-----:|------:| |ethanol | 3| 3| 3| |kerosene | 10| 10| 10| |lamp | 3| 3| 3|

[5] :theMeans<-aggregate(lux, by=list(fuel_type,test_phase), FUN=mean) names(theMeans)[names(theMeans)=="x"]<-"Mean" theSDs<-aggregate(lux, by=list(fuel_type,test_phase), FUN=sd) StdDev<-theSDs$x kable(cbind(theMeans,StdDe v))

|Group.1 |Group.2 | Mean| StdDev| |:------|:------|------:|------:|

105 |ethanol |clean | 0.2250354| 0.1992525| |kerosene |clean | 0.1920364| 0.0940383| |lamp |clean | 0.1836338| 0.1268160| |ethanol |dirty | 0.2565068| 0.2998238| |kerosene |dirty | 0.1277798| 0.0561928| |lamp |dirty | 0.0711925| 0.0607778| |ethanol |simmer | 0.1271076| 0.0811128| |kerosene |simmer | 0.1138469| 0.0466173| |lamp |simmer | 0.0797478| 0.0848673|

[6] :par(mfrow=c(1,2)) options(repr.plot.width=10, repr.plot.height=4) boxplot(CO.emission.factor.per.lux~Test.Phase,data=df, xlab="Test Phase", ylab="lux/g fuel", main="CO Emission Factor Per Lux") boxplot(CO.emission.factor.per.lux~Fuel.Type,data=df, xlab="Fuel Type",ylab="lux/g fuel", main="CO Emission Factor Per Lux")

Now I run a 1-way anova on XX and YY This is ∑i xi We will now analayze the CO EF as a function of fuel type. :fit<- aov(lux~fuel_t

106 ype ) summary(fit)

Df Sum Sq Mean Sq F value Pr(>F) fuel_type 2 0.0394 0.01968 1.641 0.205 Residuals 45 0.5395 0.01199

Because the F-value is a 1.641, the Pr(>F) is 0.205, meaning there is a 20.5% chance that the actual F-value is higher than 1.641. Thus, we would say that the Lux is not statistically dependenet on the fuel type. Now we are running a test comparing the CO EF to the test phase. :fit<- aov(lux~test_ph ase) summary(fit)

Df Sum Sq Mean Sq F value Pr(>F) test_phase 2 0.0617 0.03084 2.683 0.0793 . Residuals 45 0.5172 0.01149 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

The F value is 2.683, Pr(>F) is 7.9%, meaning there is a 7.9% chance that the actual F value is higher than 2.683. Thus we would say that the CO EF is not statistically dependent on test phase, given the number of tests done. [7] :f it<- aov(lux~fuel_type*test_phas e) summary(fit)

D Sum Mean Sq F valu Pr(>F) f Sq e fuel_type 2 0.039 0.01967 1.69 0.1976 4 9 1 test_phase 2 0.061 0.03084 2.65 0.0833 . 7 0 0 1 '*' 0.05 '.' 0.1 ' ' 1 Signif. codesfuel_type:test_ph: 0 '***' 0.0041 0.023 '**' 0.00.00598 0.51 0.7256 ase 9 5 4 Residuals 3 0.453 0.01163 9 9 8 ---

107