1 Sleep Loss and Environmental Exposures in Asthma Patients

Home , Cholestane

Sleep Loss and Environmental Exposures in Asthma Patients (SLEEAP): Chemical and Statistical Analyses for Interior Aerosols from Buffalo, NY Residences

Thesis

Presented in Partial Fulfillment of the Requirements for the Degree Master of Science in the Graduate School of The Ohio State University

Johnson Luma, B.S.C.E.

Graduate Program in Civil Engineering

The Ohio State University

2018

Thesis Committee:

Dr. Andrew May, Advisor

Dr. Karen Dannemiller

Dr. Jeffrey Bielicki

Copyrighted by

Johnson Luma

2018

Abstract

We spend up to 90% of our time indoors and more than 30% of our time in the bedroom.

Although aerosols and poor air quality most commonly occur outdoors, they can also occur inside the home and be very detrimental to health and sleep. The Ohio State University and the University of Buffalo are collaborating to determine how indoor exposure may be measured and to see how these pollutants contribute to asthma. In this study, 51 dust samples were retrieved from the bedroom doorframes of asthmatic adult women residing in Buffalo, New York. Six environmental standards were used to determine whether certain compounds, especially those related to combustion sources, can be found in the dust samples. Compounds in our standards include n-alkanes (C7-C40), PAHs, cholesterol, 5- alpha-cholestane, nicotine, and phthalates. The first five compounds and compound groups are associated with combustion, thereby allowing us to test the influence of combustion sources on indoor dust. We used Gas-Chromatography Mass-Spectrometry (GC-MS) to analyze our samples. We evaluated calibration curves and reviewed measures such as

Limits of Detection (LOD) and Limits of Quantification (LOQ). We then picked twelve activities which we thought would lead to greater contributions of the chemicals we measured in the indoor aerosol. Using SAS (version 9.3), we tested for which compounds were significant based on a p-value and q-value less than 0.05. Nicotine was the only compound that was significant to all its associating activities. After the preliminary stage of this research, more work including new methods will be needed to further calculate compound significance, calculate compound concentration in samples, and to perform a source contribution. ii

Dedication

Dedicated to the students at The Ohio State University

iii

Acknowledgements

I could not have completed this Master’s Thesis without the contributions and efforts of several individuals whom I know at a personal level as well as those with whom I collaborated from The Ohio state University and the University of Buffalo. First, I would like to thank God for

His faithfulness as He has brought me tremendous success throughout my entire academic career and during my time at Ohio State. Next, I would like to thank my parents who brought their small children here from Haiti for better education and opportunities. My parents have instilled the great discipline, encouragement, and wisdom into my life that has carried me this far.

My next set of acknowledgements goes towards my advisors and my collaborators. First, I am deeply grateful to my advisor, Dr. Andrew May, who not only guided me throughout the completion of this project and document, but also the faculty member to have accepted to be my host professor after I applied to attend the Ohio State’s Graduate & Professional Student

Recruitment Initiative during the fall of 2015. Second, I would like to acknowledge Dr. Karen

Dannemiller who is the Principle Investigator as well as one of my advisors in this collaborative effort. Third, I would also like to thank Dr. Marcia Nishioka who taught me the ins and outs of the

GC-MS instrument as well as many of the methods I used during my analyses. Fourth, I would also like to thank Ashleigh Bope-a fellow peer as well as a collaborator who made herself available to guide me in some of the initial phases of my experiments. Last but not least, I want to thank Dr.

Jessica Castner who provided the samples as well as the survey information we used. Dr. Castner also provided some insightful contributions and suggestions for finalizing this document. And many thanks to the residents of Buffalo who were willing to contribute to this study. Their

iv willingness and contributions go a long way in bringing about improvements in public health as well as to the nation’s infrastructure.

I would further like to acknowledge sources of funding which made this project, thesis, and my involvement in this work possible. I would like to thank my home institution, The Ohio

State University, for providing funding for this project through the Sustainable and Resilient

Economy SEED Grant. Moreover, the project was co-sponsored by the Rockefeller University

Heilbrunn Family Center for Research Nursing through the generosity of the Heilbrunn Family.

Support was also provided/committed by the University at Buffalo HomeBASE Center and the

Buffalo Clinical and Translational Research Center.

Vita

May 2011……………….… Stratford Stem Magnet High School

December 2015………….....B.S. Civil Engineering, The University of Tennessee, Knoxville

May 2018…………………..M.S. Civil Engineering, The Ohio State University

Fields of Study

Major Field: Civil Engineering

Table of Contents

Abstract ...... ii Dedication ...... iii Acknowledgements ...... iv Vita ...... vi List of Tables ...... x List of Figures ...... xi Chapter 1: Introduction ...... 1 1.1. Motivation ...... 1 1.2. Exposure Environment ...... 1 1.3. Aerosols and Particulate Matter (PM) ...... 2 1.3.1 Contributors to Aerosol Particles ...... 4 1.4. Adverse Health Effects Associated with Aerosols ...... 5 1.5. Measuring Exposure ...... 8 1.6. Objectives of This Study ...... 10 Chapter 2: Materials and Methods ...... 11 2.1. Collection and Storage of the Indoor Aerosol Samples ...... 11 2.2. Extraction of the Indoor Aerosol Samples ...... 11 2.3. Instrument: Usage, Advantages, Specifications, and Protocols ...... 12 2.3.1. Usage of Gas-Chromatography Mass-Spectrometry (GC-MS) Instruments 12 2.3.2. Data Interpretation Software ...... 15 2.3.3. Temperature Protocol...... 15 2.4. Experiments ...... 16 2.4.1. Standards ...... 16 2.4.2. Compound Detection and Integration ...... 17 2.4.3. Calibration Curves ...... 19 2.5. Difficulties Encountered during Preparations and Resolutions ...... 20 2.5.1. Quality Assurance ...... 21 2.6. Adjustments from Resulting Concurrences ...... 21 2.7. Separating and Running the Samples ...... 22

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2.8. Analyzing and Interpreting the Data ...... 22 2.9 Limits of Detection (LOD) and Limits of Quantification (LOQ) ...... 23 2.9.1. Limit of Detection (LOD) ...... 23 2.9.2. Limit of Quantification (LOQ) ...... 24 2.10. Participant Surveys ...... 24 2.11. Statistical Analysis ...... 26 Chapter 3: Results and Discussions ...... 28 3.1. Calibration Curves ...... 28 3.2. Integration Results from the 51 Samples ...... 31 3.3. Blanks ...... 35 3.4. Limits of Detection (LOD and Limits of Quantification (LOQ) ...... 36 3.5. Statistical Analysis ...... 39 3.5.1. Activities which We Evaluated for Associations with Nicotine ...... 41 3.5.2. Activities which We Evaluated for Associations with N-Alkanes, 5-Alpha- Cholestane, and PAHs ...... 42 3.5.3. Activities which We Evaluated for Associations with Cholesterol ...... 42 3.5.4. Activities which We Evaluated for Associations with Phthalates ...... 43 Chapter 4: Conclusions & Future Work ...... 46 4.1. Conclusions ...... 46 4.2. Future Work ...... 46 4.2.1. Source Apportionment ...... 46 4.2.2. Collecting More Indoor Dust ...... 47 4.2.3. Improve Level of Details in the Survey Questions ...... 47 4.2.4. Have More Standards/Evaluate More Peaks...... 48 4.2.5. Addressing Interferences and Distortions with Current Samples ...... 48 References ...... 50 Appendix A: Result Tables ...... 56 Results from Statistical Analyses...... 56 Table 9. Activity 1. Have you ever smoked cigarettes? ...... 56 Table 10. Activity 2: Do you smoke cigarettes now? ...... 58 Table 11. Activity 3: Have you or someone smoked inside of your home in the past week? ...... 59

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Table 12. Activity 4: Was there any diesel vehicle parked around your home in the last 48 hours? ...... 61 Table 13. Activity 5: Did you use a gas lawn mower in last 48 hours? ...... 63 Table 14. Activity 6: Have you or someone grilled indoors in the last 48 hours?.... 64 Table 15. Activity 7: Is gas used for cooking? ...... 66 Table 16. Activity 8: Were you exposed to chemicals in the last 48 hours? ...... 68 Table 17. Activity 9: Have you had a major home renovation in the past year? ...... 69 Table 18. Activity 10: Have you been exposed to or have you used chemical solvents in the last 48 hours? ...... 71 Table 19. Activity 11: Have you dealt with or been exposed to cleaning solutions in the last 48 hours? ...... 73 Appendix B: Codes Used for Calculations ...... 75 Matlab Code to Obtain Calibration Curves, Slopes, Y-Intercept, R-Squared ...... 75 Matlab Code for LOD ...... 77 Matlab Code for LOQ ...... 77 Code for Significance in the Statistical Analysis Software (SAS) ...... 78 Related Survey Questions for Environmental Exposure (Castner et al., 2018)...... 80

List of Tables

Table 1. Legends and Notes Added During Analysis of the Compounds...... 19 Table 2. Activities Evaluated to Find Which Compounds Were Significant...... 25 Table 3. Summary Statistics of Compound Area Counts from Samples: N-alkanes...... 32 Table 4. Closer Observation of Area Count Statistics Pentacosane...... 33 Table 5. Summary Statistics of Compound Area Counts from Samples: Cholesterol, 5- Aplha-Cholestane, Nicotine, Phthalates, PAHs...... 34 Table 6. Area Counts of N-alkanes from Blanks; LOD & LOQ...... 37 Table 7. Cholesterol, 5-Aplha-Cholestane, Nicotine, Phthalates, PAHs Area Counts from Blanks; LOD & LOQ...... 38 Table 8. Activities, Potentially Significant Compounds, and p-values and q-values (All numbers are q-values unless noted as p-value)...... 40

List of Figures

Figure 1. Basic Schematic of the Indoor Exposome...... 8 Figure 2. Gas Chromatogram (Top) and Mass Spectrometer (Bottom) from Our GC-MS...... 14 Figure 3. Best Examples of a Compound Peak...... 17 Figure 4. Limited Signal and Noise in GC-MS Results.s ...... 18 Figure 5. Compound: Nicotine: Linear Calibration Curve Ranging from 10 to 5,000 ng/mL...... 29 Figure 6. Compound: Nicotine-Linear Calibration Curve Ranging from 10 at 600 ng/mL...... 30

Chapter 1: Introduction

1.1. Motivation

We spend approximately 90% of our lives indoors (Klepeis, 2001). We further spend about 33% of our lives inside of the bedroom (OECD, 2011). The time we spend in our bedrooms will expose us to aerosols and particulate matter that can be very damaging to our health. In this environment, we will be extensively exposed to complex mixtures of chemicals and microbes, which constitute the “indoor exposome” (Dannemiller et al.,

2016). Exposome is defined as the measure of exposure experienced by an individual and how that exposure relates to their health (NIOSH, 2014). Exposure is commonly ignored due to the difficulty of environmental measurement. Traffic pollutants, allergens, and viral communities impact human health, and we want to gain a better understanding of their impact (Dannemiller et al., 2016). In this work, we want to eventually measure the significance of these pollutants in various indoor activities. In this thesis, I will discuss our work and will provide the statistical analysis to interpret the data.

1.2. Exposure Environment

As mentioned above, approximately 90% of our lifetime is spent indoors, and 33% is spent inside of our bedrooms. These two percentages are for adults. Infants however, especially those who are three years or younger usually spend close to 100% of their time indoors (Combs et al., 2016). Unfortunately, many air pollutants can be found indoors, and these pollutants, being hazardous to our health, have higher concentrations when indoors

(Rudel et al., 2009). The work of Qian et al. (2012) concluded that human occupancy would

1 result in a greater increase of particulate matter, bacteria, and fungal genomes. Other works have also concluded that aerosols that are present indoors have a higher concentration than those in the ambient air due to the building architecture, occupancy levels, and lack of ventilation (Destaillats et al., 2008; Löfroth et al., 1991; Meadow, 2013).

The 33% of an individual’s lifetime spent in the bedroom is derived from the average daily adult sleep time which is about 8-9 hours (OECD, 2011). These 8-9 hours of sleep (usually assumed to be in the bedroom), usually take place in the sleep microenvironment. The sleep microenvironment is the space around which an individual sleeps, and this space contains everything from bedding furniture and the breathing zone.

As a result, individuals are subjected to acute and chronic exposure to pollutants in this region (Boor, 2014; Boor, 2015; Boor et al., 2017; Laverge, 2013). In this work, we will not go over the sleep microenvironment in detail, but we will analyze pollutants found in the bedroom and determine exposure.

1.3. Aerosols and Particulate Matter (PM)

The atmosphere contains different concentrations of polluting particles. These particles can be particulate matter (PM) or aerosol. PM and aerosol particles are not altogether different and often overlap with similar properties. They are composed of various compounds and matter which affect the quality of our air. “Air pollution” occurs when these particles are present at greater concentrations than their normal ambient levels, and this excess concentration leads to negative impacts on humans, animals, vegetation, and materials (Seinfeld and Pandis, 2016). This work encompasses contributions from both types of particle pollutants. Therefore, for the purposes of this research, aerosol, PM, and

2 air pollutants (or airborne particles) can be used interchangeably for the particulates which affect or “pollute” the quality of our air.

Aerosol concentrations will vary throughout different areas. Aerosol particles have varying compositions and sources. Some can be generated from combustion activities such as a running an automobile, power generation, and wood burning. Other sources of aerosol include windblown dust, pollen, plant fragment, and sea salt. The size of an aerosol particle can be as small as a few nanometers to as large as 1 µm. The size of the particle has a huge impact on its lifetime and time in the atmosphere (Seinfeld and Pandis, 2016).

Aerosols can further be generalized into the source region (i.e. urban, rural, and marine). Urban aerosols for example are composed of a mixture of industrial particulate emission, transportation, power generation, natural sources, and secondary material. Size distribution of urban aerosol varies tremendously across the area, and concentration of specific aerosol emissions will decrease tremendously away from the source. Rural aerosols are generally from natural and anthropogenic sources (Seinfeld and Pandis, 2016).

Next, aerosols not only occur in the ambient air or atmosphere, they can also migrate into buildings (commercial, residential and public). The indoor concentration of aerosol is greatly affected by the outdoor concentration. In addition to migration of aerosol indoors, several indoor emission sources may exist in the building. Two common indoor emission sources are cooking and smoking (Na, 2005). A third common activity contributing to indoor aerosol concentration is cleaning (Martuzevicius, 2008). Cleaning products and air freshener contain volatile organic compounds (VOCs) and chemicals which can react with oxidants to form indoor secondary particles (Nazaroff, 2004).

1.3.1 Contributors to Aerosol Particles

Cigarette smoking was found to be the most important indoor source of fine and coarse particulates. Cooking had secondary importance, although more research was needed to identify the remaining unknown sources (Wallace, 1996). Sheldon et al. (1989) studied the effects of combustion sources on indoor air quality. Effects of kerosene heaters, gas stoves, wood stoves, fireplaces, and cigarette smoking were all evaluated. Smoking was found to create the most significant PM2.5 concentration than all the other emission sources on the list. Since these previous works by Wallace (1996) and Sheldon et al. (1989), more recent work has shown that cooking, smoking, and other combustion sources are still top producers of indoor aerosol (Afshari et al., 2005; Martuzevicius et al., 2008; See and

Balasubramanian, 2006). In our work, we have indoor aerosol samples from the urban region of Buffalo, NY, and we will be analyzing aerosols with the primary intent of identifying compounds usually associated with combustion sources.

Rushdi et al. (2014) studied characteristics of organic compounds in indoor aerosol particulate matter in Saudi, Arabia. The researchers identified from a list of n-alkanes, polycyclic aromatic hydrocarbons (PAHs), and plasticizers (phthalates), as sources of PM.

The GC-MS was used to analyze and identify the aerosol particles. Rushdi et al. (2014) performed a very similar work to what we will perform for the city of Buffalo, NY.

Many of the compounds from the aforementioned works occur from combustion and can be very detrimental to our health (IARC, 2007; Rogge et al., 1993; Simoneit, 2002;

Swan, 2008). These compounds can serve as “tracer” or “marker” activities, meaning detection of one of these compounds in an aerosol sample will give indication of what

4 activity is contributing to those particulates (Rogge et al., 1991). Tracer compounds are especially important since an aerosol sample will contain various constituents. A common tracer for cigarette smoke is nicotine.

Rogge et al. (1993) suggested that the detection of nicotine in fine particulate matter is an indication that cigarette smoke is indeed present. Rogge et al. (1991) and Schauer et al. (1996) both found that cholesterol along with several other fatty acids could serve as an important tracer for meat cooking. Cai et al. (2017) found that n-alkanes were the most abundant species in organic matter taken from vehicular emissions. The researchers further evaluated different ratios and averages for many cholestane compounds and identified a cholestane ratio of .863 can be used as a tracer for vehicular emission. Larsen and Baker

(2003) and Yang et al. (2005) concluded that PAHs can be used as a tracer for motor vehicle emissions. Harrison et al. (1996) concluded that PAHs could specifically be used as a tracer for diesel emissions. Finally, phthalates can serve as markers for plasticizers, polyvinyl chloride (PVC), polyvinyl acetate (PVA), paints, glues, personal care products, automobile parts, fragranced products, and food packaging parts (Bornehag, 2005; Buckeley et al.,

2012; Claussen et al., 2003; Dodson et al., 2012; Fromme et al., 2004; Parlett et al., 2013;

Rudel et al., 2003; Wensing et al., 2005).

1.4. Adverse Health Effects Associated with Aerosols

Given the amount of time we spend indoors, human exposure to indoor air pollutants are greater and can be more detrimental than outdoor exposure (Rudel et al.,

2009). Effects of exposure to aerosol particles can be anything from negligible to fatal depending on dose (Morris, 2001; Lanki et al., 2006). The health risks associated with air

5 pollutants depend on the pollutants’ chemical composition. The source and properties of indoor aerosols depend on the microenvironment (Chitra et al., 2018). Different residences will have indoor aerosols that are of different health risks to residents. Some homes may be near major emission sources such as a major freeway or manufacturing site, while some may be nearer to natural emission sources. Some homes may have residents who smoke frequently, while others do not have any smokers.

Morawska (2013) found that particulate matter contributes to as much as 30% of the burden of disease. Pope et al. (2004) found that an increase of PM2.5 exposure by 10

μg/m3 increased the risk of death up to 18%. Frequent exposure to particulates have also been correlated with an increasing risk for bronchitis and other respiratory illnesses: of note loss of lung function and risk of lung cancer (Schwartz, 1993). Acute bronchitis and cough in young children can be attributed to exposure at even low concentrations (Dockery et al.,

1989).

Children are especially affected by an elevated concentration of indoor aerosols since they breathe in more air than adults (Chitra, 2018). As mentioned above, children three years old and younger are indoors for about 100% of the time (Combs et al., 2016).

When it comes to asthma patients, an increase in the PM concentration usually leads to more emergency room visits (Norris, 1999). Lung conditions of asthmatic individuals also worsen when they are exposed to high levels of PM (Baldacci, 2015). Research has proven that combustion-derived PM leads to inflammation in the lungs. Due to the small aerodynamic diameter of PM, it can easily be inhaled into the lungs and cause havoc within

6 the human system. PM2.5 can be inhaled more deeply into the lungs and get deposited on the alveoli and can even enter pulmonary circulation (Wu et al., 2018).

Wu et al. (2018) further evaluated the human respiratory system’s vulnerability to

PM. The researchers found how the lungs are often the primary site for inflammation. PM particles cause injury in airway epithelial cells, monocytes, and macrophages. Stress forms on the cells and blood vessels and even affects the heart. Brooks et al. (2010) found that those who have illnesses such as asthma, diabetes, myocardial infarction, and chronic obstructive pulmonary disease (COPD) have a higher risk for disease exacerbation when exposed to PM.

Schultz et al. (2017) found that annual average PM2.5 could detrimentally impact lung function, obstruct airways, and lead to asthmatic symptoms. Further, when PM2.5 levels were above 30 μg/m3 for just 7 to 21 days of the year, asthma prevalence increased by 67% in comparison to days where PM2.5 levels did not rise as much. Results from

Schultz et al. (2017) also showed that living 300 m within an Interstate highway increased the odds for asthma three-fold. Finally, the work found residing 800 m away from an industrial site increased the likelihood for asthma by 47%.

Nazaroff and Weschler (2004) found that exposures to chemicals emitted by cleaning products can also aggravate asthma. A greater concentration of the pollutants is created by the confined indoor environment, and both poisoning and acute health effects have been reported in many cases. Individuals with pre-existing respiratory ailments such as asthma will be most vulnerable to the greater concentrations of aerosols in the air that come from cleaning activities and cleaning solutions. The work also found that VOCs from

7 cleaning products contain a list of extremely toxic chemicals such as formaldehyde and benzene which are known to worsen asthma conditions. These two compounds are also carcinogenic at very low levels. Colborn et al. (1993) found that chemicals in the sleep microenvironment can cause endocrine disorders. Finally, Phthalates such as Dibutyl

Phthalate (DBP) have been shown to affect the male gender (European, 2004).

1.5. Measuring Exposure

Figure 1. Basic Schematic of the Indoor Exposome. Figure 1 displays how chemicals and microorganisms circulate within the confines of a home. The mix of particulate matter constitutes the indoor exposome. Chemicals and microorganisms can be found at high concentrations within the home. Exposure to these aerosol particles, via inhalation, can lead to many types of illnesses. Sleep disruption and further aggravation of respiratory illnesses are some of the main side effects of bedroom exposure. 8

Ginsberg and Belleggia (2017) performed work to prioritize chemicals found in house dust based on risk levels. Prioritizing the chemicals would lead to further assessment to reduce exposure and protect, in particular, children’s health. House dust contains a variety of chemicals that are of concern to public health. House dust may serve as a reservoir for both semi-volatile and non-volatile chemicals. For our work, we will also be analyzing the constituents of house dust to measure exposure. The work of Ginsberg and

Bellegia (2017) gives an account of which compounds in the house dust samples were set as a priority and what can be done to reduce exposure levels in the future.

Boor et al., (2017) wanted to determine and contribute to the lack of knowledge surrounding how exposure to indoor air pollutants affect both sleep and human health. The researchers provided an analysis of the duration of sleep exposure period as well as how the bedding furniture and surrounding air contributed to chemicals and pollutants in the sleep microenvironment. The researchers concluded that more work is needed to assess impact of aerosol exposure during sleep and how exposure affects sleep quality. The entirety of our work will add some much needed details to these questions.

Dannemiller et al. (2015) completed work on how housing characteristics affected human exposure to microorganisms. The research had asthmatic children as participants.

Due to the varying characteristics from house to house, different microorganisms affect the children. Their work associates closely with what we will discuss in this thesis document.

We will determine housing characteristics with our compounds of interest, specifically those from combustion sources.

1.6. Objectives of This Study

Given the major health risks and the lack of assessment done on indoor exposure, we will aim to conduct our own research to quantify and characterize indoor exposure for

51 homes in Buffalo, NY. We especially want to measure exposure for those who already suffer from respiratory ailments. Aerosol samples were taken from the home of asthmatic individuals, particularly from the bedroom. Each home and each bedroom has its own microenvironment for aerosol due to the location of the home and due to the activities that occur within and outside of the home. Thus, a survey was used to collect data to gather the characteristics necessary to further help us is our analysis.

Chapter 2: Materials and Methods

2.1. Collection and Storage of the Indoor Aerosol Samples In this work, we perform a chemical analysis on aerosol samples taken from 51 residences in Buffalo, NY. Dust was collected by a collaborator from the University of

Buffalo. Sterile cotton swabs were ran across the doorframes of the primary bedroom to collect the indoor aerosol samples. These samples were then stored in a freezer at -80°C on the same day of the dust collection. Samples were later stored in dry ice and shipped to

The Ohio State University’s Environmental Engineering Laboratory. Before extraction, we cut the swab a few millimeters above the cotton tip where dust had been collected, and we put the samples in a 2 mL polyethylene plastic screw top vial and stored them in a freezer at -80°C until extraction.

2.2. Extraction of the Indoor Aerosol Samples We extracted the 51 samples for the chemical analysis as well as a microbial analysis which we will not discuss in this document. We removed the 51 samples from the

2 mL plastic vials and placed them in 40 mL amber vials. We cleaned and filled each 40 mL vial beforehand with a 3 mL solution of Gas Chromatography (GC) grade Hexane mixed with Isopropanol in a 3:1 ratio (2.25 mL Hexane and 0.75 mL Isopropanol). Next, we pipetted this solution into each 2 mL plastic vial to remove as much dust residue as possible and then pipetted the mixture back into its corresponding 40 mL amber vial.

During the extractions, we spiked in two organic analytical standards (deuterated

PAHs) to the solution for tracking throughout our analysis. First, we spiked in 2 µL of

Acenaphthene-d10 (500 µg/mL) to the 3 mL solution. Second, we split the 3 mL solution

11 in half for the separate microbial and chemical analyses. Third, we spiked in 2 µL of

Pyrene-d10 (500 µg/mL) into the 1.5 mL solutions.

After the split and spikes, half of the 1.5 mL samples went towards the chemical analysis and the other half towards the microbial analysis. For the chemical analysis, we further sonicated the extractions for 30 minutes to agitate and mix the solution and analytes.

The extraction solution was poured into pre-rinsed Nunc centrifuge tubes (339650) and centrifuged at 3000 G for 10 minutes. Each fraction was transferred into a separate 1.5 mL pre-cleaned glass sampling vial (pre-baked at 450°C for 8 hours). After transferring, the 51 vials set apart for the chemical analysis were stored in the refrigerator under normal refrigerator temperatures.

2.3. Instrument: Usage, Advantages, Specifications, and Protocols

2.3.1. Usage of Gas-Chromatography Mass-Spectrometry (GC-MS) Instruments

As part of our analysis, we want to identify the compounds in our samples and eventually the compound concentrations. Our primary analysis instrument is the Gas-

Chromatography Mass-Spectrometry (GC-MS). The GC-MS is widely used to identify small and volatile molecules. The instrument can thoroughly separate complex mixtures, quantify analytes, and detect trace levels of organic contamination. These capacities make it advantageous compared to similar analysis tools such as the LC-MS

We used a 2000 ThermoScientific TracePolaris Q GC-MS with a DB-5ms column

(5% Phenyl and 95% Methyl Silicone) with a 30 m length, 0.25 mm internal diameter, and a 0.25 µm film thickness. The DB-5ms column allows for better sensitivity and mass spectral integrity. It is a very slightly polar and general purpose column.

The different compounds of the sample elute from the column at specific times.

The time at which compound elution occurs is known as the compound’s retention time.

We further used helium as our carrier gas. For electron impact ionization, helium is usually the gas used. Flow of the carrier gas was set at 1 mL/min. 1 mL/min is a common standard flow rate to use for this type of column and purpose.

The first stage of the GC-MS analysis is the gas chromatograph. In this stage, the sample is volatized, thereby allowing it to separate into its different components using a capillary column. The next stage of the GC-MS analysis is the mass spectrometry. After the components leave the GC column, a mass spectrometer is used to ionize the compounds using either a chemical or electron source. Once ionized, molecules are sent to the instrument’s mass analyzer. The mass analyzer separated the ions into their mass-to-charge

(m/z) ratios. For most compounds, the charge is +1, yielding an m/z ratio equal to the compounds molecular weight.

Finally, our next stage is ion detection and analysis. The GC-MS is generally accompanied by a software that shows analysis results. The data interpretation software of a GC-MS will contain a gas chromatogram and a mass spectrometry view. Thermo

Xcalibur software was used with our GC-MS to acquire and process our data. Compound peaks appear as a function of their m/z ratios in the gas chromatogram. In complex samples, many peaks will appear. The size of the peak represents the quantity of the compound.

Figure 2. Gas Chromatogram (Top) and Mass Spectrometer (Bottom) from Our GC-MS. Figure 2 gives an example of both the gas chromatogram and mass spectrometer views from the GC-MS used in our analysis. Thermo Xcalibur provides the retention times and area counts for the compounds of interests. On the top is the gas chromatogram, and on the bottom is the mass spectrometer. The many peaks along that chromatogram represents the many compounds that can be found in a dust sample. We identify these compounds by their retention times and by their ions from the mass spectrometer.

2.3.2. Data Interpretation Software

The Xcalibur data system provides a library from the National Institute of Standards and Technology (NIST, 2017) to help us identify our target compounds. The library provides compound probabilities for all potential compounds detected. However, after considering the complex matrices of our samples and after analyzing a few earlier samples on the GC-MS, we decided to manually analyze our samples using the top three ions obtained from the compounds in our standards. Manual analysis prevents other complexities from arising since ionization from the NIST database is different from that of our mass spectrometer.

2.3.3. Temperature Protocol

Our instrument method through Thermo Xcalibur is CT splitless 300°C column, 20 min hold, with a 250°C source. A splitless injection allows for the entire injection volume to go to the column. Splitless normally allows for better detection since everything injected will be analyzed. Splitless also prevents normal material buildup on the injector from getting onto the column. The temperature of the injection area was set at 300°C. Although, we expected to have some high molecular weight compounds in our samples, a temperature this high allows for greater flash evaporation and volatilization. These species would then be easily transferred to the column. 80°C was set as the starting temperature of the column.

This cooler temperature of 80°C allows the instrument to focus on the analytes.

The 250°C of the source is the temperature of the ionization region (the mass- spectrometer portion). The filament is not turned on until after 4 minutes to protect our instrument. We also set the instrument to scan from 50 amu to 500 amu. The 50-500 amu

15 range encompasses the majority of the compounds we have in our standards. As stated before, we are particularly measuring a mass-to-charge ratio (m/z), with the charge equal to +1.

2.4. Experiments

To identify compounds in our samples as well as to create calibration curves, we used six environmental standards encompassing 60 compounds. Varying curve concentrations were used based on compound concentrations we would expect to see in our sample extractions. We further wanted to obtain Limits of Detection (LOD) and Limits of Quantification (LOQ) to obtain concentrations and to determine which compounds are of significance to household activities. But first, to obtain quantity of compounds in our samples, we set the GC-MS to inject 1.5 µL of every sample, standard, or blank that we ran through the instrument. It is recommended to inject the maximum amount of solution that your GC-MS injector can handle to obtain optimum detection. 1.5 µL was the largest volume that our GC-MS injector could handle without flooding.

2.4.1. Standards

To identify compounds of interest, we obtained six environmental standards and two internal standards to help us detect potential compounds in our samples. We are using standard solutions for n-alkanes, cholesterol, cholestane, nicotine, phthalates, and PAHs.

This is not an exhaustive list of compounds that might be present in the samples, but these are some that we expected to see from these indoor dust samples (see Chapter 1). Our two internal standards are the deuterated PAHs which were mentioned in the Section 2.2. Since we had specific compounds in our standards we wanted to look for in our samples, we

16 began the process by finding and documenting the top thee ions, retention times and area counts.

2.4.2. Compound Detection and Integration After doing some preliminary runs for calibration curves, we documented ion abundance and retention times. The more concentrated standard solution from the initial calibration curves were used to identify these compound characteristics. Analyzing a greater concentration allows for easier compound detection and more defined properties.

The most abundant ion (the ion with a relative abundance of 100%) was used to quantify each compound. During integration, compound peaks were drawn down to the baseline of all peaks to obtain full area counts. Peak integrations were done manually using the

Xcalibur software.

Figure 3. Best Examples of a Compound Peak.

Figure 3 gives us two examples of some very nice peak shapes. We would have welcomed peaks like this for all our compounds. Many compounds had well-defined peaks especially those of higher curve concentrations. The quality of the peaks dropped as concentration dropped. The bottom of the peaks here is considered the baseline. On top of the peaks, RT is the retention time. Here we have 22.37 and 25.76 minutes. The AA is the area count of the whole peak and gives us an indication of the compound quantity.

Figure 4. Limited Signal and Noise in GC-MS Results. The schematic in Figure 4 do not represent a peak. This is mostly what we would call “noise”. There is not much here that can be accurately identified and integrated. When a view like this occurs in the chromatogram, we usually leave it and move on to the next compound. While analyzing the actual samples, legends and notes were created to record interferences, complexities, and other important observations.

Table 1. Legends and Notes Added During Analysis of the Compounds.

*Every compound without a legend is ok for the most part. *All work is done manually *Assume average of peak is taken with two spectra subtractions

A Falls outside of curve detection limit

Foreign ions present or even dominant in addition to main B (20% or greater)

Low-detection or non-detection of top 3 ions (less than C 50%), or overabundance of foreign ions

Category C, but ratio of top three ions are ok CR

Main ions/main peak found at least 0.05 minutes away then D what was listed originally

Split peak (two or more peaks with main ions near the E expected retention time).

F Compound integration done over two or more peaks.

Table 1 shows the keys and corresponding notes added needed during the analysis.

Seven legends are used to categorize nuisances and issues encountered with ions, peak integration, and detection. Documenting notes helps us to quickly identify and resolve issues as needed later on. The notes will also be useful for future researchers who extend this work.

2.4.3. Calibration Curves Once we obtained area counts for all the compounds in the standards and samples, we began work on the calibration curves. A calibration curve is used to estimate an 19 unknown compound concentration in a sample based on the known concentration in standards. The calibration curves can be perfectly or almost linear. The equation of a line y=mx+b is derived from the created curve. R-squared values, slope (m), and y-intercepts

(b) are all documented as part of our analysis.

We experimented with several sets of standards before finalizing our calibration curves. We encountered errors in area count, detection, volatility, and measurement which we would work to resolve. We initially prepared standards for a 9-point calibration curve via serial dilution. In a serial dilution, each consequential concentration is a product of the previous one. Our concentrations ranged from 20 µg/mL to .0195 µg/mL. Dilutions were done at ½ step and ¼ step intervals. All concentrations for this initial 9-point curve were

20, 10, 2.5, 1.25, .625, .3125, .15625, .078125, and .0195 in units of µg/mL. These are the initial standards which are used to obtain retention times, ion properties, and early outlooks of potential sample concentrations.

2.5. Difficulties Encountered during Preparations and Resolutions We encountered several issues with our first standard preparations. One issue is that it was very difficult to detect the cholesterol even in the higher calibration concentrations. We believed this may have been due to ineffective transfer of the analytes with the plastic pipettes. After a brief discussion on what could have caused these issues, we reasoned that plastic pipettes would not be very effective at handling organic analytes.

Sanders (2012) stated that glass is a requirement when dealing with organics. We later learned that plastic pipettes could introduce errors because of evaporation losses when dealing with volatile compounds (Thermo Fisher Scientific, 2010). As a result, we decided to use glass syringes instead of plastic pipettes for all transfers throughout this work. The 20 glass syringes were cleaned with the same Hexane stock solution throughout our experiments.

We concluded that it would be best to allow all solutions (stock solutions, dilution series, samples or blanks) to warm to room temperature for at least 1 hour before initiating any process. Allowing the solutions to warm to room temperature allowed the solutions to de-coagulate as well as increase in volatility. We also concluded that sonication will be a necessary step to make sure solutions are well agitated before initiating any process. We would sonicate all solutions for 10 seconds. Further, the previous 9-point calibration had excluded the two deuterated PAHs-the Acenaphthene-d10 and Pyrene-d10-which was spiked into extractions-so they will be added to all consequent experiments.

2.5.1. Quality Assurance

In this work, blanks were prepared the same way as samples. The same solute of

3:1 Hexane: Isopropanol was used. We sonicated and centrifuged our blanks at the same rate as we did regular samples. The same methods and criteria used to analyze and evaluate the samples were the same used for the blanks.

In addition to the standard we created for the 9-point calibration curve, we had also ran swab blanks and a few samples for basic analysis and discussion to see how to best move forward. We evaluated factors such as purity of the Hexane used as well as peaks and trends of the peaks found in the solutions. Completing these primary analyses allowed us to have a better idea of what to look for and how to best move forward

2.6. Adjustments from Resulting Concurrences After our prior experiments with the 9-point calibration curve, we made a few adjustments to our next set of preparations. This time we used a larger 5 mL flask to make 21 dilutions a bit easier. We further wanted all compounds in our standards to have an initial concentration of 10 µg/mL. Unlike during our first experiment where we created a calibration curve with 9-points, ranging from .019520 µg/mL to 20 µg/mL [19.52 ng/mL to 20,000 ng/mL]. This set of calibration curves only has seven points. The curve concentrations range from 10 ng/mL to 5000 ng/ml. The entire range include concentrations of 10, 60, 100, 300, 600, 1000, and 5000 in units of ng/mL. We are using these values because we believe that compound concentration in our samples will be in that range.

2.7. Separating and Running the Samples It was not ideal to run all 51 samples at once due to the high amount of organic matter in our samples. We would have had issues with sensitivity and detection as material accumulates on the column and injector. To lessen this load on our instrument we decided to separate everything into four runs. In this case, we prepared four sets of standards and four blanks. Each set of standards and each blank was ran with twelve to thirteen samples.

2.8. Analyzing and Interpreting the Data Next, it was time to perform the chemical analysis. The first step to the analysis was identification of each compound as well as representative compound retention times for each batch. We used the four 5,000 ng/mL calibration points to obtain retention times for each batch since clear detection of all compounds was expected at this higher concentration. Using the top three ions of each compound, area counts were obtained for compounds in the standards, samples, and blanks. Data statistics including maximum, minimum, mean, and median values are provided in section 3.2. We struggled with the best

22 way to obtain reliable compound concentration from our samples. We will resume that portion of the work in the future.

2.9 Limits of Detection (LOD) and Limits of Quantification (LOQ) We used both the Limit of Detection (LOD) and Limit of Quantification (LOQ) as parameters in our analysis. Both terms describe the smallest concentration measurement that can be made in an analysis (Armbruster and Pry, 2008). In this work, we use the area counts as the measurement. Both LOD and LOQ will be calculated and inputted into our statistical analysis software (SAS) to help us determine compounds which are significant to the activities that may induce pollution in the indoor environment. LOD and LOQ will also be useful for when we will calculate compound concentrations from our calibration curves. No nicotine appeared in our blanks during analysis, but we nonetheless saw nicotine as an important compound to evaluate as we further our work. Thus, estimated blank values were derived for nicotine using the minimum nicotine area count along with the ten lowest area count values.

2.9.1. Limit of Detection (LOD) LOD in this case gives us an indication of the lowest concentration we can detect from each known compound in our sample. Detection of this compound does not mean it is quantifiable. LOD will be especially useful in this case since the potential exists that some of the compounds in our dust will also be in our blanks. Under this situation, a false detection of an analytes would have resulted. The equation for LOD is as given:

LOD=Xbl+3Sbl (Equation 1)

Where Xbl is the average of the blank concentrations for the analyte; Sbl is the standard deviation of the blank concentration for the analyte (Shrivastava and Gupta, 2011). For

23 nicotine, we used the equation above, but given that we could not directly obtain estimates from our blanks, we said that our Xbl was the minimum nicotine area count value amongst the 51 samples, while our Sbl was the average of the ten lowest area count values from the

51 samples.

2.9.2. Limit of Quantification (LOQ) LOQ gives us an indication of the lowest concentration of an analyte that we can determine with precision and accuracy (Shrivastava and Gupta, 2011). LOQ is generally greater than or equal to LOD. It is never lower than LOD (Armbruster and Pry, 2008).

LOQ=Xbl+10Sbl (Equation 2)

Xbl the average of the blank concentrations for the analyte; Sbl is the standard deviation of the blank concentration for the analyte (Shrivastava and Gupta, 2011). Similarly to the

LOD, for nicotine, we use the same equation above for LOQ, but we said that our Xbl was the minimum nicotine area counts amongst the 51 samples, while our Sbl was the average of the 10 lowest area counts. More details on our usage of LOQ will be discussed further in the results and discussion section below.

2.10. Participant Surveys Since this work will be used to assess exposure, the 51 participants were also asked to answer a few pertinent survey questions that can help us combine all steps of our experiments. The survey was broken into four major sections: Basic demographic information, symptoms and asthma, clinical history, and environmental exposure and modification. For the purposes of this work, we are concerned with questions that deal with environmental exposure. These questions encompass everything from cigarette smoking, vehicular fuels, appliance usage, cooking habits, and chemicals.

Table 2. Activities Evaluated to Find which Compounds Were Significant (Castner et al., 2018).

Activity Variables Yes No 1 Have you ever smoked cigarettes? 25 26

2 Do you smoke cigarettes now? 17 34

3 Have you or someone smoked inside of your home in the past 21 30 week? 4 Was there any diesel vehicle parked around your home in the 6 45 last 48 hours? 5 Did you use a gas lawn mower in last 48 hours? 7 44

6 Have you or someone grilled indoors in the last 48 hours? 21 30

7 Is gas used for cooking? 35 16

8 Were you exposed to chemicals in the last 48 hours? 14 37

9 Have you had a major home renovation in the past year? 9 42

10 Have you been exposed to or have you used chemical solvents in 7 44 the last 48 hours? 11 Have you dealt with or been exposed to cleaning solutions in the 41 10 last 48 hours?

Table 2 has a list of activities that occur inside or around the home. We chose to include these activities for three reasons.

1. They are relevant to the compounds we have in our standards that we’ve identified

in the samples.

2. They have a higher potential to create exposures inside of the home when compared

to other activities.

3. The number of participants (out of 51) who responded “Yes” to performing these

activities or having been exposed to these materials were also considered.

Activities 1-3 are related to smoking cigarettes. Here nicotine is a compound of interest.

Activities 4-7 are related to gas, diesel, and other fuels. Here alkanes, 5-alpha-cholestane,

25 and PAHs are compounds of interest. Activities 6-7 also have cholesterol as a compound of interest. Activities 8-11 include cleaning, solvents, plasticizers and chemical gases.

Here, we have phthalates as compounds of interests.

2.11. Statistical Analysis

We used the Statistical Analysis Software (SAS), version 9.3 (SAS Institute., Inc.,

Cary, NC, USA) to determine which of our compounds are significant for the residential activities. As stated in section 2.10 above, many activities had potential impacts on indoor aerosol concentration. Many of these activities were common recurrent activities performed in or around the home of our participants. Many of our 51 participants reported to have had these activities to occur within or around their home 48 hours before dust samples were collected.

We tested for which of our compounds were significant in which activity.

Significance is determined when the p-value or q-value is less than 0.05. The p-value is used for our a priori hypothesis for activities 1-3 and 6 from Table 2. We assumed significance based on which compounds serve as tracers for common activities as discussed in section 1.3.1. In activities 1-3, we expected that only nicotine would be significant, since nicotine is the common tracer for cigarette smoking. In activity 6, we expected that cholesterol would be significant since cholesterol is the common tracer for meat cooking, and when people grill, it is usually meat. To facilitate these measurements and find significance across multiple variables and compounds, the q-value is used. Activities 4-5 and activities 7-11 can have several compounds as significant. For these activities, we used the q-value to facilitate for the multiple comparisons. The SAS MULTTEST procedure

26 was used with the false discovery rates (FDR), particularly with the pFDR option, to determine q-values (Storey et al., 2003).

Chapter 3: Results and Discussions To evaluate which compounds we have in our indoor dust, we used the six environmental standards to obtain retention times, top three ions, and area counts during our GC-MS analysis. We used the same retention times and top three ions when looking for these compounds in our actual dust samples and blanks. The area counts allowed us to get a sense of how much of the compound existed. From the area counts, we derived calibration curves and calculated the LOD and LOQ. We had hoped to obtain compound concentration from our calibration curves in this preliminary stage of our work, but we will have to obtain them later in the future since many factors prevented us from getting clearer estimates of this measurement. The LOD and LOQ values were inputted to SAS and combined with results from the participant surveys to obtain which of our compounds are significant for activities which could have contributed to the indoor dust. This chapter will provide the results, usage, and discussion of our GC-MS analysis.

3.1. Calibration Curves The primary usage of calibration curves is to obtain sample concentrations, but we will not be able to do so here. After evaluating some of our results, we are not certain that current methods would be enough to provide accurate concentration results, especially with the small amount of dust collected on the swabs. We need to explore more into our LOD,

LOQ, as well as response factors from our internal standards. More methods of obtaining corrected concentrations would also have to be evaluated by the blanks. Nicotine, however was the one compound which we currently feel confident in quantifying. We feel confident in quantifying nicotine because its area counts were above the LOQ.

The calibration curves were derived using Matlab with curve concentrations in the x-axis and area counts in the y-axis. Curve slopes, y-intercepts, and R-squared values were all documented for later use if needed.

Figure 5. Compound: Nicotine: Linear Calibration Curve Ranging from 10 to 5,000 ng/mL.

Figure 5 is a calibration curve for the compound nicotine which was created using area counts and concentrations from our standards. The curve equation is

y = 273x-16010. The R-squared is .99 which indicates that the line was a pretty good fit for the given points. Next, we wanted to optimize and tighten our curves appropriately.

We documented compound area counts from our standards at 5,000 ng/mL and compared them to maximum compound area counts from our samples. After evaluating area counts and previous work, we decided it would be best start at the 1000 ng/mL calibration curve.

Afterwards, we saw that we could reduce the upper limit of the concentration range even further to the 600 ng/mL.

Figure 6. Compound: Nicotine-Linear Calibration Curve Ranging from 10 at 600 ng/mL.

After reducing our range in calibration concentration points, new curves were derived. This new range and curves are more appropriate and compatible with our sample area counts. Although the fit line for nicotine in Figure 6 may not look very compatible with those points, the resulting R-squared is still .99, which means that the fit is still almost perfect. It should be noted that the graph in Figure 6 is also more focused and zoomed in than it is in Figure 5. The equation is y = 229x-5649. After reducing the upper limit of the calibration curve to 600 ng/mL, we observe a noticeable drop in the y-intercept from -16010 to -5649. This difference of around 11,000 could have a significant impact on concentration since collected dust levels in the swabs were not abundant.

3.2. Integration Results from the 51 Samples During analysis of the 51 samples, we determined whether our compounds of interest could be found in the sample as well as estimated of how much of that compound was found in that sample. As mentioned in section 2.43, the GC-MS allows us to integrate peaks and obtain area counts of those peaks. The integration area count is the instrument’s way of quantifying the compound. Table 3, Table 4 and Table 5 below contain summary statistics of our integrations. The minimum, maximum, mean, and median area count values of each compound which was identified in the 51 samples are provided. The right most column (Blank Mean) contains the average area counts of the compounds which we were able to identify from our four blanks. This column is added here for quicker comparison between the samples and the blanks.

Table 3. Summary Statistics of Compound Area Counts from Samples: N-alkanes.

Compound Integrable Sample Sample Sample Sample Blank Min Max Mean Median Mean Heptane n/d n/d n/d n/d n/d n/d Octane n/d n/d n/d n/d n/d n/d Nonane n/d n/d n/d n/d n/d n/d Decane n/d n/d n/d n/d n/d n/d Undecane 51 1,356 4,366 2,786 2,817 1,899 Dodecane 51 16,627 78,969 41,089 35,574 43,998 Tridecane 51 2,964 27,918 8,359 6,878 7,936 Tetradecane 51 10,890 54,311 26,117 23,406 34,165 Pentadecane 49 2,633 18,678 5,801 4,946 4,816 Hexadecane 51 2,494 33,383 7,102 5,575 15,107 Heptadecane 51 1,495 25,677 8,306 7,343 6,999 Octadecane 51 3,107 26,468 8,367 7,253 12,875 Nonadecane 51 925 9,556 4,118 3,730 5,034 Eicosane 50 1,659 22,361 9,873 8,775 6,848 Heneicosane 51 1,942 41,914 11,242 9,530 4,353 Docosane 51 7,072 59,928 25,438 23,400 11,517 Tricosane 51 7,793 102,842 30,328 25,807 16,462 Tetracosane 51 11,088 132,287 45,349 40,833 27,906 Pentacosane 51 6,535 138,806 41,371 33,009 24,633 Hexacosane 51 12,908 119,330 47,836 42,702 30,426 Heptacosane 51 8,242 146,512 48,622 37,577 34,821 Octacosane 51 8,804 111,927 49,466 41,987 38,429 Nonacosane 51 11,467 124,501 52,823 43,326 45,241 Triacontane 51 8,401 109,819 47,112 41,572 47,696 Hentriacontane 51 7,396 117,235 49,472 40,004 53,512 Dotriacontane 51 8,024 93,369 35,470 28,873 47,670 Tritriacontane 51 2,822 57,981 25,531 21,225 37,819 Tetratriacontane 51 1,021 36,959 16,102 13,829 30,922 Pentatriacontane 51 423 28,998 8,414 5,876 21,294 Hexatriacontane 50 310 11,765 3,693 2,930 16,208 Heptatriacontane n/d n/d n/d n/d n/d n/d Octatriacontane n/d n/d n/d n/d n/d n/d Nonatriacontane n/d n/d n/d n/d n/d n/d Tetracontane n/d n/d n/d n/d n/d n/d 32

Table 3 contains summary statistics for the n-alkanes we identified and measured in our 51 samples. The first column shows how many of the samples had integrable peaks for that compound. Whether the peaks are integrable or not was based on the criteria listed in Table 1 in section 2.42. Most cells with “n/d” are compounds which we could not integrate for several reasons: one being that they eluted before our instrument start time

(Heptane, Octane, Nonane, Decane), or they were out of our programming range which only considered compounds with weights in the range of 50-500 amu (Heptatriacontane,

Octatriacontane, Nonatriacontane, Tetracontane).

If we focus in on the sample maximum and sample means of the n-alkanes

(Columns 3 & 4), we see how area counts varied going down the columns. Some n-alkanes were more present in certain samples then others, which caused a huge discrepancy across the maximum values. We note Pentacosane had a maximum area count of 138,806 which occurred in sample 1542, while Hexadecane only had an overall maximum area count of

33,383.

Table 4. Closer Observation of Area Count Statistics for Pentacosane.

Compound Sample Min Sample Max Sample Mean Sample Median Pentacosane 6,535 138,806 41,371 33,009

In Table 4, we further observe min, max, mean, and median area count statistics for

Pentacosane. A maximum area count value of 138,806 occurred in sample 1590, while a minimum value of 6,535 occurred in sample 36. The mean area count across all samples where Pentacosane could be integrated was 41,371, while the median was only 33,009.

These results give us an indication how pollutants can vary from house to house as residential activities and characteristics change.

Table 5. Summary Statistics of Compound Area Counts from Samples: Cholesterol, 5-Aplha-Cholestane, Nicotine, Phthalates, PAHs.

Compound Integrable Sample Sample Sample Sample Blank Min Max Mean Median Mean Cholesterol 51 199 16,105 2,887 2,487 1,855 5-Alpha-Cholestane 33 432 4,106 1,619 1,313 2,834 Nicotine 45 290 698,561 38,671 4,405 n/d Dimethyl Phthalate 41 113 2,486 419 333 613 Diethyl Phthalate 50 1,260 38,039 5,308 3,908 4,184 Di-N-Butyl Phthalate 51 4,500 101,130 27,268 21,168 99,382 Benzyl Butyl Phthalate 51 524 92,207 12,880 3,974 2,491 Bis (2-Ethylhexyl) 50 8,712 1,507,228 129,917 43,678 16,497 Phthalate Di-N-Octyl Phthalate 35 669 40,492 7,496 4,054 4,295 Naphthalene 51 340 2,054 873 721 845 Acenaphthylene 7 86 1,040 328 242 215 Acenaphthene 16 134 751 293 241 384 Fluorene 36 91 659 300 306 502 Phenanthrene 36 282 4,053 1,325 1,127 1,630 Anthracene 41 72 1,181 590 571 730 Fluoranthene 25 511 11,647 2,510 1,962 2,076 Pyrene 14 369 12,236 3,073 1,775 281 Benz[a]anthracene 29 101 1,262 647 595 546 Chrysene 33 226 3,413 1,001 821 483 Benzo[b]fluoranthene 22 254 2,177 1,138 1,044 537 Benzo[k]fluoranthene 25 552 8,425 3,109 2,615 2,650 Benzo[a]pyrene 35 310 3,572 1,408 1,163 970 Dibenz[a,h]anthracene 9 193 1,479 610 408 192 Benzo[ghi]perylene 3 173 1,440 528 249 n/d

Table 5 has the remaining results from compounds we were able to integrate from our 51 samples. The table provide summary statistics for cholesterol, 5-Aplha-Cholestane, nicotine, phthalates, and PAHs. Cholesterol could be integrated in all 51 samples which

34 makes sense as all residential units would have some sort of food or meat material from indoor cooking or from the cooking of nearby homes. Nicotine could be integrated in 45 of 51 samples. A maximum nicotine area count of 698,561 was observed in sample 168.

This is most likely a home where the participant reported to have smoked regularly at the time of the survey. The statistics for 5-alpha-cholestane, PAHs, and phthalates are also summarized on Table 5.

3.3. Blanks We analyzed our blanks and found that they contained just about all compounds from our standards. As mentioned earlier, nicotine was the only compound that was not found from the blank analysis, but we used equations to estimate both LOD and LOQ nicotine values and obtained values of approximately 500 (LOD) and 1500 (LOQ).

Unfortunately, some compound area counts from the blanks rivaled and even surpassed a few compound area counts from the samples. We believe that the compound contribution from the blanks are directly from the cotton swab. The wood, the cotton, and the glue used to hold the swab most had several of our compounds and elevated compound area counts.

This led us to question how much of these compounds the dust truly contributed to when compared to the blanks.

With values in Table 3 and Table 5 above, we are able to compare area counts from compounds integrated in our 51 samples and those integrated in our blanks. The compound area counts did not vary significantly across the blanks, thus the average blank area counts are representative of the four blanks. For each compound, we compared the sample means and the blank means. We observed that some compounds had higher area count values in 35 our blanks than in our samples. For example, in Table 3, we note that Hexadecane had a sample mean area count of 7,102 and a blank mean of 15,107. In Table 5, we note that Di-

N-Butyl Phthalate has a sample mean of 27,268 and a blank mean of 99,382. This type of observation was made in a couple more compounds. We do not fully understand what is contributing to this huge discrepancy. We had assumed that compound area counts in our samples would have been higher than those in the blanks.

3.4. Limits of Detection (LOD and Limits of Quantification (LOQ) Table 6 and Table 7 provide analysis results from our four blanks. The mean and standard deviations were used to calculate LOS and LOQ using Equation 1 and Equation

2. Next, we generated a Matlab code so that a value of “1” would appear where the compound area counts were greater than the calculated LOD. Otherwise a value of “0” would appear. Another code was generated for LOQ. Here, a value of “0” appeared where the area counts were less than the calculated LOQ. If not, the actual “area count” appears.

As mentioned previously, we only needed the LOQ for nicotine. Thus, we created a

Comma Separated Value (CSV) file with LOD results for all compounds and LOQ for nicotine.

Table 6. Area Counts of N-alkanes from Blanks; LOD & LOQ.

Compounds Blank Standard LOD LOQ Mean Deviation Heptane n/d n/d n/d n/d

Octane n/d n/d n/d n/d

Nonane n/d n/d n/d n/d

Decane n/d n/d n/d n/d

Undecane 1,899 649 3,845 8,386

Dodecane 43,998 5,912 61,734 103,118

Tridecane 7,936 1,185 11,491 19,787

Tetradecane 34,165 5,483 50,615 88,997

Pentadecane 4,816 259 5,592 7,404

Hexadecane 15,107 4,067 27,308 55,778

Heptadecane 6,999 1,911 12,731 26,105

Octadecane 12,875 1,628 17,760 29,159

Nonadecane 5,034 2,367 12,136 28,707

Eicosane 6,848 1,411 11,080 20,955

Heneicosane 4,353 763 6,642 11,984

Docosane 11,517 1,651 16,469 28,023

Tricosane 16,462 1,284 20,314 29,302

Tetracosane 27,906 6,377 47,036 91,672

Pentacosane 24,633 3,072 33,849 55,355

Hexacosane 30,426 5,808 47,851 88,509

Heptacosane 34,821 7,069 56,027 105,507

Octacosane 38,429 7,942 62,255 117,849

Nonacosane 45,241 8,475 70,668 129,996

Triacontane 47,696 9,267 75,497 140,366

Hentriacontane 53,512 9,396 81,701 147,476

Dotriacontane 47,670 7,862 71,257 126,293

Tritriacontane 37,819 4,366 50,917 81,479

Tetratriacontane 30,922 2,117 37,274 52,097

Pentatriacontane 21,294 2,918 30,048 50,473

Hexatriacontane 16,208 3,749 27,455 53,696

Heptatriacontane n/d n/d n/d n/d

Octatriacontane n/d n/d n/d n/d

Nonatriacontane n/d n/d n/d n/d

Tetracontane n/d n/d n/d n/d

Table 7. Cholesterol, 5-Aplha-Cholestane, Nicotine, Phthalates, PAHs Area Counts from Blanks; LOD & LOQ.

Compound Blank Standard LOD LOQ Mean Deviation Cholesterol 1,855 581 3,598 7,667 5-Alpha-Cholestane 2,834 1,364 6,925 16,469 Nicotine n/d n/d 500 1,500 Dimethyl Phthalate 613 156 1,080 2,169 Diethyl Phthalate 4,184 1,835 9,688 22,530 Di-N-Butyl Phthalate 99,382 44,400 232,580 543,377 Benzyl Butyl Phthalate 2,491 1,460 6,870 17,090 Bis (2-Ethylhexyl) Phthalate 16,497 4,001 28,501 56,511 Di-N-Octyl Phthalate 4,295 2,753 12,555 31,828 Naphthalene 845 255 1,609 3,393 Acenaphthylene 215 57 386 785 Acenaphthene 384 65 579 1,033 Fluorene 502 301 1,404 3,510 Phenanthrene 1,630 155 2,095 3,179 Anthracene 730 168 1,233 2,407 Fluoranthene 2,076 1,703 7,186 19,108 Pyrene 281 155 745 1,829

Benz[a]anthracene 546 406 1,764 4,605 Chrysene 483 121 847 1,696 Benzo[b]fluoranthene 537 267 1,336 3,203 Benzo[k]fluoranthene 2,650 389 3,817 6,540 Benzo[a]pyrene 970 192 1,546 2,892 Dibenz[a,h]anthracene 192 34 294 531 Benzo[ghi]perylene n/d n/d n/d n/d

3.5. Statistical Analysis

We used the CSV file along with activity variables from the participant surveys to test for compound significance. In the following sections, we will discuss the compounds that are significant for our tested activities. Before running the tests, we made some basic assumptions about what compounds will be significant for each activity. Our assumptions were linked to the tracer compounds we found for the various activities and materials in section 1.3.1. Nicotine is often used as a tracer for cigarette smoking. Cholesterol is often used as a tracer for meat cooking. N-Alkanes, 5-Alpa-Cholestane, and PAHs are often used as tracers for fuel emissions. Phthalates can be used as a tracer for plasticizers, PVC, and the chemicals in personal care products, cleaning products, and chemical sprays.

Table 8. Activities, Potentially Significant Compounds, and p-values and q-values (All numbers are q-values unless noted as p-value).

Activity Potentially Significant p-value, Compounds q-value 1 Have you ever smoked cigarettes? Nicotine 0.0015: p-value

2 Do you smoke cigarettes now? Nicotine 0.004: p-value 3 Have you/someone smoked inside Nicotine <0.0001: of your home in the past week? p-value 4 Was there any diesel vehicle N-Alkanes, 5-Alpha- All 1.0 parked around your home in the Cholestane, and PAHs last 48 hours? 5 Did you use a gas lawn mower in N-Alkanes, 5-Alpha- All ~1.0 last 48 hours? Cholestane, and PAHs 6 Have you or someone grilled Cholesterol 0.937 indoors in the last 48 hours? p-value 7 Is gas used for cooking? Cholesterol; N-Alkanes, Range 5-Alpha-Cholestane, and PAHs [0.4, 0.9] 8 Were you exposed to chemicals in Phthalates ~1.0 the last 48 hours? 9 Have you had a major home Phthalates ~1.0 renovation in the past year? Fluoranthene 0.071 10 Have you been exposed to or have Phthalates ~1.0 you used chemical solvents in the last 48 hours? 11 Have you dealt with or been Phthalates All 1.0 exposed to cleaning solutions in the last 48 hours?

Table 8 is provides results for the activities we tested. Entire summary tables with all compounds are provided in Appendix A [Table A1-Table A12]. As mentioned in section 2.11, we assumed significance to be a p-value and a q–value less than 0.05. The p-value is evaluated with activities with our a priori hypothesis where we were confident to see one compound as significant for the activity. The q-value is a p-value that is adjusted for multiple comparisons and is evaluated in activities where multiple

40 comparisons would be required across compounds. The following sections will provide the results we had expected, actual results from Table 8, and how we view these results.

We will also discuss these results with information from Table 2 from Section 2.10.

3.5.1. Activities which We Evaluated for Associations with Nicotine

For activity 1 (Have you ever smoked cigarettes?), we had an a priori hypothesis that nicotine would be the only compound of significance. Our hypothesis was proven true since nicotine had a p-value of 0.0015. For this activity, 25 of 51 participants answered

“Yes”, they have smoked a cigarette. This is a general question of whether they have smoked cigarettes and not specified whether this smoking was done in the home, outside the home, nor how recent.

For activity 2 (Do you smoke cigarettes now?), we again had an a priori hypothesis that nicotine would be the only compound of significance. Nicotine was found to be a significant compound for activity 2 with a p-value of 0.004. Further 17 of 51 participants had answered “Yes”, they currently smoke cigarettes. Other data we received showed that survey participants smoked at varying rates.

Activity 3 reads: “In the past week, has anyone smoked inside your home?” 21 of

51 responded “Yes”. This question is a bit more specific since we know how recent and where the activity took place. For this activity our priori hypothesis was that nicotine would be the only compound of significance. The test results have a nicotine p-value of less than

0.0001 which confirms our initial hypothesis here.

3.5.2. Activities which We Evaluated for Associations with N-Alkanes, 5-Alpha- Cholestane, and PAHs N-alkanes, 5-alpha-cholestane, and PAHs are the three groups of compounds that could be significant for activity 4. To facilitate for the multiple comparisons, we will also be evaluating the q-value. The question is: “Was there any diesel vehicle parked around your home in the last 48 hours?” 6 of the 51 participants responded “Yes”. Although this was a low number of “Yes” responses, this was nonetheless an important activity to evaluate due to the potential impact diesel particulates can have on those suffering from respiratory illnesses. As far as results, we did not have any of the n-alkanes, PAHs, nor 5- alpha-cholestane show up as significant since q-values hovered around 1.0 for each compound.

For activity 5 (“Did you use a gas lawn mower in last 48 hours?”), compounds from our list of n-alkanes, 5-alpha-cholestane, and PAHs could all be significant since they can be used as tracers for different types of fuels. 7 out of 51 participants reported exposure to a gasoline lawn mower. It is important to note that lawn mower smoke can easily find its way in the home through opened windows, doors, or garages. It can be assumed that PM produced by lawn mower smoke will be in greater quantities in the summer as well. We evaluated the q-value and ultimately, we found none of our compounds to be significant in the statistical test with a q-value around 1.0.

3.5.3. Activities which We Evaluated for Associations with Cholesterol

Here we looked at activity 6 (Have you or someone grilled indoors in the last 48 hours). In general, 21 of the 51 participants reported exposure to grilling while inside. For grilling and other cooking variables in this project, we expect cholesterol to be the only 42 significant compound. Individuals usually grill meat whenever they grill, and cholesterol is the common tracer for meat grilling amongst our list of compounds. Therefore cholesterol’s significance was an a priori hypothesis. For this activity, however, after evaluating the p-value (0.937), cholesterol was not a significant compound. Our other compounds were also far from being significant. We are not exactly sure the reason for this, and more exploration will be needed in the future. This could have been attributed to the low dust collected on the swabs. It is also possible that not much cooking emissions accumulated on the bedroom door frame. Cholesterol area counts in our samples were also generally on the low end when compared to other compound area counts. We had trouble detecting the compound even in the higher concentrations of our standard injections, but we are not sure of the exact cause of this.

In activity 7 we evaluated the question “Is gas used for cooking?” 35 of the 51 participants responded with a “Yes”. Although we could again expect n-alkanes, 5-alpha- cholestane, and PAHs to be significant since this variable deals with a fuel source for ignition, we will also expect to see cholesterol since gas is used for cooking. After running the test, none of our compounds were significant. Cholesterol, which was our target compound for this test, had a q-value of 0.4368. All compounds had q-values in the range of [0.4 to 0.9].

3.5.4. Activities which We Evaluated for Associations with Phthalates For activity 8, we evaluated the question: “Were you exposed to chemicals in the last 48 hours?” 14 of 51 participants responded “Yes” to exposure. This variable assesses whether participants have ever been exposed to gaseous and chemical fumes. This exposure

43 may have occurred at home or in the workplace. Specifics were not provided for the gaseous and chemical fumes. We believed that phthalates could be compounds of potential significance for this test. As mentioned in section 1.3.1, individuals can be exposed to phthalates through chemical fumes in fragrance sprays such as perfume, air fresheners, and those used for cleaning. With the uncertainty and with multiple phthalate compounds, we evaluated significance with the q-value. The results showed that none of our compounds were significant for this activity with compound q-values around 1.0. Moving forward, a bit more work would have to be done on making sure the variable is well-defined and more compounds can be introduced to this work.

Next, we looked at activity 9 (“Have you had a major home renovation in the past year?”). 9 of 51 participants answered “Yes” to home renovations. The survey question associated with home renovation included adding a room, taking down a wall, replacing windows, or refinishing floors. For this test, we expect to see phthalates as significant.

Plasticizers used in PVC could have been used during the home renovation. Phthalates could have also been a component of the other renovation material such as tape, paint, and cleaning items. After running the test, none of our phthalate compounds were found to be of significance. One of our PAHs-Fluoranthene-was as close as we got with a q-value of

0.0710. Although Fluoranthene is not significant, its approach to the value of significance makes senses as it can be found in coal tar and petroleum-based asphalt (National Center for Biotechnology Information). Some of these materials may have been used during the home renovations.

Here we evaluated significant compounds for activity 10 (“Have you been exposed to or have you used chemical solvents in the last 48 hours?”). 7 of 51 participants said

“Yes” to solvent exposure. The participants could have been exposed to a wide variety of solvents. And again, phthalates are our compounds of interest as was explained in section

1.3.1. When this many participants respond “Yes” to one of these variables, it is easy to assume that we could find some compounds of significance. However, the results of the statistical test showed that none of our compounds were significant with actual q-values hovering again around 1.0. This lack of significance could have been caused by the lack of precision in the survey questions that was asked. Maybe if homes were specified as exposure location and types of solvents were specified, the test results would have been different.

Next, we looked at activity 11 (“Have you dealt with or been exposed to cleaning solutions in the last 48 hours?”). 41 of 51 participants said “Yes” when it comes to exposure to cleaning solutions. As mentioned in the section 1.3.1, phthalates can serve as tracers for cleaning solutions and cleaning products. However, from this test, none of our compounds were significant. We had again evaluated the q-value to facilitate multiple comparisons and found that q-values hovered around 1.0. Some potential causes is that particulate matter from cleaning solutions were most likely not that abundant in our dust samples, and even less so in our samples with the amount of dust collected.

Chapter 4: Conclusions & Future Work 4.1. Conclusions One of our main findings was that the nicotine was a significant compound for cigarette smoking. This finding was an expected result. Besides this, we must continue to optimize our methods, experiments, and analysis. A secondary effort of this work will be needed to obtain clearer and more reliable results. Everything learned here will contribute to advancing this work to greater heights.

4.2. Future Work Overall, this current work was a preliminary analysis and can still be continued to evaluate new areas of research and new ideas. We were able to identify some issues and resolve them throughout the analysis and group discussions. However, I propose five more areas below to explore that can help us optimize our results in the future as well as to avoid many of the problems that we encountered.

4.2.1. Source Apportionment A source contribution is vital to bring about control methods for particulate matter.

Many recent work have been published delineating details of source apportionment for indoor aerosols. Gertler et al. (2000), in particular, evaluated mobile sources amongst other combustion sources. Researchers have come up with many modeling techniques to estimate relative concentration contributions from different sources. These techniques include the building of both material balance and receptor models. We will most likely apply diagnostic ratios to a modified chemical balance (CMB) approach as given in

Equation 3 (Schauer et al., 1996):

Cik=Cref,i ∑ rijSjk (Equation 3)

Where Cik is the measured concentration of a compound from one of our dust samples (k).

Cref,i is some reference compound paired with compound i; rij is the diagnostic ratio of compound i to that reference compound in source j; Sjk is the relative contribution of source j to sample k.

4.2.2. Collecting More Indoor Dust In the future, we would like to collect larger masses of dust per sample. This work was completed with limited dust from the cotton swabs. Most but not all of our compounds were detected and integrated across the samples. However, we could not properly calculate their concentrations since many uncertainties appeared once we evaluated area counts from our blanks, LOD, and LOQ. It will be more effective to collect a higher mass of dust to proceed this project in the future especially when instrument sensitivity and interference issues also come into play. The more mass we have, the easier it will be to also detect some of the compounds that often show up in very low counts. Cholesterol was one of those compounds, and clearer detection will be made possible from having more dust. Moving forward, it will also be ideal to use some sort of collection vacuum to gather the dust. Using a vacuum will reduce interference issues and confusions of compound quantity we have in our current samples due to the cotton swab. It may also be ideal to consider evaluating different locations in the bedroom to collect the dust.

4.2.3. Improve Level of Details in the Survey Questions This study was part of a larger study. In the future, survey questions could be written specifically for associations with the compounds of interest. Some survey questions had a list of activities or exposure to those activities. It would have been best to classify these activities individually and get the participants to answer more detailed questions. The 47 survey questions will need to be more precise with additional details on location (preferably the home and more specifically in the bedroom). The details will allow us to make proper assessments on compound significance, level of exposure, and other variables we could test for.

4.2.4. Have More Standards/Evaluate More Peaks We had an initial set of standards including the n-alkanes, cholesterol, 5-alpha- cholestane, nicotine, phthalates, and PAHs. In total, these amounted to about 60 compounds, which we identified in our samples. Most of these compounds were detected at different levels. The gas chromatogram of every sample also had chromatogram views with peaks and retention times which we did not at all recognize. These other compounds may or may not be from combustion sources or other phthalates. But they could be a point for exploration as this project proceeds.

4.2.5. Addressing Interferences and Distortions with Current Samples This preliminary work used dust that was collected with a cotton swab. Organic matter from the cotton swab (the wood and the cotton) did show up on our chromatogram views. When we compared chromatogram views from our blanks with that of our samples, we noticed very similar peaks from both. We also had siloxanes and a few integrable compounds (in very low area counts) as the siloxanes appeared in all chromatogram views across the 51 samples. We only analyzed peaks that were part of our compound list. We will need to further evaluate why compound area counts in some of our blanks were sometimes higher than in our samples. Then, we must determine how we should subtract compound contributions from both the hexane and the blanks if we want to further quantify compounds in our samples. 48

We further noticed several interferences from some peaks and ions that were not very defined in a few of our compounds. We believe this could have been due to our instrument. For the future analyses, we have discussed using a different instrument to analyze the indoor dust. Our GC-MS has been in the lab for almost 17 years, so analyzing our future samples on a newer GC-MS and potentially an LC-MS could give us different results.

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Appendix A: Result Tables

Results from Statistical Analyses

Table 9. Activity 1. Have you ever smoked cigarettes?

Estimated Number of True Null Hypotheses P-Value Adjustment Method Estimate Proportion 52 1 Positive FDR Bootstrap

p-Values Variable Contrast Raw FDR Q-Value Nicotine Trend 0.0015 0.0802 Undecane Trend 0.5843 0.98 Dodecane Trend 0.353 0.933 Tridecane Trend 0.353 0.933 Tetradecane Trend 0.5843 0.98 Pentadecane Trend 0.6374 0.9962 Hexadecane Trend 0.978 1 Heptadecane Trend 0.6514 0.9962 Octadecane Trend 0.978 1 Nonadecane Trend 1 1 Eicosane Trend 0.5024 0.933 Heneicosane Trend 0.2219 0.933 Docosane Trend 0.3878 0.933 Tricosane Trend 0.3878 0.933 Tetracosane Trend 0.033 0.8587 Pentacosane Trend 0.498 0.933 Hexacosane Trend 0.698 1 Heptacosane Trend 0.4853 0.933 Octacosane Trend 0.3051 0.933 Nonacosane Trend 0.6939 1 Triacontane Trend 0.4486 0.933 Hentriacontane Trend 0.1138 0.933 Dotriacontane Trend 0.538 0.9647 Tritriacontane Trend 0.1635 0.933 Tetratriacontane Trend 1 1 Pentatriacontane Trend 1 1 Hexatriacontane Trend 1 1 Heptatriacontane Trend 1 1 56

Octatriacontane Trend 1 1 Cholesterol Trend 0.321 0.933 _5_Alpha_Cholestane Trend 1 1 Dimethyl_Phthalate Trend 0.1635 0.933 Diethyl_Phthalate Trend 0.1469 0.933 Di_N_Butyl_Phthalate Trend 1 1 Benzyl_Butyl_Phthalate Trend 0.4948 0.933 Bis_2_Ethylhexyl_Phthalate Trend 0.4853 0.933 Di_N_Octyl_Phthalate Trend 0.2527 0.933 Naphthalene Trend 0.9682 1 Acenaphthylene Trend 0.3126 0.933 Acenaphthene Trend 0.3126 0.933 Fluorene Trend 1 1 Phenanthrene Trend 0.6136 0.9962 Anthracene Trend 1 1 Fluoranthene Trend 0.1635 0.933 Pyrene Trend 0.3051 0.933 Benz_a_anthracene Trend 1 1 Chrysene Trend 0.4157 0.933 Benzo_b_fluoranthene Trend 0.3673 0.933 Benzo_k_fluoranthene Trend 0.9602 1 Benzo_a_pyrene Trend 0.4853 0.933 Dibenz_a_h_anthracene Trend 0.1504 0.933 Benzo_ghi_perylene Trend 1 1

Table 10. Activity 2: Do you smoke cigarettes now?

Estimated Number of True Null Hypotheses P-Value Adjustment Method Estimate Proportion 52 1 Positive FDR Bootstrap

p-Values Variable Contrast Raw FDR Q-Value Nicotine Trend 0.0004 0.022 Undecane Trend 1 1 Dodecane Trend 0.2371 1 Tridecane Trend 0.2371 1 Tetradecane Trend 1 1 Pentadecane Trend 0.5436 1 Hexadecane Trend 0.6184 1 Heptadecane Trend 0.574 1 Octadecane Trend 0.6184 1 Nonadecane Trend 1 1 Eicosane Trend 0.162 1 Heneicosane Trend 0.1678 1 Docosane Trend 0.8246 1 Tricosane Trend 0.3735 1 Tetracosane Trend 0.1054 1 Pentacosane Trend 1 1 Hexacosane Trend 0.8417 1 Heptacosane Trend 0.8287 1 Octacosane Trend 0.6573 1 Nonacosane Trend 0.8246 1 Triacontane Trend 0.6263 1 Hentriacontane Trend 0.2854 1 Dotriacontane Trend 0.2146 1 Tritriacontane Trend 0.3173 1 Tetratriacontane Trend 1 1 Pentatriacontane Trend 1 1 Hexatriacontane Trend 1 1 Heptatriacontane Trend 1 1 Octatriacontane Trend 1 1 Cholesterol Trend 0.5241 1 _5_Alpha_Cholestane Trend 1 1

Dimethyl_Phthalate Trend 0.3173 1 Diethyl_Phthalate Trend 0.3173 1 Di_N_Butyl_Phthalate Trend 1 1 Benzyl_Butyl_Phthalate Trend 0.8351 1 Bis_2_Ethylhexyl_Phthalate Trend 0.3849 1 Di_N_Octyl_Phthalate Trend 0.2586 1 Naphthalene Trend 0.7193 1 Acenaphthylene Trend 0.4851 1 Acenaphthene Trend 0.4851 1 Fluorene Trend 1 1 Phenanthrene Trend 0.5151 1 Anthracene Trend 1 1 Fluoranthene Trend 0.3173 1 Pyrene Trend 0.6573 1 Benz_a_anthracene Trend 1 1 Chrysene Trend 0.5241 1 Benzo_b_fluoranthene Trend 0.3666 1 Benzo_k_fluoranthene Trend 0.3666 1 Benzo_a_pyrene Trend 0.3849 1 Dibenz_a_h_anthracene Trend 0.1901 1 Benzo_ghi_perylene Trend 1 1

Table 11. Activity 3: Have you or someone smoked inside of your home in the past week?

Estimated Number of True Null Hypotheses P-Value Adjustment Method Estimate Proportion 52 1 Positive FDR Bootstrap

p-Values Variable Contrast Raw FDR Q-Value Nicotine Trend <.0001 <.0001 Undecane Trend 0.7813 1 Dodecane Trend 0.7508 1 Tridecane Trend 0.7508 1 Tetradecane Trend 0.7813 1 Pentadecane Trend 0.811 1 Hexadecane Trend 0.8008 1 59

Heptadecane Trend 0.9244 1 Octadecane Trend 0.8008 1 Nonadecane Trend 1 1 Eicosane Trend 0.6636 1 Heneicosane Trend 0.5373 1 Docosane Trend 0.2916 1 Tricosane Trend 0.8222 1 Tetracosane Trend 0.4986 1 Pentacosane Trend 0.6233 1 Hexacosane Trend 0.2926 1 Heptacosane Trend 0.8837 1 Octacosane Trend 0.6801 1 Nonacosane Trend 0.6801 1 Triacontane Trend 0.9345 1 Hentriacontane Trend 0.8224 1 Dotriacontane Trend 0.1407 1 Tritriacontane Trend 0.2357 1 Tetratriacontane Trend 1 1 Pentatriacontane Trend 1 1 Hexatriacontane Trend 1 1 Heptatriacontane Trend 1 1 Octatriacontane Trend 1 1 Cholesterol Trend 0.6156 1 _5_Alpha_Cholestane Trend 1 1 Dimethyl_Phthalate Trend 0.2357 1 Diethyl_Phthalate Trend 0.0878 1 Di_N_Butyl_Phthalate Trend 1 1 Benzyl_Butyl_Phthalate Trend 0.1448 1 Bis_2_Ethylhexyl_Phthalate Trend 0.8837 1 Di_N_Octyl_Phthalate Trend 0.0167 0.4343 Naphthalene Trend 0.7155 1 Acenaphthylene Trend 0.4083 1 Acenaphthene Trend 0.4083 1 Fluorene Trend 1 1 Phenanthrene Trend 0.9562 1 Anthracene Trend 1 1 Fluoranthene Trend 0.2357 1 Pyrene Trend 0.3872 1 Benz_a_anthracene Trend 1 1

Chrysene Trend 0.9144 1 Benzo_b_fluoranthene Trend 0.6481 1 Benzo_k_fluoranthene Trend 0.6481 1 Benzo_a_pyrene Trend 0.4411 1 Dibenz_a_h_anthracene Trend 0.3779 1 Benzo_ghi_perylene Trend 1 1

Table 12. Activity 4: Was there any diesel vehicle parked around your home in the last 48 hours?

Estimated Number of True Null Hypotheses P-Value Adjustment Method Estimate Proportion Positive FDR Bootstrap 52 1

p-Values Variable Contrast Raw FDR Q-Value Nicotine Trend 0.8254 1 Undecane Trend 0.5241 1 Dodecane Trend 0.1783 1 Tridecane Trend 0.1783 1 Tetradecane Trend 0.5241 1 Pentadecane Trend 0.0556 1 Hexadecane Trend 0.6069 1 Heptadecane Trend 0.3078 1 Octadecane Trend 0.6069 1 Nonadecane Trend 1 1 Eicosane Trend 0.2368 1 Heneicosane Trend 0.6805 1 Docosane Trend 0.6061 1 Tricosane Trend 0.6061 1 Tetracosane Trend 0.8365 1 Pentacosane Trend 0.4833 1 Hexacosane Trend 0.8365 1 Heptacosane Trend 0.5381 1 Octacosane Trend 0.6061 1 Nonacosane Trend 0.6061 1 Triacontane Trend 0.8505 1 Hentriacontane Trend 0.9453 1

Dotriacontane Trend 0.2405 1 Tritriacontane Trend 0.6069 1 Tetratriacontane Trend 1 1 Pentatriacontane Trend 1 1 Hexatriacontane Trend 1 1 Heptatriacontane Trend 1 1 Octatriacontane Trend 1 1 Cholesterol Trend 0.4757 1 _5_Alpha_Cholestane Trend 1 1 Dimethyl_Phthalate Trend 0.6069 1 Diethyl_Phthalate Trend 0.6069 1 Di_N_Butyl_Phthalate Trend 1 1 Benzyl_Butyl_Phthalate Trend 0.4187 1 Bis_2_Ethylhexyl_Phthalate Trend 0.5381 1 Di_N_Octyl_Phthalate Trend 0.8279 1 Naphthalene Trend 0.4569 1 Acenaphthylene Trend 0.7189 1 Acenaphthene Trend 0.7189 1 Fluorene Trend 1 1 Phenanthrene Trend 0.4001 1 Anthracene Trend 1 1 Fluoranthene Trend 0.0902 1 Pyrene Trend 0.6061 1 Benz_a_anthracene Trend 1 1 Chrysene Trend 0.8267 1 Benzo_b_fluoranthene Trend 0.6986 1 Benzo_k_fluoranthene Trend 0.6986 1 Benzo_a_pyrene Trend 0.7373 1 Dibenz_a_h_anthracene Trend 0.3707 1 Benzo_ghi_perylene Trend 1 1

Table 13. Activity 5: Did you use a gas lawn mower in last 48 hours?

Estimated Number of True Null Hypotheses P-Value Adjustment Method Estimate Proportion 52 1 Positive FDR Bootstrap

p-Values Variable Contrast Raw FDR Q-Value Nicotine Trend 0.1252 1 Undecane Trend 0.4863 1 Dodecane Trend 0.1407 1 Tridecane Trend 0.1407 1 Tetradecane Trend 0.4863 1 Pentadecane Trend 0.6957 1 Hexadecane Trend 0.574 1 Heptadecane Trend 0.2648 1 Octadecane Trend 0.574 1 Nonadecane Trend 1 1 Eicosane Trend 0.544 1 Heneicosane Trend 0.2018 1 Docosane Trend 0.0392 0.9646 Tricosane Trend 0.0392 0.9646 Tetracosane Trend 0.6174 1 Pentacosane Trend 0.3009 1 Hexacosane Trend 0.6174 1 Heptacosane Trend 0.9444 1 Octacosane Trend 0.8443 1 Nonacosane Trend 0.8443 1 Triacontane Trend 0.5297 1 Hentriacontane Trend 0.9148 1 Dotriacontane Trend 0.3187 1 Tritriacontane Trend 0.574 1 Tetratriacontane Trend 1 1 Pentatriacontane Trend 1 1 Hexatriacontane Trend 1 1 Heptatriacontane Trend 1 1 Octatriacontane Trend 1 1 Cholesterol Trend 0.3543 1 _5_Alpha_Cholestane Trend 1 1

Dimethyl_Phthalate Trend 0.574 1 Diethyl_Phthalate Trend 0.574 1 Di_N_Butyl_Phthalate Trend 1 1 Benzyl_Butyl_Phthalate Trend 0.0556 0.9646 Bis_2_Ethylhexyl_Phthalate Trend 0.3353 1 Di_N_Octyl_Phthalate Trend 0.9639 1 Naphthalene Trend 0.4161 1 Acenaphthylene Trend 0.6942 1 Acenaphthene Trend 0.6942 1 Fluorene Trend 1 1 Phenanthrene Trend 0.3577 1 Anthracene Trend 1 1 Fluoranthene Trend 0.574 1 Pyrene Trend 0.8443 1 Benz_a_anthracene Trend 1 1 Chrysene Trend 0.9591 1 Benzo_b_fluoranthene Trend 0.3078 1 Benzo_k_fluoranthene Trend 0.3078 1 Benzo_a_pyrene Trend 0.3353 1 Dibenz_a_h_anthracene Trend 0.6752 1 Benzo_ghi_perylene Trend 1 1

Table 14. Activity 6: Have you or someone grilled indoors in the last 48 hours?

Estimated Number of True Null Hypotheses P-Value Adjustment Method Estimate Proportion 52 1 Positive FDR Bootstrap

p-Values Variable Contrast Raw FDR Q-Value Nicotine Trend 0.4765 1 Undecane Trend 0.2813 1 Dodecane Trend 0.9884 1 Tridecane Trend 0.9884 1 Tetradecane Trend 0.2813 1 Pentadecane Trend 0.4968 1 Hexadecane Trend 0.3849 1 Heptadecane Trend 0.3353 1

Octadecane Trend 0.3849 1 Nonadecane Trend 1 1 Eicosane Trend 0.7492 1 Heneicosane Trend 0.3484 1 Docosane Trend 0.6895 1 Tricosane Trend 0.2677 1 Tetracosane Trend 0.2556 1 Pentacosane Trend 0.7182 1 Hexacosane Trend 0.6192 1 Heptacosane Trend 0.4261 1 Octacosane Trend 0.7619 1 Nonacosane Trend 0.6895 1 Triacontane Trend 0.8442 1 Hentriacontane Trend 0.8689 1 Dotriacontane Trend 0.2813 1 Tritriacontane Trend 0.3849 1 Tetratriacontane Trend 1 1 Pentatriacontane Trend 1 1 Hexatriacontane Trend 1 1 Heptatriacontane Trend 1 1 Octatriacontane Trend 1 1 Cholesterol Trend 0.937 1 _5_Alpha_Cholestane Trend 1 1 Dimethyl_Phthalate Trend 0.3849 1 Diethyl_Phthalate Trend 0.476 1 Di_N_Butyl_Phthalate Trend 1 1 Benzyl_Butyl_Phthalate Trend 0.6883 1 Bis_2_Ethylhexyl_Phthalate Trend 0.5625 1 Di_N_Octyl_Phthalate Trend 0.9444 1 Naphthalene Trend 0.9111 1 Acenaphthylene Trend 0.5439 1 Acenaphthene Trend 0.5439 1 Fluorene Trend 1 1 Phenanthrene Trend 0.7012 1 Anthracene Trend 1 1 Fluoranthene Trend 0.3849 1 Pyrene Trend 0.3123 1 Benz_a_anthracene Trend 1 1 Chrysene Trend 0.937 1

Benzo_b_fluoranthene Trend 0.7373 1 Benzo_k_fluoranthene Trend 0.5381 1 Benzo_a_pyrene Trend 0.1346 1 Dibenz_a_h_anthracene Trend 0.0892 1 Benzo_ghi_perylene Trend 1 1

Table 15. Activity 7: Is gas used for cooking?

Estimated Number of True Null Hypotheses P-Value Adjustment Method Estimate Proportion 52 1 Positive FDR Bootstrap

p-Values Variable Contrast Raw FDR Q-Value Nicotine Trend 0.71 1 Undecane Trend 0.8184 1 Dodecane Trend 0.1361 1 Tridecane Trend 0.1361 1 Tetradecane Trend 0.8184 1 Pentadecane Trend 0.97 1 Hexadecane Trend 0.476 1 Heptadecane Trend 0.9444 1 Octadecane Trend 0.476 1 Nonadecane Trend 1 1 Eicosane Trend 0.7492 1 Heneicosane Trend 0.0931 1 Docosane Trend 0.6895 1 Tricosane Trend 0.2677 1 Tetracosane Trend 0.2556 1 Pentacosane Trend 0.7182 1 Hexacosane Trend 0.8914 1 Heptacosane Trend 0.9143 1 Octacosane Trend 0.7619 1 Nonacosane Trend 0.7619 1 Triacontane Trend 0.8442 1 Hentriacontane Trend 0.8689 1 Dotriacontane Trend 0.8184 1 Tritriacontane Trend 0.476 1

Tetratriacontane Trend 1 1 Pentatriacontane Trend 1 1 Hexatriacontane Trend 1 1 Heptatriacontane Trend 1 1 Octatriacontane Trend 1 1 Cholesterol Trend 0.4516 1 _5_Alpha_Cholestane Trend 1 1 Dimethyl_Phthalate Trend 0.3849 1 Diethyl_Phthalate Trend 0.0186 0.9682 Di_N_Butyl_Phthalate Trend 1 1 Benzyl_Butyl_Phthalate Trend 0.7959 1 Bis_2_Ethylhexyl_Phthalate Trend 0.9143 1 Di_N_Octyl_Phthalate Trend 0.9444 1 Naphthalene Trend 0.302 1 Acenaphthylene Trend 0.5439 1 Acenaphthene Trend 0.5439 1 Fluorene Trend 1 1 Phenanthrene Trend 0.7012 1 Anthracene Trend 1 1 Fluoranthene Trend 0.476 1 Pyrene Trend 0.3123 1 Benz_a_anthracene Trend 1 1 Chrysene Trend 0.4516 1 Benzo_b_fluoranthene Trend 0.7373 1 Benzo_k_fluoranthene Trend 0.5381 1 Benzo_a_pyrene Trend 0.5625 1 Dibenz_a_h_anthracene Trend 0.5176 1 Benzo_ghi_perylene Trend 1 1

Table 16. Activity 8: Were you exposed to chemicals in the last 48 hours?

Estimated Number of True Null Hypotheses P-Value Adjustment Method Estimate Proportion 52 1 Positive FDR Bootstrap

Dimethyl_Phthalate Trend 0.3849 1 Diethyl_Phthalate Trend 0.0186 0.9682 Di_N_Butyl_Phthalate Trend 1 1 Benzyl_Butyl_Phthalate Trend 0.7959 1 Bis_2_Ethylhexyl_Phthalate Trend 0.9143 1 Di_N_Octyl_Phthalate Trend 0.9444 1 Naphthalene Trend 0.302 1 Acenaphthylene Trend 0.5439 1 Acenaphthene Trend 0.5439 1 Fluorene Trend 1 1 Phenanthrene Trend 0.7012 1 Anthracene Trend 1 1 Fluoranthene Trend 0.476 1 Pyrene Trend 0.3123 1 Benz_a_anthracene Trend 1 1 Chrysene Trend 0.4516 1 Benzo_b_fluoranthene Trend 0.7373 1 Benzo_k_fluoranthene Trend 0.5381 1 Benzo_a_pyrene Trend 0.5625 1 Dibenz_a_h_anthracene Trend 0.5176 1 Benzo_ghi_perylene Trend 1 1

Table 17. Activity 9: Have you had a major home renovation in the past year?

Estimated Number of True Null Hypotheses P-Value Adjustment Method Estimate Proportion 52 Positive FDR Bootstrap 1

p-Values Variable Contrast Raw FDR Q-Value Nicotine Trend 0.1879 1 Undecane Trend 0.4187 1 Dodecane Trend 0.4108 1 Tridecane Trend 0.4108 1 Tetradecane Trend 0.4726 1 Pentadecane Trend 0.8948 1 Hexadecane Trend 0.229 1 Heptadecane Trend 0.8065 1

Octadecane Trend 0.229 1 Nonadecane Trend 1 1 Eicosane Trend 0.6975 1 Heneicosane Trend 0.9209 1 Docosane Trend 0.0546 0.6391 Tricosane Trend 0.8089 1 Tetracosane Trend 0.3136 1 Pentacosane Trend 0.8659 1 Hexacosane Trend 0.7936 1 Heptacosane Trend 0.7054 1 Octacosane Trend 0.8089 1 Nonacosane Trend 0.8089 1 Triacontane Trend 0.8318 1 Hentriacontane Trend 0.6848 1 Dotriacontane Trend 0.4726 1 Tritriacontane Trend 0.229 1 Tetratriacontane Trend 1 1 Pentatriacontane Trend 1 1 Hexatriacontane Trend 1 1 Heptatriacontane Trend 1 1 Octatriacontane Trend 1 1 Cholesterol Trend 0.7813 1 _5_Alpha_Cholestane Trend 1 1 Dimethyl_Phthalate Trend 0.5139 1 Diethyl_Phthalate Trend 0.5139 1 Di_N_Butyl_Phthalate Trend 1 1 Benzyl_Butyl_Phthalate Trend 0.5241 1 Bis_2_Ethylhexyl_Phthalate Trend 0.6706 1 Di_N_Octyl_Phthalate Trend 0.0614 0.6391 Naphthalene Trend 0.6949 1 Acenaphthylene Trend 0.0293 0.507 Acenaphthene Trend 0.0293 0.507 Fluorene Trend 1 1 Phenanthrene Trend 0.285 1 Anthracene Trend 1 1 Fluoranthene Trend 0.0014 0.071 Pyrene Trend 0.2846 1 Benz_a_anthracene Trend 1 1 Chrysene Trend 0.1915 1

Benzo_b_fluoranthene Trend 0.2926 1 Benzo_k_fluoranthene Trend 0.9479 1 Benzo_a_pyrene Trend 0.7054 1 Dibenz_a_h_anthracene Trend 0.285 1 Benzo_ghi_perylene Trend 1 1

Table 18. Activity 10: Have you been exposed to or have you used chemical solvents in the last 48 hours?

Estimated Number of True Null Hypotheses P-Value Adjustment Method Estimate Proportion 52 1 Positive FDR Bootstrap

p-Values Variable Contrast Raw FDR Q-Value Nicotine Trend 0.9091 0.9104 Undecane Trend 1 1 Dodecane Trend 0.0682 0.255 Tridecane Trend 0 0 Tetradecane Trend 0.25 0.438 Pentadecane Trend 0 0 Hexadecane Trend 0.25 0.438 Heptadecane Trend 0 0 Octadecane Trend 0.0682 0.255 Nonadecane Trend 0 0 Eicosane Trend 0.3864 0.4925 Heneicosane Trend 0.1429 0.378 Docosane Trend 0.0455 0.2107 Tricosane Trend 0 0 Tetracosane Trend 0.1591 0.37 Pentacosane Trend 0 0 Hexacosane Trend 0.0455 0.2107 Heptacosane Trend 0 0 Octacosane Trend 0 0 Nonacosane Trend 0 0 Triacontane Trend 0.4091 0.4974 Hentriacontane Trend 0.2857 0.488 Dotriacontane Trend 0.75 0.438

Tritriacontane Trend 0.8571 0.378 Tetratriacontane Trend 0.7045 0.4615 Pentatriacontane Trend 1 0 Hexatriacontane Trend 0.7045 0.4615 Heptatriacontane Trend 1 0 Octatriacontane Trend 0.3636 0.4866 Cholesterol Trend 0.4286 0.5345 _5_Alpha_Cholestane Trend 0.5 0.5058 Dimethyl_Phthalate Trend 0.2857 0.488 Diethyl_Phthalate Trend 0.3864 0.4925 Di_N_Butyl_Phthalate Trend 0.2857 0.488 Benzyl_Butyl_Phthalate Trend 0.2727 0.4505 Bis_2_Ethylhexyl_Phthalate Trend 0.2857 0.488 Di_N_Octyl_Phthalate Trend 0.25 0.438 Naphthalene Trend 0.2857 0.488 Acenaphthylene Trend 0.25 0.438 Acenaphthene Trend 0.2857 0.488 Fluorene Trend 0.1818 0.3902 Phenanthrene Trend 0.2857 0.488 Anthracene Trend 0.1591 0.37 Fluoranthene Trend 0.1429 0.378 Pyrene Trend 0.0455 0.2107 Benz_a_anthracene Trend 0.1429 0.378 Chrysene Trend 0.0227 0.1508 Benzo_b_fluoranthene Trend 0.1429 0.378 Benzo_k_fluoranthene Trend 0 0 Benzo_a_pyrene Trend 0 0 Dibenz_a_h_anthracene Trend 0 0 Benzo_ghi_perylene Trend 0 0

Table 19. Activity 11: Have you dealt with or been exposed to cleaning solutions in the last 48 hours?

Estimated Number of True Null Hypotheses P-Value Adjustment Method Estimate Proportion 52 1 Positive FDR Bootstrap

p-Values Variable Contrast Raw FDR Q-Value Nicotine Trend 0.7653 1 Undecane Trend 0.388 1 Dodecane Trend 0.331 1 Tridecane Trend 0.331 1 Tetradecane Trend 0.5465 1 Pentadecane Trend 0.7347 1 Hexadecane Trend 0.486 1 Heptadecane Trend 0.7094 1 Octadecane Trend 0.486 1 Nonadecane Trend 1 1 Eicosane Trend 0.1387 1 Heneicosane Trend 0.5993 1 Docosane Trend 0.6645 1 Tricosane Trend 0.7217 1 Tetracosane Trend 0.3625 1 Pentacosane Trend 0.8394 1 Hexacosane Trend 0.3625 1 Heptacosane Trend 0.3311 1 Octacosane Trend 0.7217 1 Nonacosane Trend 0.7217 1 Triacontane Trend 0.9729 1 Hentriacontane Trend 0.683 1 Dotriacontane Trend 0.5465 1 Tritriacontane Trend 0.2785 1 Tetratriacontane Trend 1 1 Pentatriacontane Trend 1 1 Hexatriacontane Trend 1 1 Heptatriacontane Trend 1 1 Octatriacontane Trend 1 1 Cholesterol Trend 0.4226 1

_5_Alpha_Cholestane Trend 1 1 Dimethyl_Phthalate Trend 0.486 1 Diethyl_Phthalate Trend 0.486 1 Di_N_Butyl_Phthalate Trend 1 1 Benzyl_Butyl_Phthalate Trend 0.5216 1 Bis_2_Ethylhexyl_Phthalate Trend 0.0302 1 Di_N_Octyl_Phthalate Trend 0.099 1 Naphthalene Trend 0.7825 1 Acenaphthylene Trend 0.6263 1 Acenaphthene Trend 0.6263 1 Fluorene Trend 1 1 Phenanthrene Trend 0.2349 1 Anthracene Trend 1 1 Fluoranthene Trend 0.2785 1 Pyrene Trend 0.7217 1 Benz_a_anthracene Trend 1 1 Chrysene Trend 0.1155 1 Benzo_b_fluoranthene Trend 0.3774 1 Benzo_k_fluoranthene Trend 0.3774 1 Benzo_a_pyrene Trend 0.1745 1 Dibenz_a_h_anthracene Trend 0.2349 1 Benzo_ghi_perylene Trend 1 1

Appendix B: Codes Used for Calculations

Matlab Code to Obtain Calibration Curves, Slopes, Y-Intercept, R-Squared data = xlsread('Batch1.xlsx'); %standard area counts and concentrations % Copy and paste a text file that is tab-delimited with data organized % such that the first column is a list of all the standard concentrations % and each of the subsequent columns are the integrated peak areas for the % compounds that you want to generate calibration curves for. This code % has NOT been written to deal with header lines, so these should be % removed when generating the text file. conc = data(:,1); % Store the column of standard concentrations as a vector areas = data(:,2:end); % Store all of the integrated peak areas as a matrix

%run this this to use row 5 through 9 which is 600ng/ml to 10ng/ml conc = conc(5:9,:) areas = areas(5:9,:)

%conc = conc(3:9,:); %areas = areas(3:9,:);

[m,n] = size(areas); % Obtain the dimensions of matrix "areas". Variable % m is the number of rows while n is the number of columns. for i = 1:n % Create a for loop to process each of the compounds mdl = fitlm(conc,areas(:,i)); % Fit a linear model to each compound i coeffs = mdl.Coefficients; % Store the model's coefficients as a table coeffs = table2cell(coeffs); % Convert the table into a cell array slope(i,:) = coeffs{2,1}; % Store the value of the slope from the cell % array as a column vector, updating for each compound intercept(i,:) = coeffs{1,1}; % Store the value of the intercept from % the cell array as a column vector, updating for each compound R2(i,:) = mdl.Rsquared.Ordinary; % Store the model's R-squared value as % a column vector, updating for each compound

% Below, you can turn on/off the generation of individual figures for % using the data for each compound; that is, n total figures will be % generated. These figures are not intended to be presentation quality, % but they may be useful as diagnostics if desired.

% To turn on plotting: delete or comment the %{ and %} lines % (everything in between should turn black). 75

% To turn off plotting: add or uncomment the %{ line before the line % with figure(i)and a %} line after the final line before the end % statement.

%%{ figure(i) plot(conc,areas(:,i),'o'); % Plot concentration vs. area counts hold on % x = [0:0.1:1]; %original steps of increase for x-axis %(concentration), modify as needed x = [0:50:600]; %new steps of increase for x-axis (concentration),\ % from 0 to 600 but in 50 steps increment. Use 5000 if I use the 5000 % standard, and maybe make steps every 100 or every 1000 for better fit % and resolution % x = [0:100:5000]; % Change this to correspond to your data. ypred = slope(i,:).*x + intercept(i,:); % Generates a line based on % the linear model for each compound above. plot(x,ypred,'r-') % Adds the prediction line to the concentration vs. % area counts

xlabel('Conc. in std. solution (AU)') % Labels the x-axis, using AU % for arbitrary units. ylabel('Area counts (AU)') % Labels the y-axis. %%} end output = [slope, intercept, R2]'; % Compile the outputs from the linear % regression into an 3 x n matrix, where the first row is the slope, the % second row is the intercept, and the third column is the R-squared value. % Each column in this output matrix will correspond to each of the % compounds entered in the text file (in the same order). This matrix can % be copied and pasted into Excel for subsequent use.

Matlab Code for LOD %LOD from column 1 of the file Colm1 = data(:,1); %LOD column 1 for m = 1:size(data,1) %creating matrix for total number of cells in the columns=65 cells for n = 3:size(data,2) %creating matrix for total number of cells in the rows=53 cells if Colm1(m) < data(m,n) %Concentration is greater than LOD, we say 1 binary(m,n) =1; %...we say=1 else binary(m,n) =0; %...if not 1, then put zero end end end binary = binary(:,3:size(data,2)); %LOD Results

Matlab Code for LOQ %----- Colm2 = data(:,2); %LOQ from column 2 of the file for m = 1:size(data,1) %creating matrix for total number of cells in the columns=65 cells for n = 3:size(data,2) %creating matrix for total number of cells in the rows=53 cells if Colm2(m) > data(m,n) %if concentration is below the LOQ... binary1(m,n) = 0; %...put 0 else binary1(m,n) = data(m,n) ; %If not, then put what was originally there (concentration) end end end binary1 = binary1(:,3:size(data,2)); %LOQ results

Code for Significance in the Statistical Analysis Software (SAS) proc multtest pfdr data=sleeap.chem_030218; class smoking_cigarettes_now___1; test mean (

Nicotine_high_low_no

Undecane Dodecane Tridecane Tetradecane Pentadecane Hexadecane Heptadecane Octadecane Nonadecane Eicosane Heneicosane Docosane Tricosane Tetracosane Pentacosane Hexacosane Heptacosane Octacosane Nonacosane Triacontane Hentriacontane Dotriacontane Tritriacontane Tetratriacontane Pentatriacontane Hexatriacontane Heptatriacontane Octatriacontane

Cholesterol _5_Alpha_Cholestane Dimethyl_Phthalate Diethyl_Phthalate Di_N_Butyl_Phthalte Benzyl_Butyl_Phthalate Bis_2_Ethylhexyl_Phthalate Di_N_Octyl_Phthalate Naphthalene Acenaphthylene Acenaphthene Fluorene Phenanthrene Anthracene Fluoranthene Pyrene Benz_a_anthracene Chrysene Benzo_b_fluoranthene Benzo_k_fluoranthene Benzo_a_pyrene Dibenz_a_h_anthracene Benzo_ghi_perylene ); run;

Related Survey Questions for Environmental Exposure (Castner et al., 2018).

 Was this on a farm?  Did you live close to the center or margin of town?  Near fields or orchards?  TOBACCO SMOKING: How much pipe tobacco are you smoking now?  Is an air cleaner or purifier regularly used inside your home?  Is a dehumidifier regularly used to reduce moisture inside your home?  Is a dehumidifier regularly used to reduce moisture inside your home?  Is an exhaust fan that vents to the outside used regularly when cooking in your kitchen?  Is gas used for cooking?  Does your household have pets such as dogs, cats, hamsters, birds or other feathered or furry pets that spend time indoors?  Are pets allowed in your bedroom?  Is a wood burning fireplace or wood burning stove used in your home?  Are unvented gas logs, unvented gas fireplaces, or unvented gas stoves used in your home?  In the past week, has anyone smoked inside your home?  In your work or daily life, are (were) you regularly exposed to Asbestos?  In your work or daily life, are (were) you regularly exposed to Chemicals/Acids/Solvents?  In your work or daily life, are (were) you regularly exposed to Coal or Stone Dusts?  In your work or daily life, are (were) you regularly exposed to Coal Tar/Pitch/Asphalt?  In your work or daily life, are (were) you regularly exposed to Diesel Engine Exhaust?  In your work or daily life, are (were) you regularly exposed to Formaldehyde?  In your work or daily life, are (were) you regularly exposed to Gasoline Exhaust?  In your work or daily life, are (were) you regularly exposed to Pesticides/Herbicides?  In your work or daily life, are (were) you regularly exposed to Textile Fibers/Dusts?  In your work or daily life, are (were) you regularly exposed to Wood Dust?  In your work or daily life, are (were) you regularly exposed to X- rays/Radioactive Materials? 80

 In the past year has there been a major renovation to this house or apartment, such as adding a room, putting up or taking down a wall, replacing windows, or refinishing floors?  What type of renovation?  When was the last renovation?

 Use of paints, thinners, removers, typewriter corrective fluids in the last 48 hours  Use of glues, adhesives, contact cement, super glues, and aerosol adhesives containing chemical solvents  Use of gasoline lawn mower, and chain saw, or other gasoline equipment in the last 48 hours

 Use of sander or saw in the last 48 hours  Have you used Pesticides sprayed or has someone used near you in the last 48 hours?  Have you used Pesticides sprayed or has someone used near you in the last 48 hours?  Have you used sweeping indoors or has someone used near you in the last 48 hours?  Have you used Dusting or has someone used near you in the last 48 hours?

 Have you used cleaning solutions (including household cleaners and chemicals) or has someone used near you in the last 48 hours?  Have you used Gardening or has someone used near you in the last 48 hours?  Have you used Woodworking or has someone used near you in the last 48 hours?  Have you used Metal working/welding or has someone used near you in the last 48 hours?

 Have you used Broiling, smoking, grilling or frying inside the house or has someone used near you in the last 48 hours?  Have you used Broiling, smoking, grilling or frying outside the house or has someone used near you in the last 48 hours?  During the last 48 hours (the study period) did you or anyone else park a car or other motor vehicle in:  A garage attached to your home, detached garage, A carport attached to your home, No

 During the last 48 hours (the study period) did you or anyone else start a car or other motor vehicle in:  A garage attached to your home, detached garage, A carport attached to your home, No  During the last 48 hours (the study period) was there any diesel vehicles parked around the house?