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CHARACTERIZING PERSONAL EXPOSURE IN CLOSE PROXIMITY TO INDOOR AIR POLLUTION SOURCES A DISSERTATION SUBMITTED TO THE DEPARTMENT OF CIVIL AND ENVIRONMENTAL ENGINEERING AND THE COMMITTEE ON GRADUATE STUDIES OF STANFORD UNIVERSITY IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY Viviana Acevedo-Bolton December 2010
Transcript
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CHARACTERIZING PERSONAL EXPOSURE IN CLOSE PROXIMITY TO

INDOOR AIR POLLUTION SOURCES

A DISSERTATION

SUBMITTED TO THE DEPARTMENT OF

CIVIL AND ENVIRONMENTAL ENGINEERING

AND THE COMMITTEE ON GRADUATE STUDIES

OF STANFORD UNIVERSITY

IN PARTIAL FULFILLMENT OF THE REQUIREMENTS

FOR THE DEGREE OF

DOCTOR OF PHILOSOPHY

Viviana Acevedo-Bolton

December 2010

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http://creativecommons.org/licenses/by-nc/3.0/us/

This dissertation is online at: http://purl.stanford.edu/cc164tj5057

© 2011 by Viviana Acevedo-Bolton. All Rights Reserved.

Re-distributed by Stanford University under license with the author.

This work is licensed under a Creative Commons Attribution-Noncommercial 3.0 United States License.

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I certify that I have read this dissertation and that, in my opinion, it is fully adequatein scope and quality as a dissertation for the degree of Doctor of Philosophy.

Lynn Hildemann, Primary Adviser

I certify that I have read this dissertation and that, in my opinion, it is fully adequatein scope and quality as a dissertation for the degree of Doctor of Philosophy.

Alexandria Boehm

I certify that I have read this dissertation and that, in my opinion, it is fully adequatein scope and quality as a dissertation for the degree of Doctor of Philosophy.

James Leckie

Approved for the Stanford University Committee on Graduate Studies.

Patricia J. Gumport, Vice Provost Graduate Education

This signature page was generated electronically upon submission of this dissertation in electronic format. An original signed hard copy of the signature page is on file inUniversity Archives.

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Abstract

Personal exposure to air pollutants can be substantially higher in close

proximity to an active source, due to non-instantaneous mixing of emissions. This

thesis quantifies this proximity effect for buoyant and nonbuoyant sources in 2

naturally ventilated homes in Northern California, assessing its spatial and temporal

variation, and the influence of factors such as ventilation rate and source type on its

magnitude.

A total of 68 experiments were conducted in two homes under natural

ventilation rates ranging from 0.2 – 5.4 air exchanges per hour, each involving 12 to

37 real-time carbon monoxide (CO) or particulate matter (PM2.5) monitors placed in

proximity (0.25 – 5.0 m) to a controlled stationary point source. Concentrations

measured in close proximity (within 1 m) to the source were highly variable, with 5-

min averages that typically varied by >100-fold. This variability was due to short-

duration (< 10 s) pollutant concentration peaks (“microplumes”) that were frequently

recorded in close proximity to the source – for PM2.5, these peaks often exceeded 1000

g/m3.

Compared with concentrations predicted assuming uniform, instantaneous

mixing within the room, average concentrations measured within 0.25 m of the source

were 6 – 20 times as high. As distance from the active source increased the ratio of

measured concentration to the well-mixed prediction decreased. For most cases, ratios

approached 1 (well-mixed) at distances of 3 – 5 m from the source.

Air change rate and vertical distance from source to receptor were two factors

that affected horizontal concentration profiles. Under natural ventilation conditions, as

air change rate increased, the concentrations close to the source became more

elevated, magnifying the proximity effect. Mechanical ventilation increased mixing in

the room, and resulted in a diminished proximity effect. Vertical mixing of the

pollutant was even more hindered, due to temperature stratifications present in the

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rooms – the concentrations more than 0.5 m vertically from the height of a nonbuoyant

plume rapidly approached the well-mixed prediction.

For buoyant stationary plumes, such as emissions from a smoldering cigarette

or stick of incense, the height of maximum concentrations was typically 0.5 – 1 m

above the source, due to plume rise. The rise of a buoyant plume was more limited

under natural than mechanical ventilation conditions, presumably due to less

temperature stratification. For an actual smoker, where both highly buoyant sidestream

smoke and less buoyant exhaled mainstream smoke were generated, and where the

cigarette was moved around by the smoker, emissions were dispersed over a broader

vertical extent. However, for 20 indoor experiments involving 2-4 people sitting in

close proximity to an active smoker, the magnitude of the proximity effect within 0.5

– 1 m was comparable to measurements for the stationary nonbuoyant source

experiments.

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Acknowledgements

Dedicado a mis padres

First of all I would like to thank my advisor, Dr. Lynn Hildemann for her support and

guidance in my graduate career and my life. She was available when I needed her and

always had solutions to my problems. I could not have found a better advisor and

mentor.

I would like to thank my committee members Dr. Jim Leckie and Dr. Ali Boehm for

serving on my thesis committee and for their friendship over the years. I would also

like to thank Dr. Wayne Ott and Dr. Neil Klepeis who have been wonderful mentors

and friends. This thesis would not have been possible without them.

I want to thank my friends for their support during these years, in particular my

colleagues Ruoting Jiang and Kai-Chung Cheng who have helped me so much and

shown me such kindness. They have made these last 3 years so much fun and I could

not have done this without them.

Finally, I would like to thank my family for loving me and supporting me through

everything. They always encouraged me to do what I loved and to give it my all. I

thank my beautiful nieces for always bringing a smile to my face and for reminding

me what is really important.

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Table of Contents

Abstract .......................................................................................................................... iv

Acknowledgements ....................................................................................................... vi

List of Tables .................................................................................................................. x

List of Figures ............................................................................................................... xii

Chapter 1: Introduction ................................................................................................... 1

1.1 Motivation ............................................................................................................ 1

1.2 Modeling indoor exposure to pollutants ............................................................... 1

1.3 Factors influencing indoor exposure to pollutants ............................................... 3

1.3.1 Mixing rate .................................................................................................... 3

1.3.2 Source emissions characteristics ................................................................... 4

1.3.3 Averaging Timescale ..................................................................................... 5

1.4 Choice of Focus Areas for Thesis Research ......................................................... 6

1.5 Dissertation Overview .......................................................................................... 7

References .................................................................................................................. 8

Chapter 2: Measurement of the Proximity Effect for Indoor Air Pollutant Sources in

Two Homes .................................................................................................................. 12

2.1 Abstract ............................................................................................................... 12

2.2 Introduction ........................................................................................................ 12

2.3 Methodology ....................................................................................................... 15

2.3.1 Spatial array ................................................................................................. 15

2.3.2 Monitor calibration ...................................................................................... 17

2.3.3 Source emissions ......................................................................................... 17

2.3.4 Factorial Design ........................................................................................... 17

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2.3.5 Data Analysis ............................................................................................... 18

2.3.6 Averaging Time ........................................................................................... 20

2.3.7 Statistical Analyses ...................................................................................... 21

2.4 Results ................................................................................................................ 21

2.4.1 Summary Statistics ...................................................................................... 22

2.4.2 Frequency Distributions .............................................................................. 24

2.4.3 Proximity Curves ......................................................................................... 26

2.5 Conclusions ........................................................................................................ 30

References ................................................................................................................ 30

Chapter 3: Controlled Experiments Measuring Personal Exposure to PM2.5 in Close

Proximity to a Smoker .................................................................................................. 42

3.1 Abstract ............................................................................................................... 42

3.2 Introduction ........................................................................................................ 42

3.3 Methodology ....................................................................................................... 44

3.3.1 Frequency Distributions .............................................................................. 47

3.3.2 Statistical Analyses ...................................................................................... 47

3.4 Results ................................................................................................................ 48

3.4.1 Indoor Table Experiments ........................................................................... 48

3.4.2 Indoor Couch Experiments .......................................................................... 51

3.4.3 Casino Experiments ..................................................................................... 55

3.4.4 Outdoor Table Experiments ........................................................................ 58

3.5 Summary and Conclusions ................................................................................. 60

References ................................................................................................................ 61

Chapter 4: Real-time Measurements of PM2.5 in Close Proximity to an Indoor Particle

Source ........................................................................................................................... 70

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4.1 Abstract ............................................................................................................... 70

4.2 Introduction ........................................................................................................ 70

4.3 Methodology ....................................................................................................... 73

4.3.1.Array ............................................................................................................ 73

4.3.2 Sources ........................................................................................................ 74

4.3.3 Ventilation ................................................................................................... 74

4.3.4 Statistical Analyses ...................................................................................... 75

4.4 Results ................................................................................................................ 76

4.4.1 Source and Height Effects ........................................................................... 76

4.4.2 Effect of Ventilation .................................................................................... 82

4.5 Summary and Conclusions ................................................................................. 84

References ................................................................................................................ 85

Chapter 5: Conclusions ................................................................................................. 95

5.1 Major Findings and Contributions ...................................................................... 95

5.2 Recommendations for Future Work ................................................................... 96

References ................................................................................................................ 97

Appendix A .................................................................................................................. 98

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List of Tables

Table 2.1 Table 2.1: Summary of Experiments Conducted at Each Home .......... 18

Table 2.2 Measured Mean [Median] Concentrations in ppm, For One

Representative Case and Two Extreme Cases, Compared With the

Well-mixed Prediction in Home #1 ...................................................... 23

Table 3.1 Summary of Indoor and Outdoor Personal Exposure Experiments ..... 46

Table 3.2 Mean PM2.5 Exposures (above background) of 3 Persons Sitting with a

Smoker for the Indoor Table Experiments (g/m3; averaging time =

cigarette duration) ................................................................................. 48

Table 3.3 Pooled summary statistics for “Sitting at a table” experiments for 4

people and 1 SIM (10-s averages) ........................................................ 50

Table 3.4 Parameters Describing the Lognormal Model Fit to the Observed

Frequency Distributions of the 10-s Averages for Indoor Table

Experiments with a Smoker .................................................................. 51

Table 3.5 Mean PM2.5 Exposures (above background) of 4 Persons Sitting with a

Smoker for Couch Experiments (g/m3; averaging time = cigarette

duration) ............................................................................................... 52

Table 3.6 Mean Personal Exposure to PM2.5 (above background) of 4-5 Persons

at a Casino (g/m3; averaging time = cigarette duration)..................... 57

Table 3.7 Parameters Describing the Fit of the Lognormal Model to the Observed

Frequency Distributions of the 10-s Averages (above background) for

Outdoor Table Experiments with a Smoker ......................................... 59

Table 4.1 Summary of Particle Proximity Experiments ........................................ 75

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Table 4.2 Average concentrations in g/m3, measured during a smoked cigarette

for two ventilation conditions ............................................................... 82

Table A1 Horizontal Rate of Spread Calculated from First Increase in SF6

Concentration in Home #1 .................................................................. 101

Table A2 Horizontal Rate of Spread Calculated from First Increase in SF6

Concentration in Home #2 .................................................................. 101

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List of Figures

Figure 1.1 Conceptual model of mixing in a room ................................................ 11

Figure 2.1 Floor plan of the two homes in study with array of CO monitors used.

Black dots represent CO monitor locations while a black star in middle

of array represents the CO and SF6 sources. Positive X and Y axes are

shown on each array ............................................................................. 34

Figure 2.2 Time series plot showing how our measured data compares with the

well-mixed model’s prediction. The dotted line represents the

theoretical ambient CO concentration infiltrating from outdoors (very

low), while the dashed line shows the well mixed prediction for a given

air change rate. The solid line shows the CO concentration measured at

the given air change rate at 0.25 m from the source. During the source

emission period ( ), the measured data can be much greater than the

well-mixed predicted

concentration………………………………………………………….35

Figure 2.3 Time series plots of each monitor along the long axis of the array in

Home #1, starting with the closest monitor and moving away from the

source. This figure shows that clusters of microplumes that are biggest

in duration and magnitude closest to the source diminish as they move

farther away. This figure also shows the preferred direction of motion

changes throughout the day. Figure 2.3a shows the time series plots for

monitors along the negative X-axis, while Figure 2.3b shows monitors

along the positive X-axis. ..................................................................... 36

Figure 2.4a Logarithmic-probability graph of the incremental exposure (measured

CO concentration with the well-mixed model’s prediction subtracted),

showing the frequency distribution of the microplumes. The lines are

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nearly straight below 70%, showing that the microplumes can be

modeled as lognormal distributions. The parallel lines indicate that

while the median and average values decrease as one moves farther

away from the source, the variability (reflected by the slope) is similar.

This data was measured at Base Case air change rate (0.5 h-1

) and

height conditions, but at half the normal emission rate (~700 cc/hr).

This plot is representative of our results. .............................................. 37

Figure 2.4b Incremental exposure distributions for an experiment in Home #2 with

air change rate of 2.1 h-1

, source and monitor 1 m from ground. Above

5%, the distributions are fairly straight, indicating a tendency towards

lognormality. These lines are not as parallel as in Figure 2.4 a. For this

experiment, both the median and the variability decrease as one moves

away from the source. ........................................................................... 38

Figure 2.5 This figure shows the effect of ventilation conditions on concentration

versus distance in Home #1 (a) and Home #2 (b). The y-axis shows the

normalized CO concentration (i.e. ), and the

x-axis shows distance from the source. As the air change rate

increases, the proximity curve is more pronounced. “N” denotes

experiments done at night .................................................................... 39

Figure 2.6 This figure shows how the proximity curve is affected by source height

relative to monitor height. This graph shows that there is a maximum

plane of exposure (in this case, at source height) and that the pollutant

takes a long time to mix vertically. ...................................................... 40

Figure 2.7 This figure shows the proximity curve for a short (1-2 h) cluster of

microplumes. For a specific cluster of microplumes moving away from

source, the concentration at 0.25 m from the source can be 4 times as

high as when the results are averaged radially ..................................... 41

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Figure 3.1 Line Drawing showing the necklace designed to keep the inlet of the

SidePak monitor within 0.2 m of the person’s breathing zone ............. 63

Figure 3.2 Layout used for the indoor experiments in Home #1 and the casinos.

The two areas labeled (a) show the dining room and garage table

experiments; (b) shows the couch experiments. (c) – (d) show the

configurations used in the casinos. ....................................................... 64

Figure 3.3 Frequency distributions of the pooled “sitting at a table” indoor

experiments. Up to 8 cigarettes are represented in each distribution.

Background concentrations were measured for 5 min prior to each

smoking event. Best fit lines are drawn in ........................................... 65

Figure 3.4 Time-series plots for couch Experiment #1.6: (a) Time series plots for

the nonsmokers and the far away SIM, and (b) Time series for the

smoker and the SIM in front of him. The y-axis in (a) is magnified to

show more details. The cigarette source period is between the dotted

blue lines ............................................................................................... 66

Figure 3.5 Time-series plots of casino Experiment SP.5. The cigarette source

period is between the dotted blue lines: (a) Two people sitting at slot

machines to the left and right of the smoker, and (b) 2 people sitting 2

seats away from the smoker received few, low magnitude microplumes

during the source period. Their exposure was only slightly higher than

background concentrations. .................................................................. 67

Figure 3.6 Frequency distributions of the pooled “sitting at a table” outdoor

experiments. Each of the distributions (except BACKGROUND) has

two distinct slopes: one similar to background (ambient) concentrations

and one much higher, from exposure to cigarette emissions starting at

about 7 g/m3 ....................................................................................... 68

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Figure 3.7 Frequency distributions of the pooled “sitting at a table” outdoor

experiments (same as Figure 6) with the background concentrations

subtracted for the smoker and 3 nonsmokers. The best fit lines were

estimated by least squares regression to show how a lognormal

distribution fits the data ........................................................................ 69

Figure 4.1 Layout of the lab space used in these experiments. The two doors and

the one small window used for ventilation are located towards the

bottom of the figure. The monitoring array (with the 0 degree angle) is

shown as open circles with the source at the center (shown by the star).

The door to the small office remained closed for the experiments ...... 87

Figure 4.2 Box plots showing the distribution of 10 s average concentrations from

the 4 monitors at 0.4 m from the source. In these box plots, the edges of

the box are the 1st and 3

rd quartile, and the median is shown as the solid

black line in the box. The mean concentration is shown as the dotted

line. The whiskers extend to the 10th

and 90th

percentile, while the dots

show the 95th

and 5th

percentile (outliers) ............................................ 88

Figure 4.3 Box plots showing the distribution of 10-s averages from the 4

monitors closest to the source from 6 different experiments, measuring

concentrations from three sources. In all 6 experiments, the source and

monitors were at the same height ......................................................... 89

Figure 4.4 Distributions of 10 s averages at 0.4 m horizontal distance from the

source for 5 experiments with a real smoker. In these experiments, the

vertical distance between the smoker’s breathing zone to the monitors

was varied resulting in four distances relative to the source: 0.2 m

below, level, 0.2 m above, and 0.4 m above ........................................ 90

Figure 4.5 Time series plots of the four monitors closest to the smoker (0.4 m

horizontal distance) for the same 5 experiments presented in Figure 4.4.

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Each plot is one experiment, with the 4 monitors at all 4 angles shown

together ................................................................................................. 91

Figure 4.6 Box plots showing the distribution of 10-s average concentrations

measured at the monitors along the 0 degree (direction the smoker was

facing) axis for two experiments with a real smoker. For the

experiment shown in 6a, the smoker was exhaling smoke slightly down

and away from the monitors. In the experiment shown in 6b, the

smoker was exhaling directly at the monitors in front of him. ............. 92

Figure 4.7 Time series plots for 12 monitors 0.25 m from a burning incense stick at

3 heights in 4 directions around the source in Watsonville home. Each

section shows the three monitors in one direction (e.g. Figure 4.7 (a)

shows the three monitors at 0.25 m horizontal distance at the 0 degree

angle). ................................................................................................... 93

Figure 4.8 Time series plots for 3 experiments with a smoker: two with doors

closed (first two plots) and one with doors open (last plot). Each plot

shows the 10 s average concentrations from 2 monitors on the array:

one monitor close to the smoker that measured the highest

concentration (shown in black), and a second monitor, far away from

the smoker that measured the lowest concentration during the

experiment ............................................................................................ 94

Figure A1 Time series plots for 3 experiments with a smoker: two with doors

closed (first two plots) and one with doors open (last plot). Each plot

shows the 10 s average concentrations from 2 monitors on the array:

one monitor close to the smoker that measured the highest

concentration (shown in black), and a second monitor, far away from

the smoker that measured the lowest concentration during the

experiment ............................................................................................ 98

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Figure A2 This figure shows the result of excluding 5-min averages that are

negatively impacted by offscale measurements. Red circles show the

frequency distribution for all 5-min averages from 9/8/08 at 0.25 m

from the source. For the frequency distribution shown by the black

circles, I applied the “rule” I formulated from Figure A2 (to exclude

points that have more than 5 readings off scale) .................................. 99

Figure A3 This figure shows the effect of increased averaging times on raw data

measured at 0.25 m from the source on 9/7/08 (ACH = 0.33 h-1, source

at 0.75 m from the ground, and monitors at 1 m from the ground). As

the averaging time increases, the slope of the line gets smaller, but even

at 2 h averaging time, the slope remains very large ........................... 100

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Chapter 1: Introduction

1.1 Motivation

Recent studies of human activity patterns have found that Americans spend almost

90% of their time indoors (Nelson et al., 1994, Klepeis et al., 2001). EPA’s Total

Exposure Assessment Methodology studies (Hartwell et al., 1992, Ozkaynak et al.,

1996) showed that personal exposure to pollutants is usually substantially higher than

concentrations measured outdoors or indoors at a stationary monitor (a phenomenon

called the personal cloud). These two factors indicate that indoor air pollution and

personal exposure are increasingly important to measure and understand. Rodes et al.

(1991) hypothesized that the three main causes of the personal cloud were: (1)

proximity to a source (Furtaw et al., 1996, McBride et al., 1999), (2) resuspension of

particles from human activity (e.g. Ferro et al., 2004), and (3) a compartmental effect

(that is, being in the same room as the source; e.g. Ferro et al., 2009).

This thesis focuses on understanding and characterizing the proximity effect in

naturally ventilated homes. Of particular interest to our group is personal exposure to

secondhand smoke (SHS). Tobacco smoke contains many toxic compounds and

human carcinogens (e.g. formaldehyde, benzene, arsenic). The documented adverse

effects of exposure to SHS range from upper respiratory tract irritation to lung cancer

and coronary heart disease. The US EPA (1992) and the US Surgeon General (2006)

have established that there is no known risk-free level of exposure to secondhand

smoke. Recent anti-smoking laws in California prohibit smoking in bars and

restaurants, as well as most public, indoor spaces. Therefore, it is important to

measure the proximity effect in homes, where smokers may still smoke indoors.

1.2 Modeling indoor exposure to pollutants

Most indoor exposure models use the well-mixed box model, which assumes

instantaneous mixing, to calculate indoor concentrations of a pollutant. The main

factors in the box model are: source emissions, ventilation (transport of pollutants in

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from outdoors and out from indoors), and deposition (and/or sorption to walls). The

black arrows in Figure 1 represent these mechanisms under natural ventilation

conditions. A potentially major limitation of the box model is that it assumes an

instantaneously well-mixed space – this is not true when a source is actively emitting.

Klepeis (1999) concluded that to be able to neglect mixing effects, the appropriate

timescales were 12 min for a short-duration source (such as a 6 min cigarette). Thus,

within minutes after extinguishing a source, mixing effects can be neglected. This is

when the box model can be viewed as reliable for predicting indoor concentrations.

To distinguish this well-mixed period from non-well-mixed times, three

sequential time periods have been proposed for a source emitting indoors: the alpha,

beta, and gamma periods (Ott 1995). During the gamma period, the source is no longer

active and the room has become well-mixed. By definition, during the gamma period,

the predictions of the well-mixed box model should agree well with concentrations

measured at any point in the room. The beta period is the short time immediately after

the source is extinguished, when the room is not yet well-mixed. During the beta

period, measured concentrations in close proximity to the source will deviate from the

well-mixed model, because the most recent emissions are still mixing throughout the

room. During the alpha period, or the active source period, high concentration, short

duration peaks (or microplumes) are frequently measured, especially in close

proximity to the source. During the alpha period, measured concentrations near the

source are typically well above the mass model balance (well-mixed) prediction, as the

fresh emissions are not completely mixed throughout the space.

In the presence of an active source, two components (arrows) can be added to

the box model in Figure 1 to more fully describe pollutant dispersion in the room.

These are: advection of the freshly-emitted pollutants (due to buoyancy and/or initial

momentum), and turbulent diffusion. While advection only affects the pollutants

within the first few seconds of being emitted, the process of turbulent diffusion

continuously generates mixing in the room. These two components are shown as red

arrows (to distinguish from the box model components) in Figure 1. These added

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components to the box model conceptually capture the movement and spread of fresh

emissions with time, reflecting that mixing is non-instantaneous, As a result of these

two components, the pollutant concentration profile indoors will be non-uniform

during and immediately after the source emission (alpha) period, with the highest

concentrations (that is, the strongest proximity effect) close to the source.

1.3 Factors influencing indoor exposure to pollutants

To better characterize and understand the proximity effect, it is important to

consider the complex factors that influence how pollutants mix and behave indoors. In

planning this thesis research, I considered 3 broad types of factors as potentially

important: (1) the mixing characteristics of the room, (2) the characteristics of the

source emissions, and (3) the timescales for the source emissions and the

measurements. Each of these 3 factors, and their influence on the concentration

variations indoors, will be discussed individually, in the following sections.

1.3.1 Mixing rate

The mixing rate is influenced by vertical temperature profiles in the room, air

exchange with outdoors, ventilation type (mechanical or natural), and the temperature

of walls compared with the rest of the room (Nazaroff, 2004).

First, whether a room/house is mechanically or naturally ventilated will have a

large effect on the mixing rate. Mechanical ventilation increases mixing because it

forces an air change rate, and the mixing process reduces temperature gradients. Thus,

under vigorous mechanical ventilation conditions, the ventilation system will have a

dominant influence on the rate of mixing.

Natural ventilation depends on window/door openings and the permeability of

the building as well as on outdoor conditions (wind, sun, outdoor/indoor temperature

differential). Under natural ventilation conditions, which are more common in

residential homes, pronounced vertical temperature profiles often develop. These

stable temperature profiles will limit how high a buoyant plume will rise, and

determine to what extent vertical mixing will be inhibited. The rate of air exchange in

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naturally-ventilated rooms (the “air change rate”) can also affect turbulent diffusion.

Cheng (2010) found a direct linear relationship between air change rate and a turbulent

diffusion coefficient timescale (K divided by the squared length scale of the room).

Finally, for a non-mechanically ventilated test room with a very low air change rate (<

0.08 h-1

), Baughman et al. (1994) found that the characteristic mixing time was faster

(by up to a factor of 10) when high wall/room temperature differentials were created

via incoming solar radiation.

1.3.2 Source emissions characteristics

Source emissions characteristics also can affect the concentration variation in a

room. As mentioned in Figure 1, most sources will be released with some initial

directionality, due to momentum and/or buoyancy. Focusing on cigarettes, there are 2

types of emissions which are important: sidestream smoke and exhaled mainstream

smoke.

Sidestream smoke is released from the tip of a smoldering cigarette and is

emitted almost continually for the duration of the cigarette. Sidestream smoke is

released at a temperature much greater than ambient (350 ˚C; Baker et al., 1990) and is

therefore very buoyant. This plume will rise vertically, cooling until it reaches room

temperature. The height of maximum concentration for freshly-emitted sidestream

smoke will be on the order of a meter above the smoker.

Mainstream smoke exhaled by the smoker is a momentum-driven plume,

blown in one direction (usually horizontal) by the smoker, and having an initial

temperature similar to the smoker’s body temperature. Since body temperature is only

slightly higher than room temperature, the buoyancy for sidestream smoke will be

minimal. Thus, the height of maximum concentration for freshly-exhaled mainstream

smoke should be close to the height of exhalation. In general, once the buoyancy

and/or momentum of the emitted plume dissipates turbulent diffusion will become the

dominant indoor process for moving and mixing the emissions.

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1.3.3 Averaging Timescale

The averaging timescale for the measurement, as compared with the duration

of the source emission, also affects the spatial concentration profile, in two potential

ways.

First, if the averaging time for the measurements is less than or comparable to

the source emission time, the concentration variation with location in the room will be

substantial for measuring periods that are within or close to the source emission

period. However, if the averaging time is long compared to the source emission

period, then the spatial variation in concentration will be much lower, because the

length of the sampling time interval will allow enough time for the emissions to

disperse and approach a well-mixed state (Klepeis, 1999).

Second, during the “alpha” (= source on) period, the contribution to the total

measured concentration of the well-mixed (that is, more aged) portion of the emissions

versus the fluctuating portion (caused by fresh microplumes) will be different for

long- versus short-duration sources. For a short-duration source, such as a cigarette,

the portion of the total emissions which are well-mixed throughout the room will be

low, thereby magnifying the importance of the microplumes and the proximity effect.

For a long-duration source, the well-mixed portion of the total emissions will increase

with time, becoming a more and more important contributor to the total measured

concentration. This well-mixed portion of the total emissions will eventually reach an

equilibrium concentration. In some cases, this well-mixed portion can become large

enough to dominate the total concentration – Klepeis (1999) reported that spatial

inhomogeneities became negligible after 80 min for a continuous indoor source,

provided that the measurement was not in close proximity to the source.

With integrated filter samples it would be impossible to measure the short-

duration, high magnitude peaks in concentrations representing microplumes that result

from non-instantaneous mixing of fresh emissions. The magnitude and duration of the

peak exposures could be important to understanding the health risks associated with

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short-term exposures. Thus, real-time instruments are essential for measuring

concentrations over shorter time-scales.

1.4 Choice of Focus Areas for Thesis Research

For these field experiments, I have chosen to focus solely on naturally-

ventilated homes, and on measuring concentrations during the “alpha” (active source)

period, when the proximity effect should be especially pronounced.

For field experiments involving real homes, a number of the factors discussed

in the section above, such as wind (which can affect the air exchange rate) and sun

(which can affect the indoor temperature gradients) cannot be controlled. Therefore,

the choices of which factors to investigate were dictated by (a) the ability to vary

them, and (b) the expectation that they would be important factors influencing the

proximity effect indoors.

For all experiments, I examined a range of distances from the source to the

receptor, in order to assess the proximity effect. The factors I chose to vary were

the air change rate,

the variation with vertical vs horizontal distance from the source, and

the source, including location and emission characteristics.

Controlling air change rate in a naturally ventilated space is challenging, given

that weather conditions and indoor/outdoor temperature gradients can influence the air

change rate. Howard-Reed et al (2002) found a strong correlation between window

position and air change rate for a naturally-ventilated Redwood City, CA home, where

indoor/outdoor temperature gradients are small. However, for a given window

position, some variation in the air change rate from day to day was observed. In

California homes, typical air change rates are 0.5 to 2.0 air changes per hour (Wilson

et al., 1996) – this is expected to be important in influencing the proximity effect.

The concentration profiles versus distance from the source were needed to

quantify the proximity effect. The major effort at spatial characterization aimed to

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measure the horizontal variations in exposure with distance from the active source,

focusing on the single vertical height expected to have the maximum concentration

levels. A separate, secondary effort aimed to investigate the vertical variation in

concentrations in close proximity to a source.

For the source itself, one factor varied was whether the emissions were

buoyant. For experiments investigating the horizontal trends for the proximity effect,

a nonbuoyant point source allowed the vertical plane of maximum concentration to be

readily established. For experiments investigating the vertical variations in

concentration, a buoyant point source was chosen. The third type of source tested was

an actual smoker, to evaluate how the proximity characteristics for the controlled

point-source experiments compare with “real-world” conditions. In these

experiments, to a lesser degree, the behavior of the smoker also was varied – for

example, their location within the room (near the center versus closer to a wall), the

amount of movement by the smoker, and the plume characteristics (height of

smoldering cigarette, and direction of exhaled smoke).

Due to the complexity and interconnectedness of these factors, and the paucity

of previous work done in this area, it was not possible within the scope of my thesis to

fully characterize the influence of each general factor on the indoor proximity effect.

Instead, I chose to focus primarily on the effect of the air exchange rate on the

proximity effect for two types of controlled sources (buoyant and nonbuoyant), along

with an initial exploration of how the smoker’s location and behavior. These factors

were chosen because of our ability to vary them and because previous work in this

area pointed to these factors as important in characterizing the proximity effect.

1.5 Dissertation Overview

Chapter 2 discusses the results of a field study designed to quantify the

proximity effect during the active source period in two naturally ventilated homes. CO

is released continuously from a point source located in the middle of a room while a

30 – 37 monitor array measures CO concentrations at different horizontal distances

and angles around the source in real-time. Via different experiments, I examine how

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air change rate and vertical distance between source and monitors affected

concentrations measured at various horizontal distances from the source. I also

compare the measured concentrations with the mass balance model (well-mixed)

predicted concentrations.

In Chapter 3, I discuss experiments designed to measure the smoker’s and

nonsmokers’ real-time exposures to SHS in a number of typical activities or situations

where one might be near a smoker, such as after dinner, sitting at the table, or sitting

next to a smoker at a casino. In these experiments, 2 – 4 nonsmokers and 1 smoker are

equipped with particle monitors set to measure PM2.5 (as a tracer for SHS) for 19

indoor smoking experiments. The objective of this study is to assess to what extent the

proximity effect found from the tracer gas study in Chapter 2 can be generalized to

more realistic situations. In this chapter, I explore smoker behavior, and source to

receptor distance, as well how people versus monitors affects spatial variations in

concentration.

For Chapter 4, I conduct studies similar to those in Chapter 2, but instead

measured fine particle (PM2.5) concentrations from a real combustion source at 14 – 16

points in within 3 m of a source in an indoor environment. In this study I evaluate

three different particle sources: incense, a smoldering cigarette, and a smoked cigarette

to see if there was a difference between a smoldering source and a real smoked

cigarette. I also measure concentrations at different vertical distances from the source

to understand how effective plume heights affect exposures. Similar to chapter 2, this

chapter focuses on physical location of source and receptors, and air change rates, but

with buoyant sources.

In Chapter 5, I summarize the major findings from this research and make

recommendations for future work.

References

Baker, R.R., and Proctor, C.J. (1990) The origins and properties of environmental

tobacco smoke. Environment International, 16, 231-245.

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Baughman, A. V., Gadgil, A. J., and Nazaroff, W. W. (1994) Mixing of a Point Source

Pollutant by Natural Convection Flow within a Room. Indoor Air, 4, 114-122

Cheng, K.C. (2010) Chapter 3 in “CHARACTERIZING AND MODELING CLOSE-

PROXIMITY EXPOSURE TO AN AIR POLLUTION SOURCE IN

NATURALLY VENTILATED RESIDENCES”, Ph.D. thesis, Stanford

University. Currently under review for publication in Environmental Science

& Technology.

Ferro, A. R., Kopperud, R. J., and Hildemann, L. M. (2004) Elevated personal

exposure to particulate matter from human activities in a residence. Journal of

Exposure Analysis and Environmental Epidemiology, 14, S34-S40.

Ferro, A. R., Klepeis, N. E., Ott, W. R., Nazaroff, W. W., Hildemann, L. M., and

Switzer, P. (2009) Effect of interior door position on room-to-room differences

in residential pollutant concentrations after short-term releases. Atmospheric

Environment, 43, 706-714.

Furtaw, E. J., Pandian, M. D., Nelson, D. R., and Behar, J. V. (1996) Modeling indoor

air concentrations near emission sources in imperfectly mixed rooms. Journal

of the Air & Waste Management Association, 46, 861-868.

Hartwell, T. D., Perritt, R. L., Pellizzari, E. D., and Michael, L. C. (1992) Results from

the 1987 Total Exposure Assessment Methodology (TEAM) study in Southern

California. Atmospheric Environment Part A-General Topics, 26, 1519-1527.

Howard-Reed, C., Wallace, L. A., and Ott, W. R. (2002) The effect of opening

windows on air change rates in two homes. Journal of the Air & Waste

Management Association, 52, 147-159.

Klepeis, N. E. (1999) Validity of the uniform mixing assumption: Determining human

exposure to environmental tobacco smoke. Environmental Health

Perspectives, 107, 357-363.

Klepeis, N. E., Nelson, W. C., Ott, W. R., Robinson, J. P., Tsang, A. M., Switzer, P.,

Behar, J. V., Hern, S. C., and Engelmann, W. H. (2001) The National Human

Activity Pattern Survey (NHAPS): a resource for assessing exposure to

environmental pollutants. Journal of Exposure Analysis and Environmental

Epidemiology, 11, 231-252.

McBride, S. J., Ferro, A. R., Ott, W. R., Switzer, P. and Hildemann, L. M. (1999)

Investigations of the proximity effect for pollutants in the indoor environment.

Journal of Exposure Analysis and Environmental Epidemiology, 9, 602-621.

Nazaroff , W.W. (2004) Indoor Particle Dynamics. Indoor Air, 14 (S7): 175–183

Nelson, W.C., Ott, W.R, and Robinson, J.P. (1994) National Human Activity Pattern

Survey (NHAPS): Use of Nationwide Activity Data for Human Exposure

Assessment., EPA Report No. EPA/600/A94/147 prepared by Maryland

University, College Park, Survey Research Center, Enivornmental Protection

Agency, Research Triangle Park, NC.

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Ott, W. R. (1995) Environmental statistics, and data analysis. CRC Press, Inc.; CRC

Press

Özkaynak, H., Xue, J., Spengler, J., Wallace, L., Pellizzari, E., and Jenkins, P. (1996)

Personal exposure to airborne particles and metals: Results from the particle

team study in Riverside, California. Journal of Exposure Analysis and

Environmental Epidemiology, 6, 57-78.

Rodes, C.E., Kamens, R., Wiener, R.W. (1991) The significance and characteristics of

the personal activity cloud on exposure assessment measurements for indoor

contaminants, Indoor Air 2, 123-145.

U.S. E.P.A. (1992) Respiratory Health Effects of Passive Smoking: Lung Cancer and

OtherDisorders; Technical Report EPA/600/6-90/006F; U.S. Environmental

Protection Agency, Office of Research and Development: Washington, DC

U.S. Surgeon General (2006) The Health Consequences of Involuntary Exposure to

Tobacco Smoke: A Report of the Surgeon General; Technical Report; U.S.

Department of Health and Human Services, Centers for Disease Control and

Prevention, Coordinating Center for Health Promotion, National Center for

Chronic Disease Prevention and Health Promotion, Office on Smoking and

Health: Washington, DC

Wilson A. L., Colome S. D., Tian Y., Becker E. W., Baker P. E., Behrens D. W.,

Billick I. H., Garrison C. A. (1996) California residential air exchange rates

and residence volumes, Journal of Exposure Analysis and Environmental

Epidemiology, 6, 311-326.

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Figure 1.1 Conceptual model of mixing in a room.

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Chapter 2: Measurement of the Proximity Effect for

Indoor Air Pollutant Sources in Two Homes

VIVIANA ACEVEDO-BOLTON, KAI-CHUNG CHENG, RUO-TING JIANG, WAYNE R. OTT,

NEIL E. KLEPEIS, AND LYNN M. HILDEMANN

2.1 Abstract

To quantify how proximity to residential sources of indoor air pollutants

affects human exposure, we performed 16 separate monitoring experiments in the

living rooms of two detached single-family homes. CO (as a tracer gas) was released

from a point source in the center of the room at a controlled emission rate for 6-10

hours per experiment, while an array of 30-40 monitors simultaneously measured CO

concentrations with 15-s time resolution at radial distances ranging from 0.25-5 m.

Average exposure levels close to the source were up to 20 times as high as predictions

based on the well-mixed mass balance model. The spread of source emissions

vertically in these houses was greatly hindered compared with the spread in the

horizontal direction.

2.2 Introduction

Americans spend about 90% of their time indoors, the greatest share of which

is spent in their homes (Nelson et al., 1994; Klepeis et al., 2001). A number of studies

have shown that pollutant concentrations measured via a personal exposure monitor

worn by a person are consistently higher than those measured by a stationary monitor

located in the person’s home (Rodes, et al., 1991; Ott, 1995; Wallace, 1996; Ferro et

al., 2004, Wallace et al., 2007b). This phenomenon is called the personal cloud. In

EPA’s Particle Total Exposure Assessment Methodology (PTEAM) study, 178

persons carried personal exposure monitors in Riverside, CA, and the results showed

that 41% of their indoor PM10 exposure was from outdoor air infiltrating into their

homes, 30% was from indoor sources, and 29% was due to the personal cloud

(Özkaynak et al., 1996; Ott et al., 2003; Wallace et al., 2007b). As part of EPA’s Total

Exposure Assessment Methodology (TEAM) study, personal air, fixed site (indoor

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and outdoor) air, and breath samples were collected from 50 individuals in the Los

Angeles, CA area and analyzed for 12 volatile organic compounds (VOCs). The

median ratios of personal air concentrations to indoor (kitchen) air to for these 12

VOCs ranged from 1.07 to 1.86 (Hartwell et al., 1992).

One possible cause of the personal cloud is a compartmental effect: closed or

partially-closed doors between the rooms in a house cause higher concentrations in

rooms where both the person and a source are located, compared with a separate room

where there is no source (Miller et al., 1997; Ott et al., 2003; Ferro et al., 2009).

Another possible cause of the personal cloud is human activities: for particulate

matter, particles from floors and surfaces are resuspended as people move about (Ferro

et al., 2004); for VOCs, the wearing of freshly dry-cleaned clothes can cause elevated

tetrachloroethylene exposure (Wallace et al., 2007). An important cause suggested

for the personal cloud is the close proximity of the person wearing the monitor to an

actively emitting source, called the proximity effect. The proximity effect may occur

if the person is in the same room as the source; for example, an individual in a kitchen

cooking near a gas stove whose pilot light emits carbon monoxide, or a cook broiling

meat with an open flame that emits particles.

When a point source is actively emitting a pollutant, concentrations measured

close to the source are higher than those measured farther away. Ott (1995) describes

the three periods of an indoor source as: , the active source period, , the poorly-

mixed period after the source has ended, and , the well-mixed decay period. Figure

2.2 (described in more detail later) illustrates these three periods. The proximity effect

is associated only with the active emission ( ) period while the pollutants are being

released into the imperfectly mixed surrounding air. For a cigarette, for example, the

proximity effect occurs indoors near the cigarette while it is burning – typically 7-10

min – but concentrations in the room approach a uniform distribution over the entire

volume shortly after the cigarette is extinguished.

Higher pollutant levels near a source are caused by non instantaneous mixing

of the emissions into the surrounding air. Due to the phenomenon of the proximity

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effect, a person closer to a pollution source receives a greater exposure than a person

farther away. A better understanding of the proximity effect, characterizing the

complex relationship between concentrations and distance from the emitting source, is

important for more accurately assessing the relationship between exposure levels and

health effects.

Current inhalation exposure models such as RISK (Sparks 1988, 1991) and

SHEDS-PM (Burke et al., 2001) use the combination of human activity patterns and

microenvironmental concentrations to describe this exposure: i.e.

, where is the microenvironmental concentration, and

, is the time spent in that microenvironment. The models differ in how activity

patterns are chosen and in how to express (Klepeis 2007). Some models estimate

for indoor residential microenvironments using the mass balance model (e.g., SHEDS-

PM as described in Burke et al. (2001)). This model is useful and accurate for a long-

term exposure to a short release of pollutant (Mage et al. 1996) – specifically if the

time scale over which exposure is estimated is much greater than the active source

period ( Conversely, if the exposure time of interest is comparable to the

source emission period, then the non-instantaneous mixing of the pollutant becomes

important to consider for human exposure modeling. Klepeis (1999) concluded that

for a point source in a room, the average concentration measured at a single point

represented, within 10%, the average concentration at any point in the room for

averaging times (starting when the source starts) ranging from 12 min for a short

duration source (such as a 6-min cigarette) to 80 min for a continuous source (such as

multiple cigarettes in a smoking lounge) without measuring in close proximity to an

active source.

McBride (1999) and Ferro et al. (2004, 2009) have found that the source

proximity effect plays an important role in the exposure of humans to indoor and

outdoor pollution sources, but it has not received adequate study in very close

proximity to the source. McBride et al. (1999) conducted CO tracer gas experiments

measuring concentrations in four directions around the source, and at 3 different

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heights in the living room of a 2-story detached home. They compared the

measurements close to the source near the center of the room ( ) with

measurements at a Stationary Indoor Monitor (SIM, ), located at the edge of the

room, and they found that the ratio of average concentration measured at these two

locations ( ) ranged from 1.1 to 13.2.

This current study goes beyond McBride’s research by measuring

concentrations with greater spatial resolution, close to the source and in many

horizontal directions around the source, to better predict average exposure as a

function of distance from the source. The major goals for this study are: (1) to

measure the proximity effect at distances of 0.25-5.0 m from a source; and (2) to

mathematically characterize personal exposure to a continuous point source as the sum

of three components: the ambient concentration infiltrating from outdoors, the well-

mixed (background) concentration from an indoor source component, and the

proximity concentration component.

2.3 Methodology

2.3.1 Spatial array

The design of the spatial array for these experiments expanded upon the array

designed by McBride and coworkers (1999) for proximity measurements. Pure CO

(99.99%) and SF6 gases (Scott Specialty Gases, Plumsteadville, PA) were emitted

from a point located roughly in the center of the room. Up to 37 continuous CO

monitors (Langan Products Model T5, Inc., San Francisco, CA) were placed at radial

distances of 0.25 to 5 m from the source at 8-16 different angles, each measuring CO

concentrations every 15 s. Within the first meter from the source, monitors were

grouped closer together because we expected the proximity effect to be more

pronounced (see Figures 2.1a and 2.1b for the floor plan and monitor array used in

each home). At each radial distance, there were 2-12 monitors, with the highest

number of monitors at 1 and 2 m from the source.

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The array configuration was designed to fit the layout of the room. In the first

house (a two-story home with a single-sloped vaulted ceiling in Redwood City, CA),

the room was longer than it was wide (4 m by 9 m), so the array extended to 4 to 5 m

from the source along the long axis, but only to 2 m from the source along the short

axis, resulting in a total of 37 monitors (Figure 2.1a). In the second house (a one-story

ranch style home with a double-sloped vaulted ceiling in Watsonville, CA), the room

width and length were fairly equal (4.4 m by 5.6 m), but with smaller square footage

than the Redwood City home, allowing for only 30 monitors at radial distances of up

to 2.8 m from the source (Figure 2.1b). Most of the experiments were performed with

the source and monitors at the same height (1 m from the ground, or typical breathing

height of a sitting person); however, for a few experiments we moved the source

above or below the base height of the monitors.

Measurements from 35 of the 37 monitors in Home #1 were used to examine

changes in concentration with radial distance. For the analysis of concentration versus

radial distance from the source, data measured at the 2 monitors at a radial distance of

3 m were excluded, because the much lower monitor density (2 monitors at 3 m versus

12 monitors at 2 m) were deemed not to be sufficient to capture the radial

heterogeneity remaining at 3 m.

SF6 concentrations were measured with two SF6 monitors (Brüel-Kjær Type

1302 Multigas™ monitor) located 4 m from the source (on opposite sides along the

long axis) in Home #1. For Home #2, the SF6 monitors were 2.8 m (at the wall) on

either side of the source. Besides calculating the air change rate from the measured

decay, SF6 concentrations were also used to calculate first-arrival times (time for SF6

monitor to measure the first increase in concentration of > 0.2 ppm), a measure of the

pollutant’s rate of horizontal spread (see Appendix A: Table A1, A2). A 2-D

ultrasonic anemometer (Windsonic, Gill, Inc) on the patio of the home being sampled,

measured outdoor wind speed and direction every second, simultaneous with each

experiment. These measurements were useful for understanding changes in ventilation

conditions throughout the day.

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2.3.2 Monitor calibration

We calibrated each CO monitor with a NIST-certified 50 or 60 ppm span gas

(Scott Specialty Gases, Plumsteadville, PA) at the beginning of each set of

experiments. The monitors were sometimes sensitive to being moved, so after

calibration, they were left in-place until the end of the set of experiments. The

monitors were calibrated to include “offset” concentrations of 1 – 2 ppm to maintain

the monitors and their data loggers in the positive range. Subsequently, in processing

each data set, we subtracted the small background (ambient) concentration measured

during the first 10 minutes, before gas release from the sources began, which was

typically 1-2 ppm (equivalent to the offset concentration).

2.3.3 Source emissions

Flow for both gases was controlled using an electronic mass flow controller

(Model 5850E controller valve and Model 5896 electronic controller, Brooks

Instrument Division, Emerson Electric Co., Hatfield, PA). Each flowrate was checked

repeatedly during the first 10-15 minutes of each experiment using a primary flow

calibrator (Gilibrator, Sensidyne, Inc. Clearwater, FL), to confirm that the value was

correct and stable. Pure CO gas was released from a point source at a flow rate of

approximately 20 cc/min (exit velocity of 0.04 m/s) for 5 – 12 hours per experiment.

SF6 was released from the same point at ~200 cc/min (exit velocity of 0.18 m/s) for

10-20 min at the beginning of each experiment to create a high enough initial SF6

concentration so that its decay could be measured during the rest of the experiment for

calculating the air change rate. Both sources pointed vertically upwards.

2.3.4 Factorial Design

During each experiment, we varied one of three factors: window position,

source height, or source strength. We viewed these factors as most likely to affect a

person’s exposure in a home. The factorial design used at each house is summarized in

Table 2.1. For Home #1, we selected window positions based on a paper (Howard-

Reed, et al., 2002) that studied, in great detail, the effect of window position on air

change rates in this particular home. For Home #2, we chose window positions that we

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expected would match with the air change rates found in the first home. For these

experiments we relied solely on natural ventilation, with the exception of one

experiment in Home #1 in which we closed all windows and used the HVAC fan for

ventilation. We also ran experiments overnight in the second home to make

comparisons (day vs. night) that we were not able to make in the first home.

Table 2.2: Summary of Experiments Conducted at Each Home

Summary of Experiments Home #1 Home #2

No. of Experiments 9 7

Range of Air Change Rates (h-1

) 0.17 – 1.25 0.19 – 5.4

No. of Source Heights 4 2

No. of Source Emission Rates 2 1

Range of Distances (m) 0.25 – 5 0.25 – 3.56

2.3.5 Data Analysis

We conducted three different types of analyses for the CO data to explain and

quantify the proximity effect. First, the radially-averaged concentrations at different

distances from the source, as well as the range and variability of the data under the

different conditions, were determined.

Second, frequency distribution plots of the incremental exposure

(

), were generated to characterize the distribution of 5-min

concentrations measured. Using calculated air change rates, source emission rates,

and the mass balance model, the following equation (Ott 2007) was used to predict CO

concentrations for a given experiment:

(1)

where g is the source emission rate, a is the air change rate, and v is the volume of the

room. Since we subtracted the initial (background) concentration from our data, the

above equation assumes that initial concentrations were zero. McBride (2002)

described indoor CO concentrations as the sum of two processes, one random (which

we are calling the incremental component), and one deterministic. Her work modeled

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the deterministic component using a lognormal tail-fitting algorithm; here, we will

model the baseline (deterministic) process using the mass balance model.

Lastly, we compared the measured data with the predicted CO concentrations

for the room based on the mass balance model (instantaneous uniform mixing

assumption). We normalized using the ratio of measured data to this prediction

( ), so that a value of “1” would indicate that the measured

concentration matched the well-mixed prediction, while a value above “1” would

reflect measurements exceeding the uniform mixing assumption. We then produced

plots of the normalized CO concentration as a function of radial distance from the

source, called proximity curves. Using these proximity curves, we made comparisons

between the two houses to see if similar conditions (e.g. air change rates) resulted in

comparable curves. We also used these curves to see the effects of height and

ventilation conditions on concentrations as a function of distance from the source.

Figure 2.2, a typical time series for one monitor close to a source, illustrates

the combination of processes that describe indoor pollutant concentrations when there

is both an active source and infiltration from outdoors. The dotted line represents the

theoretical infiltration of ambient CO, and shows what the resulting indoor

concentration would be in the absence of an indoor source. The dashed line shows the

well-mixed concentration predicted by the mass balance model, based on the

combination of outdoor air infiltration and the indoor source. The solid line shows

actual measurements made at 0.5 m from the source. The measured concentration

fluctuates above and below the well-mixed model during the source period ( ), with

very high excursions above the predicted value. During the poorly mixed period

immediately after the source has stopped ( ), the fluctuations persist but are smaller

in scale. Once the source emissions become well mixed in the room ( ), the

measured concentration agrees perfectly with the well-mixed prediction.

The work presented in this paper focuses on how measurements compare to the

well-mixed prediction during the α-period. These three lines illustrate that a person’s

exposure during the α-period can be described as the sum of 3 components: (1)

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pollutant originating from outdoors (subtracted from our measurements and

predictions), (2) the well-mixed prediction of the added concentration due to the

indoor source, and (3) the highly variable additional concentration due to imperfectly

mixed emissions, that show up as sharp peaks of short duration or microplumes which

have been found indoors (McBride et al., 1999) and outdoors (Klepeis et al., 2009).

As the distance from the source increases, the contribution of these microplumes to the

total exposure decreases. While the outdoor and source emission components can

readily be used to predict well-mixed indoor concentrations for a given set of room

characteristics, little is known about how to predict the highly variable component of

indoor exposure that occurs during the source emission period. To understand the

characteristics of microplumes, it is important to look at the frequency distribution of

this highly variable component.

2.3.6 Averaging Time

The Langan CO monitor used in this study is a passive monitor that relies on

diffusion of CO into the sensor, resulting in a finite response time that must be

considered when using the monitor for real-time applications if the concentration is

rapidly varying with time (Cheng et al., 2010). For the Langan monitor, Cheng and

coworkers (2010) have shown that 1-min averages of rapidly varying microplumes are

too short to be reliable without first applying an extensive data reconstruction

algorithm; however, at 5 times the instrument response time (~3-4 min), the error in

the averaged concentrations is reduced to < 20%. While further reductions in error

would be achieved with even longer averaging times, a major drawback is the amount

of time resolution that would be lost.

In selecting an appropriate averaging time, a second consideration involved an

analysis of the variability as a function of averaging time. In general, as would be

expected, the variability of the data decreased as averaging time increased. A very

large decrease in standard deviation was seen between one-min and 5-min averaging

times, but the further decrease between averaging over 5 min and 30 min was quite

small. This finding suggested that there were two distinct sources of variability

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exerting an influence. The first was from the microplumes (short duration, high

concentration peaks resulting from imperfect mixing), which were occurring on times

ranging from seconds to minutes. The second type of variability in the data was due to

changes in the airflow directions inside the room, which occurred over times as long

as an hour or more under natural ventilation. Based on this analysis of variability and

the analysis of Cheng et al. (2010), we selected 5 min as an appropriate averaging

time, chosen to reduce response time and synchronization issues (more visible at

shorter averaging times) while retaining adequate real-time resolution.

The upper data logging limits of the CO instruments were between 128 ppm

and 150 ppm, depending on the individual logger and its signal processing board.

Time series plots of data collected at monitors closest to the source (at 0.25 and 0.5 m)

showed that the upper logging limit was often exceeded (see Figure 2.3). Due to this

constraint, concentrations measured at these monitors were often underestimates of the

true exposure. Analysis of the effect of the upper logging limit on the frequency

distribution (Appendix A: Figure A1, A2) demonstrated that if more than 5 of the 20

(15-s) measurements that made up a 5-min average were off scale, the 5-min average

was substantially underestimated. These data points were excluded in our analysis of

the frequency distribution plots because they artificially skewed the distribution to

level off at the value of the upper logging limit. These data were not excluded from the

proximity curves (normalized data) since censoring these data from these curves

would artificially lower the normalized concentrations measured closest to the source.

2.3.7 Statistical Analyses

Summary statistics, frequency distribution plots, and calculations were

performed using SigmaPlot software Version 11.0 (Systat Software, Inc., San Jose,

CA).

2.4 Results

Figures 2.3a, b show simultaneous time-series plots for monitors placed at 0.25

to 5 m from the source along the long axis of the array inside Home #1 (Figure 2.1a)

for Base Case conditions (air change rate = 0.57 h-1

, source and monitors 1 m from

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ground) – Figure 2.3a shows measurements in one direction (negative X, as shown in

Figure 2.1a), while Figure 2.3b shows the opposite direction (positive X). These

figures illustrate that a “cluster” of microplumes can be tracked as they move away

from the source. For example, in the first time-series (Figure 2.3a, 0.25 m from the

source), there is a large cluster of high concentration peaks (microplumes) near the

beginning of the experiment. Moving farther from the source (down to the next time-

series plot), this cluster becomes smaller in magnitude and duration. These

microplumes occurred with the greatest frequency and intensity closest to the source

(within the first 0.5 m), resulting in higher average concentrations as well as higher

variability.

Comparing the two sets of time-series graphs also illustrates how the preferred

direction of motion varied throughout the day. During the first few hours of the

experiment, Figure 2.3a shows a large cluster of microplumes, while Figure 2.3b

shows almost no microplume activity. At nearly the same time that the microplumes

diminish in Figure 2.3a, they appear in Figure 2.3b. Thus, when indoor exposure time

scales are a few hours or less, this directionality can greatly affect a person’s exposure

at a specific location.

2.4.1 Summary Statistics

Table 2.2 presents summary statistics for 3 example cases that cover the range

of factors from the study conducted at the Redwood City home: Case #1 - Base Case;

Case #2 - Source Above Monitors Case; and Case #3 - High Ventilation Case. For

Case #1, all monitors and the source were placed at a height of 1 m from the floor.

Three windows in the house (one in the den on the first floor, two on the second floor

in bedrooms) were open 4-6 inches, resulting in an air change rate of 0.57 h-1

. The

row labeled “Well-mixed prediction” gives the average concentration predicted by the

mass balance model (Equation 1) for this ventilation condition (4.3 ppm) over the

experimental time period. At 0.25 m from the source, the average measured

concentration was almost 10 times higher (42.1 ppm) than the well-mixed prediction.

The difference between this mean and the median (15.1 ppm) produced a right-skewed

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23

frequency distribution, due to the disproportionate influence of the microplumes. At 5

m from the source, the average concentration was still twice the well-mixed

prediction, showing that the proximity effect persists even at this distance. However,

the mean and median concentrations were much closer at 5 m, because there were

fewer microplumes.

Table 2.3: Measured Mean [Median] Concentrations in ppm, For One Representative

Case and Two Extreme Cases, Compared With the Well-mixed Prediction in Home #1

Case #1: Base

Case

Case #2: Source

Above Monitors

Case #3: High

Ventilation

Air Change Rate (h-1

) 0.57 0.41 1.25

Source Height (m) 1 1.25 1

Measured at Radial

Distance (m) Mean [Median] (ppm)

0.25 42.1 [15.1] 13.7 [6.6] 42.4 [17.8]

0.5 26.2 [9.4] 11.8 [5.8] 28.0 [5.1]

1 17.3 [7.5] 10.4 [5.9] 15.0 [3.6]

2 10.6 [6.6] 8.6 [5.6] 7.3 [2.5]

4 8.8 [7.9] 9.5 [7.2] 5.3 [2.5]

5 8.6 [7.7] 6.1 [5.5] 4.9 [1.9]

Well-mixed

Predictiona (ppm)

4.3 5.5 2.3

aThe concentration presented in this row is the average of the well-mixed prediction over the

experimental time period.

In Case #2, the source was moved 0.25 m above the monitors, with ventilation

conditions (window positions) similar to Case #1. The mean concentration at 0.25 m

was only twice as high as the predicted concentration (13.7 ppm versus 5.5 ppm),

while the concentration at 5 m (6.1 ppm) was very close to the average well-mixed

prediction. The mean and median concentrations at 0.25 m were also much closer

together than for Case #1, implying that microplumes were not as influential in this

experiment. This result shows there was greater mixing in the horizontal dimension

than in the vertical direction, making the emissions more concentrated within a narrow

vertical layer around the source height. For this case, the difference between the

average concentration and the well-mixed prediction was much smaller at 5 m,

implying that the monitors were sampling outside a more concentrated vertical layer

of CO.

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24

For Case #3, like the base case (Case #1), both the source and monitors were at

a 1 m height, but the 3 windows were now opened as wide as possible. This window

setting gave an air change rate of 1.25 h-1

, over two times the base case. Closest to the

source (0.25 m), the mean and median concentrations closely resembled the base case,

showing that the proximity to the source emissions was more important at these close

distances than the air change rate. For Case #3, the closest-proximity average

concentrations were 20 times as high as the well-mixed prediction. The drop in

concentration between 0.25 m and 5 m was more drastic than for the base case,

reflecting the influence of the higher air change rate. At 4-5 m from the source, the

concentrations were down to roughly double the well-mixed prediction, comparable to

the ratios seen for these distances in the base case.

By comparison, using both CO and SF6, McBride et al., (1999) measured up to

10 times as high a concentration 0.25 m from the source than at 5.4 m (SIM), and up

to 4.5 times higher at 0.5 m. A measurable proximity effect was still seen up to 2 m

from the source, but no measurements were made between 2 and 5.4 m. Our results

agree very well with these findings, with the addition that we found a proximity effect

(relative to the predicted average) out to 5 m from the source.

2.4.2 Frequency Distributions

Given the tremendous range of concentrations measured in close proximity to a

point source, the best way to describe the entire exposure data set for a person close to

the source is to use a frequency distribution plot. When the logarithm of the

concentration is plotted versus the integral of the normal probability density function a

straight line indicates a lognormal distribution, a distribution commonly seen for

environmental measurements involving the dilution of pollutants (Ott, 1995b).

Figure 2.4a, for example, shows 5 frequency distributions of 5-min average

incremental (

) concentrations for one experimental day

plotted on this lognormal graph. Each separate line is the distribution of a different

radial distance from the source, ranging from 0.25 – 5 m. By focusing on incremental

values, where the background (well-mixed) concentration is subtracted, the

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25

distribution reflects just the random component, or the microplumes. Only

concentrations above 0.1 ppm are shown, because the CO monitor has a sensitivity of

0.1 ppm. The excluded concentrations consisted mostly of measurements taken during

build-up toward equilibrium, when the imperfect mixing resulted in very high peak

concentrations interspersed with very clean “pockets” of air. In addition, as discussed

in the methodology section, some data points at the high end of the frequency

distributions for 0.25 m, 0.5 m, and 1 m have been excluded due to the data logging

limit.

The frequency distributions shown are relatively straight on this logarithmic-

probability plot, suggesting that the microplumes can be approximated as lognormal

distributions. The x-axis is the percentage of the 5-min average concentrations at a

particular distance from the source that are less than the concentration value shown on

the y-axis. For example, the percentage of 5-min incremental exposures of at least 20

ppm increases from 5% at 2 m to 50% at 0.25 m. The closest proximity distances (up

to 2 m from the source) show the greatest slopes in these plots, reflecting the largest

variability seen in the real-time measurements due to the many large microplumes

close to the source. As one example, at 0.25 m from the source, 20% of the 5-min

incremental averages are less than 2 ppm, while 10% are greater than or equal to 80

ppm. At 4 and 5 m from the source, the slopes are not as great, indicating less

microplume activity, and therefore less variability at these distances. Similar

frequency distribution plots generated for the other experimental days showed similar

results.

For different averaging times (ranging from 1 to 120 min; see Appendix A,

Figure A3), the biggest difference seen in the frequency distributions was near the

lower extreme (below 5 %) – the lowest concentrations showed a smaller range with

greater averaging times. The slope of the distribution was smaller at the highest

averaging time, but still large. This result indicated that variability in exposure to a

continuously emitting indoor source persists even for averaging times of hours, due in

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26

large part to periodic shifts in the preferred direction of air motion within the home (as

shown in Figure 2.3a, b).

Figure 2.4b represents an experiment in Home #2 during which the data

logging limit was much less frequently exceeded (air change rate = 2.1 h-1

). Here,

there appear to be two lognormal segments for each of the distances – above 5%

cumulative frequency, where the contribution of the random microplumes is stronger,

each of the frequency distributions is nearly lognormal (based on straightness,

). However, the lowest 5% of the concentrations show a much steeper slope

and quite similar values regardless of distance.

In both Figures 2.4a and 2.4b, the slopes of the frequency distributions

decrease at greater distances from the source, indicating that the variability is

decreasing as distance from the source is increasing In addition, in each home, the

frequency distributions for the two distances farthest from the source (that is, closest

to the walls) resemble each other.

2.4.3 Proximity Curves

Figures 2.5 and 2.6 show proximity curves: the normalized CO concentration

( ), with the ratios radially averaged as a function of distance

from the emitting source for each of the experiments in both homes. These graphs

show that the ratio is smallest (closest to unity, the well-mixed prediction) at the

greatest distance from the source. For the experiments in which the source was moved

above or below the plane of the monitors, the proximity curves were flatter, again

demonstrating that little vertical mixing was occurring.

The effect of increasing natural ventilation on the proximity curves is shown in

Figure 2.5. For both homes, the proximity curve became more pronounced as the

ventilation rate increased by opening windows. For the highest air change rate (1.25 h-

1) measured in the first home (Figure 2.5a), an average 5-min exposure at 0.25 m from

the source was 21 times higher than predicted, while a 5-min average exposure at 5 m

was close to that predicted by the well-mixed model (Equation 1). At the lowest air

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change rate (0.17 h-1

), the proximity curve was noticeably flattened. Cheng et al., (in

preparation, 2010) saw an opposite trend when averaging over just the first 30 min of

the source. As the source continues to emit the background concentration (the mass

balance model) contributes a larger portion to the total concentration, especially for

the lower air change rate. Also, with a lower air change rate, there is less mixing,

leading to higher concentrations near the source in the first 30 minutes. With a higher

air change rate, There is not as much time for mixing to occur before ventilation

removes the pollutant, therefore concentrations are higher near the source. Forced air

ventilation (not shown) increased the rate of mixing in the room, leading to further

flattening of the proximity curve (more uniform concentration in the room).

In Home #2 (Figure 2.5b), a qualitatively similar trend was seen with

ventilation rate – the highest ratio (12.8) at 0.25 m from the source occurred at the

highest air change rate (5.4 h-1

), and the lowest ratio occurred at the lowest air change

rate (0.19 h-1

). Results from Home #2 (Figure 2.5b) also show that the proximity

curve changed between day and night (labeled as “N” in Figure 2.5b). Two

experiments were conducted at the same source height and window position (shown as

solid and open triangles); one during the day and one at night. The nighttime air

change rate was one-fourth the daytime air change rate (confirmed by lower wind

speeds measured outdoors), and the shape of the resulting proximity curve changed.

Under comparable ventilation rates, the magnitude of the proximity effect closest to

the source in the second home was only a little more than half of that seen in the first

home (which had the single-sloped ceiling). However, the two homes differed in a

few other ways, so it is not possible to attribute these differences in the proximity

effect to specific home characteristics. One possibly important difference could be the

opening of windows in the same room as the source and monitors in Home #2 to

achieve the desired air change rates. In Home #1, the windows used to control air

change rate were in adjacent rooms.

Our results are similar to those of Furtaw et al., (1996) who found that, at

arm’s length distance from the source (0.4 m), the ratio of measured to predicted was,

Page 45: characterizing personal exposure

28

on average ~2:1 (with the measured concentration ranging from nearly that predicted

by the mass balance model to several times the predicted concentration). These

experiments were conducted with SF6 in a chamber at very high air change rates (10 –

49 air changes per hour) controlled using an HVAC system. While these rates are

high, they are typical of industrial settings with mechanical ventilation. In our

experiments with much smaller air change rates of 0.19 – 5.4 h-1

(the range of typical

air change rates in California homes is 0.5 – 2.0 h-1

; Wilson et al. 1996); we found at

arm’s length (0.5 m), ratios ranging from 5:1 – 14:1.

The shape of the proximity curves were also affected by the height of the

emissions relative to the heights of the sampling points. While all monitors were set up

to sample at a height of 1 m, the source was moved to 3 different positions above and

below the monitors to measure vertical mixing and the resulting effect on proximity.

In Figure 2.6, the proximity curves for the different source heights are compared with

the Base Case of a 1-m source height (solid triangle). When the source was moved to

a height of 1.25 m (0.25 m above the monitors; open triangle), the proximity curve

was greatly flattened, and a similar result was also observed when the source was

moved 0.5 m below the source. This supports our finding that the spread of emissions

is greater in the horizontal direction than in the vertical direction. The average

horizontal rate of spread measured from the SF6 emissions was 1.02 m/min in Home

#1 and 1.48 m/min in Home #2. Vertical rate of spread was not measured. Two

temperature sensors, placed 2.4 m apart vertically, showed that both homes were

stably stratified (+0.5˚C/m in Home #1, and +0.05˚C/m in Home #2), which would

tend to inhibit vertical mixing (Ludwig, 2010).

For a source height 0.25 m below the monitors (open circle), the emissions still

produced a pronounced proximity curve (although not as dramatic as the Base Case).

This result indicates that emissions of the tracer gas may initially spread vertically up

to 0.25 m above the source release height, but further vertical spread (to 0.5 m above

and 0.25 m below) is markedly inhibited. These findings are consistent with those of

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29

McBride et al. (1999), who found the greatest proximity effect within 0.5 m above for

the same source.

As was true in McBride’s experiments, CO released from our single point

source was not heated, the exit velocity was small (~0.04 m/s), and the indoor

temperature profile was stratified, so only a small amount of momentum-driven plume

rise was expected. Thus, as would be expected for these types of experiments, the

maximum plane of exposure was close to the height of the source. In contrast, a

burning cigarette generates a buoyant (hot) plume, with a plume rise observed to be

approximately 0.5 m in Home #1. For cigarette smoke emitted into a stratified

environment, the maximum plane of exposure would be around the effective plume

height, which would include the buoyant plume rise.

Klepeis et al. (2009) reported that the CO concentration measured outdoors at

different distances r from a point source dropped as approximately r -1

– this would be

expected under conditions where convective flow due to wind dominates. For a

specific cluster of microplumes moving in a sustained direction away from the source,

our results resemble these outdoor findings. But more generally, for most of our

indoor measurements, the trend in CO concentration with radial distance showed a

more gradual decrease (roughly r -0.9

to r -0.4

, average of r -0.6

).

With these data, it is also possible to explore the relationship between

measured and predicted concentrations during those time periods when a “cluster” of

microplumes is moving along a measurement axis. As was evident in Figure 2.3, a

cluster of microplumes moving along one axis of the array can be followed from the

monitor closest to the source all the way out to the last monitor. In Figure 2.7, we

evaluated the proximity effect for the migrating clusters seen at the beginning of this

experiment (between 10:30 and 13:53) in Figure 2.3a, and in the middle of the day

(14:05 – 15:50) in Figure 2.3b. For the cluster evident in Figure 2.3a, at 0.25 m from

the source, a person’s 5-min exposure would be almost 50 times as high as the well-

mixed prediction, and about 4 times as high as the result found by radially averaging

over all 4 monitors.

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2.5 Conclusions

For a source emission timescale similar to the exposure time scale of

interest, in an indoor environment, the well-mixed mass balance model can

underpredict the exposure of a person very close to the source (< 1 m) by as

much as 10 fold.

The magnitudes of sporadic spikes in pollutant concentrations

(“microplumes”) that occur close to indoor emission sources can be

represented to a good approximation as lognormal distributions.

Modeling exposure close to a continuous point source in a home is possible by

summing 3 variables: (1) the indoor concentration caused by infiltration of

outdoor air into the home; (2) the home’s well-mixed (background)

concentration due to the indoor source, evaluated deterministically from the

mass balance model, and (3) the contribution of the concentrated fresh

microplumes from the source, represented by a random variable sampled from

a lognormal distribution.

Vertical mixing of source emissions inside homes occurs on a much slower

time scale than the horizontal dispersion, leading to a narrow range of heights

near source release height with especially elevated concentrations.

References

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Particulate Matter: Case Study Results for PM2.5 in Philadelphia, PA, Journal

of Exposure Analysis and Environmental Epidemiology, 11(6), 470-489

Cheng, K. C., Acevedo-Bolton, V., Jiang, R. T., Klepeis, N. E., Ott, W. R., and

Hildemann, L. M. (2010) Model-based reconstruction of the time response of

electrochemical air pollutant monitors to rapidly varying concentrations.

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Ferro, A. R., Klepeis, N. E., Ott, W. R., Nazaroff, W. W., Hildemann, L. M., and

Switzer, P. (2009) Effect of interior door position on room-to-room differences

in residential pollutant concentrations after short-term releases. Atmospheric

Environment, 43, 706-714.

Ferro, A. R., Kopperud, R. J., and Hildemann, L. M. (2004) Elevated personal

exposure to particulate matter from human activities in a residence. Journal of

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Furtaw, E. J., Pandian, M. D., Nelson, D. R., and Behar, J. V. (1996) Modeling indoor

air concentrations near emission sources in imperfectly mixed rooms. Journal

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Hartwell, T. D., Perritt, R. L., Pellizzari, E. D., and Michael, L. C. (1992) Results from

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Klepeis, N. E. (1999) Validity of the uniform mixing assumption: Determining human

exposure to environmental tobacco smoke. Environmental Health

Perspectives, 107, 357-363.

Klepeis, N. E., Gabel, E. B., Ott, W. R., and Switzer, P. (2009) Outdoor air pollution

in close proximity to a continuous point source. Atmospheric Environment, 43,

3155-3167.

Klepeis, N. E., Nelson, W. C., Ott, W. R., Robinson, J. P., Tsang, A. M., Switzer, P.,

Behar, J. V., Hern, S. C., and Engelmann, W. H. (2001) The National Human

Activity Pattern Survey (NHAPS): a resource for assessing exposure to

environmental pollutants. Journal of Exposure Analysis and Environmental

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Steinemann, A.C., Wallace, L.A. eds., Exposure Analysis, CRC Press, Taylor

& Francis, Boca Raton, FL, Chapter 19, 445-470.

Ludwig, F. L. (2010) personal communication.

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indoor air pollution and related sink effects, 263-278.

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Nelson, W.C., Ott, W.R, and Robinson, J.P. (1994) National Human Activity Pattern

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Rodes, C.E., Kamens, R., Wiener, R.W. (1991) The significance and characteristics of

the personal activity cloud on exposure assessment measurements for indoor

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Wallace, L.A. and Gordon, S. M. (2007a) Exposure to volatile organic compounds.

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Press, Taylor & Francis, Boca Raton, FL, Chapter 7, 147-179.

Wallace, L.A. and Smith, K.R. (2007b) Exposure to particles. In: Ott, W.R.,

Steinemann, A.C., and Wallace, L.A., eds., Exposure Analysis, CRC Press,

Taylor & Francis, Boca Raton, FL, Chapter 8, 181-199.

Wallace, L. (1996) Indoor particles; a review. Journal of the Air and Waste

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Wilson A. L., Colome S. D., Tian Y., Becker E. W., Baker P. E., Behrens D. W.,

Billick I. H., Garrison C. A. (1996) California residential air exchange rates

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and residence volumes, Journal of Exposure Analysis and Environmental

Epidemiology, 6, 311-326.

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Figure 2.1 Floor plan of the two homes in study with array of CO monitors used. Black dots

represent CO monitor locations while a black star in middle of array represents the CO and SF6 sources.

Positive X and Y axes are shown on each array.

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Figure 2.2: Time series plot showing how our measured data compares with the well-mixed model’s

prediction. The dotted line represents the theoretical ambient CO concentration infiltrating from

outdoors (very low), while the dashed line shows the well mixed prediction for a given air change rate.

The solid line shows the CO concentration measured at the given air change rate at 0.25 m from the

source. During the source emission period ( ), the measured data can be much greater than the well-

mixed predicted concentration.

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Figure 2.3 Time series plots of each monitor along the long axis of the array in Home #1, starting

with the closest monitor and moving away from the source. This figure shows that clusters of

microplumes that are biggest in duration and magnitude closest to the source diminish as they move

farther away. This figure also shows the preferred direction of motion changes throughout the day.

Figure 2.3a shows the time series plots for monitors along the negative X-axis, while Figure 2.3b shows

monitors along the positive X-axis.

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Figure 2.4a Logarithmic-probability graph of the incremental exposure (measured CO concentration

with the well-mixed model’s prediction subtracted), showing the frequency distribution of the

microplumes. The lines are nearly straight below 70%, showing that the microplumes can be modeled

as lognormal distributions. The parallel lines indicate that while the median and average values decrease

as one moves farther away from the source, the variability (reflected by the slope) is similar. This data

was measured at Base Case air change rate (0.5 h-1

) and height conditions, but at half the normal

emission rate (~700 cc/hr). This plot is representative of our results.

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Figure 2.4b Incremental exposure distributions for an experiment in Home #2 with air change rate of

2.1 h-1

, source and monitor 1 m from ground. Above 5%, the distributions are fairly

straight, indicating a tendency towards lognormality. These lines are not as parallel as in

Figure 2.4 a. For this experiment, both the median and the variability decrease as one

moves away from the source.

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Figure 2.5 This figure shows the effect of ventilation conditions on concentration versus distance in

Home #1 (a) and Home #2 (b). The y-axis shows the normalized CO concentration (i.e.

), and the x-axis shows distance from the source. As the air change rate

increases, the proximity curve is more pronounced. “N” denotes experiments done at night.

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Figure 2.6 This figure shows how the proximity curve is affected by source height relative to

monitor height. This graph shows that there is a maximum plane of exposure (in this case, at source

height) and that the pollutant takes a long time to mix vertically.

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Figure 2.7 This figure shows the proximity curve for a short (1-2 h) cluster of microplumes. For a

specific cluster of microplumes moving away from source, the concentration at 0.25 m from the source

can be 4 times as high as when the results are averaged radially.

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Chapter 3: Controlled Experiments Measuring

Personal Exposure to PM2.5 in Close Proximity to a

Smoker

VIVIANA ACEVEDO-BOLTON, WAYNE R. OTT, KAI-CHUNG CHENG, RUOTING JIANG,

NEIL E. KLEPEIS, AND LYNN M. HILDEMANN

3.1 Abstract

Few measurements of the exposure to secondhand smoke (SHS) in close

proximity to a smoker are available. We performed 41 experiments indoors and

outdoors, in gaming casinos, and in everyday locations with 2 – 4 nonsmokers sitting

near a cigarette smoker, to measure the smoker’s and nonsmokers’ real-time exposures

to SHS. The smoker and nonsmokers were equipped with TSI SidePak monitors to

measure PM2.5 concentrations in their breathing zone. In residential indoor

environments with natural ventilation, the average exposure of nonsmokers sitting

within 1 m of the smoker was highly variable, ranging from near-background levels up

to as high as 420 g/m3. The smoker received the highest average exposure for the

majority of the experiments with concentrations ranging from 60 – 600 g/m3. In

general, nonsmokers were exposed to PM2.5 levels that were 5 times the indoor

background concentration (< 10 g/m3) for the duration of the cigarette. The exposure

level of the smoker to the SHS was, on average, 5 times as high as the nonsmokers’

exposures.

3.2 Introduction

EPA's Total Exposure Assessment Methodology (Hartwell et al., 1992;

Ozkaynak et al., 1996) studies, assessing the exposures of large populations, showed

that the concentrations of volatile organics and of particulate matter (PM) measured

using a personal monitoring device are often significantly higher than stationary

measurements collected simultaneously indoors or outdoors. Rodes et al. (1991)

theorized that there were at least 3 causes of this “personal cloud”: (i) a proximity

effect (due to time spent close to sources; Furtaw et al., 1996); (ii) for PM, a

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resuspension effect (due to particles resuspended from surfaces and/or from clothing;

Ferro et al., 2004); and (iii) for indoor environments, a compartmental effect (due to

higher pollutant levels in the room containing the source, e.g. Ferro et al., 2009).

McBride et al. (1999) found that human activity near a source may also affect the

location and magnitude of the highest concentrations. However, only a few studies

have quantified exposure to air pollutants for situations when people are close to a

source.

For outdoor environments, Ott (1971) used personal monitors to assess the CO

exposure of pedestrians in proximity to motor vehicle emissions on downtown city

streets, and more recently Ogulei et al. (2007) measured ultrafine particles at distances

up to 300 m from a major highway. Repace (2005) measured PM2.5 concentrations

inside a ring of smokers sitting outdoors, and found that concentrations dropped

approximately inversely proportional to downwind distance from a cigarette. Klepeis

et al. (2007) used a single continuous particle monitor mounted on a tripod to measure

PM2.5 concentrations near a smoker sitting outdoors at a patio cafe. They found that

PM2.5 concentrations, averaged over the duration of the cigarette, ranged from 240

g/m3 at 0.5 m to 20 g/m

3 at 4 m from the smoker.

For indoor environments, the few studies published to date have all utilized

tracer gases to characterize the proximity effect, using a point source placed near the

center of the room. Furtaw et al., (1996) measured concentrations of a tracer gas

(SF6) every minute at distances up to 1.6 m from a point source in a mechanically

ventilated space. They found a source proximity effect within the first meter of the

source (i.e. concentrations within the first meter from the source were higher than

predicted by the well-mixed mass balance model). Another tracer gas study (McBride

et al., 1999) found a source proximity effect within the first two meters under natural

ventilation conditions in a home. This study also found a source proximity effect for

particles within the first meter from the source. A study by Acevedo-Bolton et al.,

(2010, Chapter 2 of thesis), measured tracer gas concentrations at 37 locations within

5 m of an active point source in a naturally ventilated home (air change rates of 0.19 –

5.4 h-1

). This study found that concentrations measured at 0.25 m (averaged radially

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44

over 4 monitors) from the source were up to 20 times the concentration predicted by

the mass balance model. They also found that in the indoor environment, there was a

preferred direction of motion that influenced the direction the pollutant moved, for

time scales ranging from a few minutes to an hour or more. In this preferred direction,

concentrations were up to 50 times the predicted concentration at 0.25 m from the

source.

To the best of our knowledge, this is the first study to assess personal

exposures close to a smoker in indoor settings. By having nonsmokers sit close to the

smoker, and including source locations that are not near the center of the room, this

study can assess to what extent the observations made by Acevedo-Bolton and

coworkers in the absence of human movement can be generalized to more realistic

everyday situations.

In the present investigation, we modified the approach initially used by Klepeis

et al (2007) to the indoor residential environment as well as outdoors, examining a

number of typical activities. We simultaneously measured exposures for 3-5

collaborating participants, each wearing a continuous PM2.5 monitor with the intake

mounted in the person's breathing zone. For the first time, the smoker's exposure to

SHS was measured as well, allowing comparisons between the smoker's exposure and

the exposure of persons located near the smoker.

The objectives of this study were to (1) measure nonsmokers’ exposure to SHS

in close proximity to the smoker in real-time, along with the smoker’s exposure; (2)

examine the effect of location (within a room) on personal exposure; (3) determine if

sitting near a smoker increases a person’s exposure to secondhand smoke even in a

smoky environment; and (4) compare indoor and outdoor exposures to SHS. This

chapter will focus on the indoor experiments, with comparisons to some of the outdoor

experiments.

3.3 Methodology

A total of 41 field experiments were conducted, encompassing a variety of

indoor and outdoor locations where the public might be exposed to SHS. For each

location, we measured the SHS exposure of the smoker, along with 2 – 4 nonsmokers

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45

sitting close by, using a TSI AM510 SidePak™ (TSI, Shoreview, MN) monitor

equipped to measure PM2.5 concentrations. Each member of the group wore a special

necklace constructed for this study to position the intake tubing of the monitor within

0.3 m of the breathing zone (the nose and mouth; see Figure 1).

For indoor experiments, 1 – 2 stationary indoor monitors (SIMs) were placed

in the same room to measure the concentrations far from the source. During each

smoking event, the smoker was careful to not exhale directly into his monitor’s intake

to ensure that only exposure to the cloud of SHS would be measured. SidePak internal

clocks were set before the start of each experiment using a precision clock. A tape

measure was used to measure the distance of each participant or monitor from the

smoker. An anemometer was used to measure and log the relative humidity,

temperature, and wind speed (outdoor only).

Each of the 3-6 SidePak laser photometers used was laboratory-calibrated by

comparing its PM2.5 mass readings for SHS, generated using a real smoker, with a

filter-based gravimetric method (Jiang et al, 2010). Each monitor utilized its own

custom calibration factor, which ranged from 0.24 to 0.32. These settings were

comparable to the SidePak calibration setting of 0.295 reported by Lee et al (2008) for

cigarette smoke. The data logging frequency of the SidePak monitor was set to either

1 s or 10 s, to obtain adequate precision relative to the starting and ending times of

each cigarette. Cigarette start and end times were recorded on a log sheet.

For each experiment, the smoker smoked one or two cigarettes, with enough

time for mixing and some decay (at least 30 – 45 min) in between cigarettes. Prior to

the second cigarette, the room was usually aired out to bring PM2.5 concentrations

back down near pre-cigarette levels. No other indoor sources were active during these

experiments. Background concentrations were measured for 5 minutes prior and

subsequent to any cigarette.

As shown in Table 3.1, there were 19 indoor and 22 outdoor experiments, each

involving several different types of situations, for a total of 41 experiments. In each

experiment, 1-2 cigarettes were smoked, for a total of 55 cigarettes. This study will

focus on the 19 indoor experiments. To create a robust data set for analysis, all the

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experiments for a given type of situation were grouped together in order to assess

exposure levels as a function of distance from the smoker.

Table 3.1: Summary of Indoor and Outdoor Personal Exposure Experiments

Type # of Experiments # of People

Indoor

Couch 5 3 – 4 (+1-2 SIMs)

Dining table 3 4 (+1-2 SIMs)

Card table in garage 4 4 (+1 SIM)

At a casino 7 5

Outdoor

Patio table 9 4-5

At a bus stop 8 4-5

Park bench 2 4

Miscellaneous* 3 4-5

Total 41

* Miscellaneous experiments were: (1) walking with a smoker, (2) waiting at a train station,

and (3) on the balcony of a 2-story apt building with smoker on the 1st floor.

The indoor experiments consisted of three main types: (1) Sitting at a table

with a smoker, (2) sitting on a couch with a smoker, and (3) sitting while playing slot

machines at a casino. Figure 3.2 (a-d) shows the layouts used for these experiments in

a home in Redwood City, CA (Home #1). This home has been the subject of many

studies (e.g. McBride et al., 1999; Howard Reed et al., 2002; Ferro et al., 2004;

Acevedo-Bolton, et al., 2010). In the first type, we used a dining table in the dining

room of two houses, and a card table in the closed garage Home #1. Air change rates

(h-1

) were not measured, but window positions utilized were expected to achieve air

change rates of 0.5 to 1 h-1

based on previous studies involving the first home

(Howard-Reed et al., 2002; Acevedo-Bolton et al., 2010). Experiments in casinos

were done covertly, with SidePak monitors hidden in the researchers’ purses or jacket

pockets with the intake tube sticking out. These were the only experiments for which

the intake was not within 0.3 m of the breathing zone.

The three types of outdoor experiments were: (1) Sitting at an outdoor table

(patio, restaurant, picnic table), (2) at a bus stop on a busy highway, and (3) at a park

bench. Many of the outdoor experiments were done in the vicinity of Home #1 in

Redwood City. Only preliminary results from some of the outdoor experiments will

be discussed in this chapter, as a comparison to the indoor experiments.

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3.3.1 Frequency Distributions

To analyze the effect of a person’s location (smoker, to the right of smoker,

etc.) on exposure, grouped data for each type of experiment can be plotted as a

frequency distribution on a logarithmic-probability plot. For lognormal distributions,

the logarithm of the concentration versus the integral of the normal probability density

function will be linear. Lognormal distributions are commonly seen for outdoor

environmental measurements involving the dilution of pollutants (Ott, 1995).

The slope and intercept of the straight line fit of a distribution on a log-

probability plot can be used to calculate the mean () and standard deviation () of the

lognormal model fit to the data (the fundamental parameters, Ott, 1995). Because we

are plotting on a log10 scale, we must multiply the parameters of the model fit of the

distribution by loge10 (or ln(10)) to get and , respectively. (If we plotted the

distribution on a natural logarithm (base e) probability plot, then the slope and

intercept would be the fundamental parameters and .) From and , we can

calculate the geometric mean (g) and geometric standard deviation (g) as:

(1)

(2)

The geometric mean and standard deviations are the parameters used to

describe the lognormal model fit to the data.

3.3.2 Statistical Analyses

For the summary statistics shown in the tables, individual (monitor)

background concentrations were subtracted from the source period averages to focus

incremental exposure due to proximity to the source. The background averages

presented in the tables are the average of the individual background concentrations for

each experiment. Summary statistics, frequency distribution plots, and calculations

were performed using SigmaPlot software Version 11.0 (Systat Software, Inc., San

Jose, CA).

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3.4 Results

3.4.1 Indoor Table Experiments

For the 8 indoor table experiments, 3 nonsmokers sat with a smoker at a table

as shown in Figure 3.2(a), with one person across the table from the smoker, and the

other two nonsmokers to the right and left of the smoker (nonsmokers sat 0.5 – 1.5 m

from the smoker). One cigarette was smoked during each experiment. Five of these

experiments were conducted in the garage of Home #1, two in the dining room of

Home #1, and one in the dining room of Home #2. Table 3.2 gives the average

concentration each monitor measured for the duration of the cigarette (5 – 10 min) for

each of the 8 experiments.

In these experiments, the smoker received the highest exposure. The location

of the nonsmoker with the highest exposure, and the magnitude of that exposure,

varied from one experiment to the next, as the preferred direction of movement for the

emissions varied. On average for the 8 experiments, the exposure to PM2.5 for

nonsmokers sitting at the table was >10 times as high during active smoking as it was

during the pre-experimental time period. Exposure of the nonsmokers to SHS was

roughly 1/4th

to 1/5th

that of the smoker.

Table 3.2: Mean PM2.5 Exposures (above background) of 3 Persons Sitting with a Smoker for the

Indoor Table Experiments (g/m3; averaging time = cigarette duration)

Experiment

Number Smoker

Nonsmokers (1 – 1.5 m)

SIM (>4 m)

Pre –

Cigarette

Background Across Left Right

D.1.1 171.0 10.5 7.3 11.6 0.0 1.3

D.1.2 58.0 5.3 0.0 8.0 2.1 3.4

D.2.1 108.6 71.8 81.8 --b --

a 8.3

G.1.1 422.9 21.6 79.4 84.6 --a 8.9

G.1.2 196.2 108.0 --b 133.9 --

a 5.8

G.1.3 374.8 164.4 33.7 49.4 25.5 1.6

G.1.4 177.5 22.2 22.2 48.8 --b 0.4

G.1.5 281.4 27.5 53.2 71.1 13.1 0.4

Overall

Average: 223.8 53.9 39.7 58.2 10.2 3.8

SD: 126.6 56.6 32.9 43.7 11.7 3.5

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“G” refers to experiments performed in a garage; “D” refers to a dining room. The first number in the

experiment number is the home. For example: G.1.2 was the second (2) experiment conducted in the

garage (G) of Home #1 (1). a not measured,

b measurements not recovered due to monitor failure

Inside the dining room, for the 8 – 9 min duration of the cigarette, the SHS

emissions did not reach the SIM monitor placed >4 m away from the source (for

Experiments. D.1.1 and D.1.2); instead, the measured levels at this monitor remained

at or very close to background concentrations. In contrast, for the garage experiments,

the nonsmokers were exposed to much higher concentrations of PM2.5, and more

noticeable elevations were also seen in the monitor placed 4 m away, near a side door.

In these experiments, there were more sizeable window/door openings (the side door

was open 4 cm, and the garage door was open 6 cm). Air change rate and air speed

were not measured, but based on the door openings, we expected the air change rate to

be higher than in the dining room (Acevedo-Bolton et al, 2010). The larger

concentrations measured 4 m away and the difference in exposures between the

nonsmokers suggest that there was a preferred direction of air motion from the garage

door towards the side door (the person sitting between the smoker and the side door

received a higher exposure than the other nonsmokers) . If further experiments were

done, measures of air change rate would be valuable for further analysis.

Figure 3.3 shows the pooled frequency distributions (data for each person, for

each experiment was combined into one data set) for each position used for indoor

“sitting at a table” experiments. Each line in the frequency distribution shows the

pooled 10 s average concentrations measured at that position for up to 8 cigarettes.

PM2.5 was measured with a sampling frequency of 1 s or 10 s, so all the 1 s data were

averaged to 10 s for consistency. As shown in Figure 3.3 and in Table 3.3, the smoker

had the highest mean and median concentration (257, 113 g/m3). The people sitting

right and across from the smoker (NS1 and NS3) have similar mean and median

exposures (60, 25 g/m3), while the person to the left (NS2) has a slightly lower mean

exposure (45, 20 g/m3). However, the three nonsmokers have very similar frequency

distributions. This shows that the exposures for three people sitting around a table with

a smoker (0.5 – 1.5 m away) over several cigarettes were very similar. Using the

Mann-Whitney Rank Sum Test (in SigmaPlot), the difference in median

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concentrations between the three nonsmokers is not enough to be statistically

significant at 95 % confidence level (R to L, p = 0.193; R to A, p = 0.997; L to A, p =

0.074).

Table 3.3: Pooled summary statistics for “Sitting at a table” experiments for 4 people and 1 SIM (10-s

averages)

n MEAN MEDIAN STD. DEV. MAXIMUM MINIMUM

(g/m3)

SMOKER 372 256.7 113.1 426.6 5106.0 0.1

RIGHT (R) 316 60.1 26.0 148.6 1562.4 0.2

LEFT (L) 324 45.1 19.8 57.0 491.9 0.0

ACROSS (A) 372 62.3 24.5 158.0 2050.5 0.2

SIM 183 11.2 3.7 19.4 111.4 0.2

BACKGROUND 1190* 2.1 1.1 2.7 28.0 0.0

* Background from all the experiments combined.

The smoker received the highest 10-s peak concentration of over 5000 g/m3,

while the person sitting across (NS3) from the smoker received the second highest

peak exposure of ~2000 g/m3. This high peak concentration was likely from

mainstream (exhaled) smoke rather than from sidestream smoke. The experiments

showed that the median background (pre-cigarette levels, 1.1 g/m3) and the median

SIM (3.7 g/m3) concentrations were significantly lower than the nonsmokers’ median

exposures (19.8 – 26.0 g/m3) and much lower than the median personal exposure of

the smoker (113.1 g/m3). This result indicates that during the source period, the

majority of measured concentrations are from the cigarette. The summary statistics

presented in Table 3.4 are slightly different than those in Table 3.3: all 10-s data points

are equally weighted in Table 3.4, while Table 3.3 presents averages per cigarette

smoked, which does not account for differences in cigarette smoking durations.

On a logarithmic probability plot, a straight line fit to the frequency

distributions of the 10 s average concentrations gives r2 values > 0.95 (Table 3.4),

indicating that the distributions can be estimated as lognormal (see Figure 3.3). The

parameters of the lognormal model were estimated by fitting a line to the data using

least-squares regression analysis. The geometric standard deviations (GSD) of the

three nonsmokers and the smoker are large and comparable, ranging from σg = 4.2 –

5.4. The high variability of these distributions indicates that all participants were

exposed to both concentrated microplumes and “clean” pockets of air. The lines are

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almost parallel to each other, showing that exposures differ mostly in magnitude. The

median (μg) concentrations found from the lognormal distributions indicate that the

nonsmokers’ median exposure was at least 5 times the median SIM concentrations,

while the smoker’s median exposure was 4 – 5 times the nonsmokers’ median

exposure.

In a previous study, Acevedo-Bolton et al. (2010) used CO as a tracer gas for

secondhand smoke to measure the proximity effect. They found that at 0.25 m from

the source (an estimate of the smoker’s exposure) the average concentration was 1.5 –

2 times as high as the average concentration at 0.5 m from the source (an estimate of

the nonsmokers). The average concentration at 0.5 m was 2 – 6 times the

concentration at the monitor farthest from the source (5 m, SIM). These 2-fold

differences between “smoker” and “nonsmoker” for the CO tracer gas study were

lower than the 4 to 5-fold differences seen here.

Table 3.4: Parameters Describing the Lognormal Model Fit to the

Observed Frequency Distributions of the 10-s Averages for Indoor

Table Experiments with a Smoker

Data Lognormal Model

Median n r2 g g

(g/m

3)

(g/m

3)

SMOKER 113.1 372 0.98 97.5 4.81

RIGHT 26.0 316 0.98 22.0 4.18

LEFT 19.8 324 0.96 16.6 5.37

ACROSS 24.5 372 0.98 20.9 4.90

SIM 3.7 183 0.97 3.98 4.18

BACKGROUND 1.1 1190* 0.98 1.02 3.63

3.4.2 Indoor Couch Experiments

To determine whether the location within a room had an impact on people’s

exposures to SHS, we performed a series of experiments on the living room sofa of

Home #1. Figure 3.2 (b) shows a top view of the layout of these experiments. As

shown in Figure 3.2 (b), the couch was against the wall of the living room, adjacent to

the dining room. Table 3.5 shows the average concentrations measured during the

active cigarette period for the “sitting on a couch with a smoker” experiments. For

each of these experiments, three people sat on a couch (a smoker in the middle and

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one nonsmoker on each side), and one person sat in a detached chair on one side. For

the majority of experiments, there were 1 – 2 stationary monitors (Stationary Indoor

Monitor, SIM) placed in front of the smoker at 1 – 2 m and another at over 4 m from

the smoker. The distance between study participants on the couch was approximately

0.7 m with the smoker in the middle. The person seated in the detached chair was 2 m

from the smoker.

Table 3.5: Mean PM2.5 Exposures (above background) of 4 Persons Sitting with a Smoker for Couch

Experiments (g/m3; averaging time = cigarette duration)

Experi-

ment

Smoker

Nonsmokers

(NS) on Couch (1 m)

SIM

(2 m) (>4 m)

More Distant

SIM

Pre-Cigarette

Background Left Right SIM

NS -

Left

1.1 173.7 0.2 121.6 42.7 1.8 --a 0.9 7.8

1.2 266.7 25.6 134.8 316.4 57.9 --a 10.2 11.6

1.3 632.0 1.4 7.3 --b --

b 1.7 --

b 1.0

1.4 406.1 4.9 2.3 4.0 --a 5.5 2.3 2.1

1.5 300.1 0.8 0.2 1.9 --a 0.4 2.1 27.0

1.6 386.2 5.7 4.1 40.5 --a 2.2 20.8 1.9

Overall

Mean: 360.8 25.7 81.1 29.9 2.5 7.3 4.9

c

SD: 157.4 48.4 132.9 39.7 2.2 8.4 4.6c

a not measured,

b measurements not recovered due to monitor failure,

c excludes Experiments 1.5

Table 3.5 shows the concentrations above the pre-cigarette background level

that each participant and SIM measured during each of the couch experiments.

Experiments 1.1 and 1.2 differed greatly from the rest of the experiments – one of the

nonsmokers on the couch (the person to the right of the smoker) and the SIM placed 1

m in front of the smoker were exposed to very high average concentrations during the

cigarette, whereas the other nonsmoker (person to the left of the smoker) and the other

two SIMs (2 m and > 4 m away) were exposed to concentrations close to the pre-

experimental background levels (measured before the cigarette was lit). Clearly,

during these experiments the air near the smoker tended to move to the right of the

smoker. Other experiments done in this home (Acevedo-Bolton et al., 2010) have

found that small, coherent plumes of emissions (“microplumes”) can move in a

sustained direction for timescales ranging from minutes up to an hour or more, before

changing direction.

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In the second set of experiments in Home #1 (1.3 – 1.6), we added another

nonsmoker sitting in a detached chair to the left of the couch about 2 m from the

smoker. For each of these experiments, the 3 nonsmokers sitting near the smoker were

exposed to concentrations just slightly above the background concentration during the

cigarette, indicating they had little exposure to microplumes. Instead, the SIM that was

out in front and slightly to the left of the smoker (1 – 1.25 m away) measured higher

concentrations and more microplumes during the active cigarette. Figure 3.4 (a, b)

shows typical time-series plots for one of these later experiments (Experiment 1.6).

Figure 3.4 (a) shows the concentrations the nonsmokers and the far away SIM

were exposed to during the cigarette source period, along with a few minutes before

and after. The cigarette source period is denoted by the dashed blue lines at 2:37 pm to

2:45 pm. Figure 3.4(a) shows that even though the average exposure concentrations

for the 3 nonsmokers were only 2 – 6 g/m3 above the background level, many

microplumes passed by them while the cigarette was smoked. The person to the left

of the smoker was exposed to many microplumes (magnitudes of 30 – 80 g/m3)

between 2:39 pm and 2:43 pm; after that time, that person’s exposure returned to

background levels. Then, starting at approximately 2:44 pm, the person to the right

was exposed to a cluster of microplumes of 90 – 120 g/m3. The SIM measured close

to background concentrations until the last 3 min of the source period when it

measured a sustained cluster of microplumes that resulted in an elevated average

concentration of 20.8 g/m3. This figure also shows that the monitors did not measure

an increase in the background concentration (from the cigarette emissions mixing

within the room) until the last 3 minutes of the source period.

Figure 3.4 (b) shows the time series plot for the same experiment for the

smoker and the SIM 1 m in front of the smoker with a larger vertical scale than Figure

3.4(a) to accommodate the higher concentrations. During this source period, the

smoker was exposed to the highest and greatest frequency of microplumes, several of

magnitude > 3000 g/m3, resulting in an average exposure of 386.2 g/m

3. The SIM

measured the next highest average concentration for this experiment (20.8 g/m3). The

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largest microplume measured by the SIM was a long (30 s) period of elevated

concentration, reaching a maximum of over 1000 g/m3.

For Experiments 1.3 – 1.5, all windows and doors in the home were closed,

aiming to achieve an air change rate of around 0.2 – 0.3 h-1

; in contrast, in

Experiments 1.1 and 1.2, the windows were set to achieve an air change rate of 0.3 –

0.6 h-1

, based on previous studies utilizing this home (Howard-Reed et al., 2002;

McBride et al. 1999; Acevedo-Bolton et al., in 2010). The air change rate may have

had an influence on the drift direction for the SHS. However, for Experiment 1.6,

even though we opened the windows to the positions used in Experiment 1.1, the

results on this day resembled the experiments performed with the windows closed.

This similarity could be due to expected variations in the air change rate, due to

factors such as indoor and outdoor temperature differences (Howard-Reed et al.,

2002). These unexpected results stress the need to measure air change rate for every

experiment, since the window position does not guarantee the same air change rate

every time. Experiment 1.5 was performed in series with Experiment 1.4 and

background levels had not fully decayed at the start of 1.5; hence the background

concentration was higher. Subtracting this background concentration from the

measured concentrations resulted in a similar outcome to the other experiments with

low starting background concentrations; that is, the nonsmokers sitting on the couch

with the smoker were not exposed to higher concentrations during the cigarette source

period.

As seen in Figure 3.2 (b), the couch was up against a wall of the house,

limiting the directions of movement for the cigarette emissions. Sometimes, they

drifted toward the middle of the room, causing lower exposures for the nonsmokers

sitting directly next to the smoker. Other times they drifted parallel with the wall,

passing near one of the nonsmokers (as in Experiment 1.1 and 1.2). At other times, it

appears that the SHS drifted in directions where there were no monitors or

nonsmokers.

The last 4 couch experiments had results similar to the dining room table

experiments from Home #1 (shown in Table 3.2). The nonsmokers in these

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experiments were not exposed to elevated levels of SHS during the smoking event.

This could be a result of variations in the direction of air movement within Home #1

during these experiments. This could be due to differences in air change rate from the

first two couch experiments. Differences in air change rates, mixing characteristics

affect pollutant movement and mixing, and where the highest concentrations are

measured. The first two couch experiments resemble the table experiments performed

in the garage in which one nonsmoker was exposed to higher levels than the other

nonsmokers. This finding suggests (since we did not measure air change rate) that

conditions were similar for these two sets of experiments. In both the couch and table

experiments, the smoker was exposed to the highest average concentrations.

3.4.3 Casino Experiments

All the experiments presented thus far were carried out in relatively clean

environments with background concentrations of < 10 g/m3. We also performed 7

experiments in two smoky California casinos to assess the magnitude of the proximity

effect in a place where the background concentration was already high. In a study of

36 CA Indian casinos by Jiang et al. (2010), the average PM2.5 concentration inside

casinos was 63 g/m3

(this average concentration is slightly lower than the average

concentration measured in the casinos where the majority of these proximity

experiments were done).

Compared to the other indoor experiments which were done in homes using

only natural ventilation, the mechanical ventilation systems in the casinos result in

much higher air change rates (in compliance with ASHRAE, the American Society of

Heating, Refrigerating, and Ventilation Engineers standards), causing mixing that

likely affected the cigarette plume and where it traveled. During many of the casino

case studies, we observed greater plume rise (> 1 m) for the cigarette smoke than what

was typically observed in homes (~0.5 m). Under ASHRAE Standard 62-2001, the

default maximum air exchange rate for a casino at maximum design occupancy is 15

air changes per hour (h-1

) (Repace et al., 2010). For the first casino, WinRiver Casino

in Redding, CA (WR), the HVAC was designed to deliver air change rates of 4 to 16

h-1

(according to the building engineer). In the second casino, San Pablo Casino in

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San Pablo, CA (SP), we estimated air change rates from indoor and outdoor CO2

levels and patron counts. The average air change rate over a 24 hour weekend period

was 10.7 h-1

at varying occupancy rates.

Since the experiments in casinos were performed covertly (with the exception

of WR.1), female researchers carried the SidePak monitor in a purse, while male

researchers carried them in a jacket pocket, with the monitor inlet within 0.5 m of the

researchers’ breathing zone. Thus, these results may be affected by the vertical

position of the monitor inlet relative to the smoker. For the majority of the

experiments, the inlets were near waist height (seated), which is significantly below

the smoker’s mouth. The cigarette was held near waist height close to the monitor in

between puffs which could have contributed to the microplumes measured by the

people on either side of the smoker.

During each casino study, the nonsmoking researchers sat at the slot machines

on either side of the smoker. In most cases, the smoker sat in the middle with two

researchers on either side as shown in Figure 3.2 (c). In one case, the smoker sat with

three people on one side and one on the other (Figure 3.2 (d)), while a third set-up

involved two people standing 0.5 m behind the smoker on either side and two more

sitting on either side (Figure 3.2 (e)). At WinRiver casino, we open permission by the

management to do an air quality study and therefore were able to use the necklaces

(shown in Figure 3.1) to measure personal exposures very close to the breathing zone.

Figure 3.5 (a, b) shows time series plots from experiment # SP.5 (see Table

3.7). While the cigarette increased everyone’s average exposure (above background),

only the people to the immediate left or right of the smoker were exposed to

microplumes that had a large effect on their average exposure (shown in Figure 3.5

(a)). The person to the left (shown in black) received the most microplumes and

therefore the highest average exposure during the cigarette (61.9 mg/m3). The person

to the right of the smoker (shown in red) received the second highest exposure (32.4

mg/m3), as well as the second-highest number of microplumes. The other two

investigators (shown in Figure 3.5 (b)), seated two seats to the right or left of the

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smoker on either side, received no large microplumes during the source, but their

average concentration increased 14.3 and 17.0 g/m3 above background level.

The smoker’s overall exposure (background and source period) tended to be

lower than everyone else’s, possibly because of how the smoker’s monitor was carried

(inside breast pocket of jacket with short piece of Tygon tubing sticking out through

front, slightly above waist-level). This is not evident from the table shown because

the table shows incremental exposures above individual average background

concentrations. The two experiments in casinos where this was not true were at

WinRiver, where we used the custom-made necklaces, and SP.3. In WinRiver, the

smoker had the highest incremental exposure (101.2 g/m3), while the three

nonsmokers had similar incremental exposures to each other (~22 g/m3

above the

background concentration). The smoker did not receive the lowest exposure for most

of the other indoor experiments discussed in this chapter.

Table 3.6: Mean Personal Exposure to PM2.5 (above background) of 4-5 Persons at a Casino (g/m3;

averaging time = cigarette duration)

Experiment # Smoker Next to smoker 2 seats from smoker Overall

Background Right Left Right Left

WR.1 101.2 23.9 21.9 22.4 --a 33.6

SP.1* 8.1 5.4 101.8 23.3, 0 --

a 75.2

SP.2**

19.7 30.0 6.3 57.8 --b 63.8

SP.3 92.6 19.4 4.7 10.3 3.5 79.4

SP.4 --b 191.7 --

b --

b 0.4 88.1

SP.5 2.3 32.4 61.9 14.3 17.0 50.2

SP.6 11.5 19.1 12.9 10.5 5.2 62.6

Average 39.2 46.0 34.9 19.8 6.5 64.7 a not measured;

b measured less than background;

* Plotted in Figure 3.2 (d); **

Plotted in

Figure 3.2 (e).

The average increase in exposure above background level was 35 – 45 g/m3

for the 2 people sitting at the slot machines next to the smoker, and 10 – 20 g/m3 for

the people sitting 2 seats to the left or right of the smoker. These results are

comparable to our results from the indoor table studies in which the people sitting

within 1 m of the smoker were exposed to approximately 50 – 60 g/m3 above

background concentrations. For the people sitting 2 seats away from the smoker (~2

m away) in the casino experiments, the increases in concentration above background

levels resemble the SIM measurements (> 4 m away) for the indoor table experiments.

These findings suggest that the increase in exposure due to SHS within ~1 m of the

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smoker is similar whether the room is mechanically or naturally ventilated. However,

the proximity effect at ~2 m away from the source may be more pronounced in

naturally ventilated indoor environments than in locations with high air exchange rates

and/or mechanical ventilation.

3.4.4 Outdoor Table Experiments

Experiments with a smoker and nonsmokers seated at a table were also

performed outdoors. Figure 3.6 shows frequency distributions of the 10-s averages

for 6 outdoor smoking events at a table with one smoker and 3 – 4 nonsmokers sitting

across, to the right and left of the smoker (around a circular table). There were a few

more experiments where the researchers were seated at a table with a smoker, but the

layout was slightly different than the indoor experiments (rectangular table with two

people across from the smoker, and one person next to them). Discussion here will

focus on the circular layout, where comparisons with the indoor results are most

appropriate.

The frequency distributions for the smoker and 3 nonsmokers appear to be the

combination of two lognormal distributions, one similar to the distribution for ambient

(background) concentrations (< 7 g/m3), then a sharp increase in the slope due to

exposure to microplumes (Figure 3.6). A similar pattern was seen in the individual

experiments (not shown). The flatter portion of the frequency distribution is most

likely due to the significant influence of ambient concentrations in the outdoor studies

– that is, a larger percentage of the 10 s averages were close to ambient levels, which

did not change due to the cigarette emissions. The frequency distribution for the

background measurements (measured pre and post cigarette) has an interesting,

nonlinear shape. The abrupt increase shown at the 99.9% is likely from post-cigarette

measurements where the cigarette was put out but some smoke was still produced.

The source of the deviation at the lowest concentrations is unknown.

To better understand exposures from the cigarette in the outdoor environment,

background concentrations were subtracted and the new distribution plotted on a log-

probability plot shown in Figure 3.7. A best fit line was applied to each distribution by

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linear regression analysis and resulted in high r2 values (> 0.92), showing that these

outdoor distributions, like the indoor measurements, can be estimated as lognormal.

The slope and intercept of the best fit lines were used to calculate the parameters of

the lognormal fit to each person and are shown in Table 3.7.

Table 3.7: Parameters Describing the Fit of the Lognormal Model to the

Observed Frequency Distributions of the 10-s Averages (above background)

for Outdoor Table Experiments with a Smoker

Data Lognormal Model

Mean Median n r2 g g

SMOKER 102.9 19.95 190 0.95 13.5 12.9

RIGHT 19.5 1.94 190 0.95 1.5 20.9

LEFT 28.2 1.95 190 0.92 1.8 9.3

ACROSS 18.5 1.60 190 0.97 1.8 11.5

BACKGROUND 3.6 3.90 763 0.83 3.0 1.9

In their study of outdoor tobacco smoke levels in a backyard patio, Klepeis et

al. (2007) found average concentrations over the duration of each cigarette of 60

g/m3 at 0.5 m; 20 g/m

3 at 1 m; 10 g/m

3 at 2 m; and 5 g/m

3 at 4 m. In our outdoor

table experiments, the nonsmokers sitting about 1 m from the smoker were exposed to

an average of 20 – 40 g/m3 over the duration of the cigarette. Similar to the indoor

table experiments, the smoker’s median exposure (31 g/m3) was 5 – 6 times as high

as the nonsmokers’ exposures (5 – 6 g/m3).

In the outdoor experiments the smoker and nonsmokers were exposed to a

lower median concentration than for the indoor experiments. Higher 10-s peak

concentrations were measured indoors (~5000 g/m3 indoors vs. 1500 g/m

3 outdoors

for the smoker; 500 – 2000 g/m3 indoors vs. 600 – 800 g/m

3 outdoors for the

nonsmokers). For the outdoor experiments, the smoker had the highest median

concentration but the lowest GSD (variability).

This denotes that the smoker was exposed to microplumes more frequently

than the nonsmokers, and experienced the highest microplume concentrations. In

contrast, for the indoor experiments, the smoker had the highest median concentration,

but the variability was similar among all participants (i.e. all participants were exposed

to microplumes with similar frequency). Similar to the indoor experiments, the

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difference in the median values are not big enough to be statistically significant for the

three nonsmokers in the outdoor experiments (SigmaPlot: Kruksal-Wallis One Way

ANOVA on Ranks, p = 0.131).

Lower peak concentrations outdoors probably resulted from high advection

from the wind (greater wind speed); it is likely, due to the wind, that the highest peak

concentrations more often missed the monitors worn by the nonsmokers. Higher wind

speed will also lower the effective plume height of the cigarette, which may have an

effect on the concentrations reaching the nonsmokers’ breathing zone. In the outdoor

environment it is also likely that there is more vertical mixing (turbulence), causing

the plumes to dilute faster than in the indoor environment. In an indoor environment

with low ventilation, the plume travels more slowly and concentrated microplumes

have a greater chance of reaching the people sitting near the smoker.

3.5 Summary and Conclusions

This study is the first to measure the exposures of both smoker and

nonsmokers in close proximity to the smoker. The proximity effect has been shown to

exist in previous studies using tracer gases (e.g. McBride et al., 1999, Klepeis et al.,

2009), but no studies to date have measured the indoor exposure of smokers and

nonsmokers to SHS in realistic field settings. In this study, we successfully measured

this exposure and found the following:

For many situations in which one would be near a smoker in an indoor

environment (such as a home), there was a measurable proximity effect, with

average exposure levels at 1 m of 50 – 60 g/m3 above background while the

cigarette was being smoked.

The smoker typically was exposed to the highest concentrations of SHS with

average levels ranging from 60 – 630 g/m3 above background concentrations.

At lower ventilation rates, people sitting indoors, roughly equidistant from the

smoker received similar exposures (with a few exceptions), while at higher

ventilation rates, there was a greater difference in exposures between

nonsmokers.

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For the outdoor experiments of persons seated at a table next to a smoker, the

average personal exposures above the background concentration were lower

than for the indoor environment, but the microplumes were greater (or similar)

in magnitude.

The exposures to SHS in close proximity to a smoker showed large variability,

but the frequency distributions of 10-s average exposures were close to

lognormal with similar geometric standard deviations.

Even in a casino, where background levels of SHs were quite high, the

proximity effect was still strong enough to significantly elevate exposures at

distances within 1 m of the smoker.

References

Acevedo-Bolton, V., Cheng, K.C., Jiang, R.T., Ott, W.R., Klepeis, N.E., Hildemann,

L.M. (2010) “The Effects of Proximity on Exposure: Beyond the Uniform

Mixing Assumption for an Active Indoor Point Source”, Chapter 2 of Ph.D.

Dissertation.

Ferro, A. R., Kopperud, R. J., and Hildemann, L. M. (2004) Elevated personal

exposure to particulate matter from human activities in a residence. Journal of

Exposure Analysis and Environmental Epidemiology, 14, S34-S40.

Ferro, A. R., Klepeis, N. E., Ott, W. R., Nazaroff, W. W., Hildemann, L. M., and

Switzer, P. (2009) Effect of interior door position on room-to-room differences

in residential pollutant concentrations after short-term releases. Atmospheric

Environment, 43, 706-714.

Furtaw, E. J., Pandian, M. D., Nelson, D. R., and Behar, J. V. (1996) Modeling indoor

air concentrations near emission sources in imperfectly mixed rooms. Journal

of the Air & Waste Management Association, 46, 861-868.

Hartwell, T. D., Perritt, R. L., Pellizzari, E. D., and Michael, L. C. (1992) Results from

the 1987 Total Exposure Assessment Methodology (TEAM) study in Southern

California. Atmospheric Environment Part A-General Topics, 26, 1519-1527.

Howard-Reed, C., Wallace, L. A., and Ott, W. R. (2002) The effect of opening

windows on air change rates in two homes. Journal of the Air & Waste

Management Association, 52, 147-159.

Jiang, R.T., Cheng, K.C., Acevedo-Bolton, V., Klepeis, N.E., Repace, J.L., Ott, W. R.,

Hildemann, L.M. (2010) Measurement of fine particles and smoking activity in

a statewide survey of 36 California Indian casinos, Journal of Exposure

Science and Environmental Epidemiology, 7 February 2010;

doi:10.1038/jes.2009.75

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Klepeis, N.E., Ott, W.R., and Switzer, P. (2007) Real-time measurement of outdoor

tobacco smoke particles, Journal of the Air and Waste Management

Association, 57 (5), 522-534.

Klepeis, N. E., Gabel, E. B., Ott, W. R., and Switzer, P. (2009) Outdoor air pollution

in close proximity to a continuous point source. Atmospheric Environment, 43,

3155-3167.

Lee, K., Hahn, E.J., Pieper, N., Okuli, C.T.C., Repace, J.L., Troutman, A. (2008)

Differential Impacts of Smoke-Free Laws on Indoor Air Quality, Journal of

Environmental Health, 70 (8), 24-30.

McBride, S. J., Ferro, A. R., Ott, W. R., Switzer, P. and Hildemann, L. M. (1999)

Investigations of the proximity effect for pollutants in the indoor environment.

Journal of Exposure Analysis and Environmental Epidemiology, 9, 602-621.

Ogulei, D., Hopke, P.K., Ferro, A.R., and Jaques, P.A. (2007) Factor analysis of

submicron particle size distributions near a major United States-Canada trade

bridge, Journal of the Air and Waste Management Association, 57 (2), 190-

203.

Ott, W.R. (1971) "An Urban Survey Technique for Measuring the Spatial Variation of

Carbon Monoxide Concentrations," Ph.D. Dissertation, Dept. of Civil and

Environmental Engineering, Stanford University.

Ott, W. R. (1995). Environmental statistics, and data analysis. CRC Press, Inc.; CRC

Press.

Özkaynak, H., Xue, J., Spengler, J., Wallace, L., Pellizzari, E., and Jenkins, P. (1996)

Personal exposure to airborne particles and metals: Results from the particle

team study in Riverside, California. Journal of Exposure Analysis and

Environmental Epidemiology, 6, 57-78.

Repace, J (2005) Measurements of outdoor air pollution from secondhand smoke at

UMBC, report for the UMBC University Health Services

Repace, J.L., Jiang, R.T., Acevedo-Bolton, V., Cheng, K.C., Klepeis, N.E., Ott, W.R.,

and Hildemann, L.M. (2010) Fine Particle and Secondhand Smoke Air

Pollution Exposures and Risks Inside 66 US Casinos, Environmental Research

(under revision)

Rodes, C.E., Kamens, R., Wiener, R.W. (1991) The significance and characteristics of

the personal activity cloud on exposure assessment measurements for indoor

contaminants, Indoor Air 2, 123-145.

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Figure 3.1 Line Drawing showing the necklace designed to keep the inlet of the SidePak monitor

within 0.2 m of the person’s breathing zone.

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Figure 3.2 Layout used for the indoor experiments in Home #1 and the casinos. The two areas

labeled (a) show the dining room and garage table experiments; (b) shows the couch experiments. (c) –

(d) show the configurations used in the casinos.

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Figure 3.3 Frequency distributions of the pooled “sitting at a table” indoor experiments. Up to 8

cigarettes are represented in each distribution. Background concentrations were measured for 5 min

prior to each smoking event. Best fit lines are drawn in.

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Figure 3.4 Time-series plots for couch Experiment #1.6: (a) Time series plots for the nonsmokers

and the far away SIM, and (b) Time series for the smoker and the SIM in front of him. The y-axis in (a)

is magnified to show more details. The cigarette source period is between the dotted blue lines.

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Figure 3.5 Time-series plots of casino Experiment SP.5. The cigarette source period is between the

dotted blue lines: (a) Two people sitting at slot machines to the left and right of the smoker, and (b) 2

people sitting 2 seats away from the smoker received few, low magnitude microplumes during the

source period. Their exposure was only slightly higher than background concentrations.

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Figure 3.6 Frequency distributions of the pooled “sitting at a table” outdoor experiments. Each of the

distributions (except BACKGROUND) has two distinct slopes: one similar to background (ambient)

concentrations and one much higher, from exposure to cigarette emissions starting at about 7 g/m3.

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Figure 3.7 Frequency distributions of the pooled “sitting at a table” outdoor experiments (same as

Figure 6) with the background concentrations subtracted for the smoker and 3 nonsmokers. The best fit

lines were estimated by least squares regression to show how a lognormal distribution fits the data.

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Chapter 4: Real-time Measurements of PM2.5 in Close

Proximity to an Indoor Particle Source

VIVIANA ACEVEDO-BOLTON, KAI-CHUNG CHENG, RUOTING JIANG, NEIL E. KLEPEIS,

WAYNE R. OTT, AND LYNN M. HILDEMANN

4.1 Abstract

To evaluate how proximity to a real combustion source affects personal

exposure in indoor locations, we performed 17 experiments using 3 different particle

sources (a smoldering cigarette, a burning incense stick, and a smoked cigarette). Each

source emitted from a point in the center of a room, while an array of 14 – 16 particle

monitors measured PM2.5 concentrations with 1-s time resolution within 3 m of the

source. The height of the source was varied relative to the monitors to measure how

the strength of the proximity effect changed. The height of maximum impact differed

between a smoldering source and a smoked cigarette, due to the exhaled mainstream

smoke having a different effective plume height. Average concentrations at source

height were significantly higher for a smoked cigarette than for a smoldering source.

For a smoldering cigarette, the average concentration 0.2 m above the source was 2 –

3 times the concentration at source height.

4.2 Introduction

A number of studies have shown that pollutant concentrations measured using

a personal exposure monitor are consistently higher than those measured by a

stationary monitor located in the person’s home (Rodes, et al., 1991; Ott, 1995;

Wallace, 1996; Ferro et al., 2004, Wallace et al., 2007b). This phenomenon is called

the personal cloud. EPA’s Particle Total Exposure Assessment Methodology

(PTEAM) study (Özkaynak et al., 1996) described the personal cloud as an “excess

mass” near a person that the EPA investigators hypothesized was related to personal

activities. They determined that smoking and cooking emissions were two major

sources of elevated indoor particle concentrations. One important cause suggested for

the personal cloud is the close proximity of the person wearing a monitor to an

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actively emitting source (Rodes et al., 1991, Özkaynak, et al., 1996). This proximity

effect may occur if the person is in the same room as the source; for example, an

individual sitting next to a smoker, or a cook near a gas stove. Higher pollutant levels

near a source are caused by non instantaneous mixing of the emissions into the

surrounding air.

Due to the phenomenon of the proximity effect, a person closer to a pollution

source receives a greater exposure than a person farther away. A better understanding

of the proximity effect, characterizing the complex relationship between

concentrations and distance from the emitting source, is important for more accurately

assessing the relationship between exposure levels and health effects. The proximity

effect is especially important for understanding a person’s exposure during the active

source period ( ) described by Ott (1995). During this period while the source is

emitting, short duration, high concentration peaks, or microplumes (McBride et al.,

1999, Klepeis et al., 2009, Acevedo-Bolton et al., 2010 [Chapter 2 in thesis]) can

contribute substantially to a person’s exposure, especially if the person is very close to

the source. Real-time measurements are important for understanding the spatial and

temporal variability of microplumes (McBride et al., 1999), and for quantifying the

proximity effect (Acevedo-Bolton et al., 2010 [Chapter 2 in thesis]).

Previous tracer gas studies have shown an important source proximity effect.

Furtaw et al. (1996) measured SF6 concentrations at different distances from a point

source in a mechanically ventilated space and found that the concentrations measured

at 0.4 m from the source were 2 times the concentration predicted by the well-mixed

mass balance model. McBride et al. (1999) measured both CO and SF6 concentrations

in a naturally ventilated home and found that a proximity effect existed within 2 m

from the source. A more recent study (Acevedo-Bolton et al., 2010) found that

radially-averaged CO concentrations at a horizontal distance of 0.25 m from a point

source for averaging times of 6 h could be up to 20 times the well-mixed prediction in

a naturally ventilated home during the active source period.

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McBride et al. (1999) also measured indoor particle counts for several sizes of

particles and particle-bound PAHs from burning incense. They found a pronounced

proximity effect for fine particles (0.5 – 2.5 m); concentrations measured at 1.0 m

from the source were 3 times the concentration measured at a stationary indoor

monitor (SIM) located far away (5.4 m) from the source. In addition, they found that

human activity (walking around) increased the concentration of larger particles (from

resuspension), and also affected the location and magnitude of microplumes for all

sizes of particles.

This study builds upon the CO array discussed by Acevedo-Bolton et al.

(2010) and the CO and particle measurements made by McBride et al. (1999) by using

a real combustion source to generate particles, measuring at 14 – 16 locations within 3

m of the source simultaneously, and by characterizing in detail the vertical variations

in the close-proximity particle concentrations. Using real-time particulate matter (PM)

monitors, which can quantify concentrations spanning 4 orders of magnitude, we can

obtain greater vertical resolution than was possible using CO monitors. This study also

goes beyond previous work by comparing the proximity effect for a smoldering

particle source with emissions from a real smoker.

In this study, we designed a multi-monitor particle array to test the following

hypotheses regarding the proximity effect: (1) Concentration levels and the frequency

of microplumes will be higher near the effective plume height; (2) For a smoked

cigarette, exhaled mainstream smoke concentrations will peak near the exhalation

height, whereas sidestream smoke will rise higher up; smoker movements (hand) will

also affect peak concentrations; (3) Exhalation direction (and behavior) of the smoker

will have an effect on the vertical distribution of concentrations near the smoker; (4)

Over the time scale of a single cigarette being smoked, there will tend to be a preferred

direction of smoke drift – one or two directions around the source will be most

affected by high concentrations and microplumes; and (5) For very high air change

rates, the dispersion behavior will resemble outdoors.

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4.3 Methodology

A total of 17 particle experiments were performed in a small, one-room (46.4

m3) lab space on the Stanford University campus to measure the source proximity

effect. The room had two doors and a small window (41 cm x 41 cm) on one side.

Figure 4.1 shows the layout of the lab space including the two doors and small

window. A second set of 15 experiments was done in a bedroom of a home in

Watsonville, CA. This bedroom was 30 m3 in volume, with one window opposite the

door. This chapter will focus on the 17 lab space experiments; only one of the

experiments done in the Watsonville home will be discussed.

4.3.1.Array

PM2.5 concentrations were measured at 4 different heights (over different

experiments) relative to a real particle source – 0.2 m, 0.4 m above the source; 0.2 m

below the source; and level with the source. 14 – 16 AM510 SidePak™ monitors

(TSI, Inc. Shoreview, MN) equally spaced 0.4 m from each other in four directions (0,

90, 180, 270 degrees) up to 1.2 m, with additional pairs of monitors at 1.6 m and 3 m

from the source at 0 and 180 degrees (Figure 4.1). The SidePak monitors were

configured to measure PM2.5 concentrations every second. Data were averaged to 10 s

to obtain adequate precision for the starting and ending times of each cigarette and to

minimize instrument noise. The SidePak monitors were previously calibrated against

gravimetric filter samples for cigarette smoke and incense to find a custom calibration

factor for each source and instrument. For cigarette smoke, the individual calibration

factors varied from 0.24 to 0.32, whereas for incense, the factors varied from 0.3 to 0.4

(Jiang et al., 2010).

The source was moved to different heights (one height per experiment) relative

to the monitors to measure the proximity effect at different heights. For the

Watsonville home, we analyzed one experiment examining the vertical proximity

effect directly. In the experiment done at the Watsonville home discussed in this

chapter, incense was used as the source and 12 SidePak monitors were placed 0.25 m

(horizontal distance) from the source at heights of 0.5 m below the source, 0.5 m

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above the source and 1 m above the source, in 4 directions (0, 90, 180, and 270

degrees).

4.3.2 Sources

The 3 types of sources tested for these experiments were incense, a smoldering

cigarette (lit, but not smoked), and a cigarette smoked by a smoker. Each smoked

cigarette lasted 5 – 7 minutes, each smoldering cigarette lasted approximately 10

minutes, and each incense stick burned for 15 – 30 minutes. Both incense and a

smoldering cigarette generate a single, buoyant plume that rises noticeably. However,

for a smoked cigarette, there is also exhaled mainstream smoke, which has very little

buoyant plume rise. We expected that this would result in differences in the height

with the highest measured concentrations. We had the same smoker for all these

experiments, and we asked the smoker to smoke as he normally would. Mainstream

smoke was generally exhaled out in front of the smoker, or slightly down away from

the monitors. While not puffing, the cigarette was held at the knee when sitting, or at

mid-thigh (arm’s length) when standing.

4.3.3 Ventilation

To control ventilation in the lab space, 3 window/door positions were used –

“low”, with all windows and doors closed (ACH ranged from 0.17 – 0.52 h-1

); “high”,

with both doors open wide; and “medium”, with all doors closed but one small

window open 15 cm x 41 cm (ACH = 0.41 h-1

). In the Watsonville home, the

bedroom door was kept closed during all experiments, and the window was opened to

different widths to control the ventilation.

Air change rate was measured by releasing CO and measuring the

concentrations after the combustion source was extinguished. The slope of the CO

decay curve plotted on a semi-log plot was used to calculate the air change rate. CO

can be assumed to be nonreactive over the hours-long timescales of these experiments,

so decay in a well-mixed indoor environment will follow a first-order exponential

decay where the air change rate, a is the decay rate constant.

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If we take the natural log of both sides we get the following linear equation:

This equation can be plotted on a normal scale as a straight line where a (air change

rate) is the slope of the line (decay curve). When CO was released during the one

“open doors” experiment, the decay lasted just 7 minutes and yielded an air change

rate of > 33 h-1

.

Table 4.1. Summary of Particle Proximity Experiments

Monitor height

relative to source

Target Ventilation

Rate

Sources Total number

of experiments C SC I

0.2 m below low X 1

Level low, high, medium X X X 10

0.2 m above low, high X X X 3

0.4 m above low, high X X 3 C = smoked cigarette, SC = smoldering cigarette, I = incense

Table 4.1 is a summary of the 17 experiments done in the lab at the Stanford

campus. Most of the experiments (10) were performed with the monitors at the same

height as the source to test each source and each ventilation condition. Only one

experiment was done with the monitors below the source since we expected that

emissions would rise. Three experiments each were done with monitors at 0.2 and 0.4

m above the source.

4.3.4 Statistical Analyses

For much of the data analysis, data from all the monitors at a particular

distance from the source were pooled, or combined into one data set. This new data set

was then analyzed to examine behavior at that particular distance from the source.

Statistical analyses, box plots, time series plots, and other calculations were

performed using SigmaPlot software, Version 11.0 (Systat Software Inc., San Jose,

CA).

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4.4 Results

4.4.1 Source and Height Effects

Figure 4.2 shows the concentrations measured at 0.4 m (4 monitors at 0.4 m

radial distance) from the source for three experiments with a smoldering cigarette. In

the first two experiments (“Smolder 1” and “Smolder 2”), both the monitors and

source were placed 1 m from the ground. For “Smolder 3”, the monitors were raised

0.2 m above the source. During Smolder 1, the measured air change rate was 0.26 h-1

.

Smolder 2 and Smolder 3 were done two days later, and the measured air change rate

was 0.4 h-1

(measured during Smolder 2). All windows and doors were closed during

these experiments; the only difference between experiments was the date and that the

monitors were placed 0.2 m above the source for Smolder 3.

As expected, the highest average concentration (197 g/m3) was measured in

Smolder 3, where the monitoring height was closer to the effective plume height.

Smolder 1 and Smolder 2 had average concentrations of 65 g/m3 and 110 g/m

3,

respectively. The difference between Smolder 1 and Smolder 2 can probably be

attributed to the different air change rates measured during these experiments.

Maximum 10-s peak concentrations were highest for Smolder 3 (1900 g/m3) vs. 300,

900 g/m3 for Smolder 1 and Smolder 2, respectively. This result indicates the

effective plume height was above the source due to the buoyancy of the hot emissions

coming out of the smoldering cigarette. While plume rise was important for a

smoldering cigarette, we wanted to see if it was also important for the other two

sources measured.

To examine how different sources might change the effective plume height, we

conducted experiments for the three different sources (as shown in Figure 4.3). The

source and monitors were at the same level (1 m from the ground) and all doors and

windows were closed for all six experiments shown. The first two box plots show the

combined 10 s average data from the 4 monitors at 0.4 m radial distance from the

source for the two experiments with a real smoker. The next 4 box plots are the same

set of data for experiments with a smoldering cigarette and for incense.

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The much higher average PM levels seen in Figure 4.3 for the experiments

with the smoker reflect a difference in the effective plume height between the smoker

and the other two sources. This difference is probably due to the exhaled mainstream

smoke. For the smoldering cigarette and the incense, concentrations measured at

source height are lower than what is measured above the source, as was seen in Figure

4.2.

Mean and median concentrations at source height were the highest for the

smoked cigarette experiments with mean values of 1200 – 1600 g/m3 and median

values of 600 – 1400 g/m3. The highest 10-s peak concentrations of over 5500

g/m3 were also measured in these experiments. Peak concentrations were similar

between the two smoked cigarette experiments. Mean and median concentrations for

the smoldering cigarette and incense were at or below 100 g/m3, an order of

magnitude below what was measured for the smoked cigarette. Peak concentrations

for these two sets of experiments ranged from 200 – 1000 g/m3 for both sources,

showing that microplumes were still measured, but were not frequent enough to affect

the average concentration.

From the smoldering cigarette experiments shown in Figure 4.2, we confirmed

that peak concentrations were located above the source. With a smoked cigarette, we

expected that the exhaled mainstream smoke would have the largest effect on the

concentrations measured at source height. Thus, for 5 of the experiments, we left the

monitors at 1 m above the floor, but asked the smoker to sit in different seats, or stand,

to place his breathing zone at 4 different heights. Thus, the monitors were 0.2 m below

the source, level with the source, 0.2 m above the source, and 0.4 m above the source.

Each box in Figure 4.4 shows the pooled concentrations measured at the 4 monitors

closest to the source (0.4 m).

At a horizontal distance of 0.4 m, the monitors below the source measured the

lowest mean and median concentrations (500, 330 g/m3; 1 experiment), while the

monitors at the smoker’s breathing height measured the highest mean and median

concentrations (1285 – 1612 g/m3, and 632 – 1375 g/m

3; 2 experiments). For the

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experiments with the monitors 0.2 m and 0.4 m above the source, the mean

concentration at 0.4 m above the source was significantly higher than the mean value

measured 0.2 m above the source, but the slightly higher median value measured at 0.2

m above the source was not statistically significant (Mann-Whitney Rank Sum Test).

These results show that exhaled mainstream smoke is important to consider for

determining effective plume height, and since the smoker is moving the cigarette to

and from his mouth, the source height (and therefore the effective plume height) of

sidestream smoke changes.

We hypothesized that mainstream (exhaled) smoke would have an impact on

the height at which the highest concentrations were measured. Previous studies have

found that mainstream smoke makes up 10 – 15% of secondhand smoke (e.g. Witschi

1997, Repace 2007). But exhaled mainstream smoke (emitted at the smoker’s

breathing height) has much less buoyancy because it has cooled to the smoker’s body

temperature (Repace, 2010), whereas sidestream smoke is hot and will rise to a height

determined by the thermal stratification in the room. If nearby individuals are

breathing at the same height as the exhaled mainstream smoke, the bulk of their

exposure in close proximity to the smoker should come from mainstream smoke. This

suggests that the composition of secondhand smoke inhaled in closer proximity to a

smoker will have a larger fraction of exhaled mainstream pollutants than the 10-15%

that might be assumed.

Figure 4.5 (a – e) shows the time series plots for the 4 monitors closest to the

smoker for the same 5 experiments as shown in Figure 4.4, where each plot represents

one experiment. In Figure 4.5 (a), the 4 monitors were 0.2 m below the smoker’s

breathing height. We expected to find few microplumes at this height – the emissions

from a cigarette are expected to rise because they are hotter than the surrounding air.

Exhaled mainstream smoke however should have cooled significantly (close to

smoker’s body temperature) and would not be expected to rise nearly as much as

sidestream smoke that is emitted from the tip of the cigarette at 350 ˚C (Baker et al.,

1990). In this experiment, the highest peaks (and highest average concentration) were

measured at the 90 degree angle (facing the windows). The higher concentration at

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this angle could be from sidestream smoke emitted while the smoker was holding the

cigarette on his left knee (~0.5 m below the smoker’s mouth) when not puffing. As

seen in Figure 4.5(a), the monitor at 90 degrees measured the most microplume

activity during this experiment. Measurements taken at 270 degrees from the source

resemble well-mixed conditions (a smooth increase with no microplumes).

The highest concentrations were measured when the smoker’s breathing height

was level with the monitors. From Figures 4.5 (b), (c) one can see that this result

coincides with the highest and most frequent microplumes, especially at 0 and 90

degrees. At these angles, the time series plots indicate sustained, high concentrations.

In contrast, many microplumes were measured in the two subsequent experiments

(Figure 4.5 (d), (e)), but concentrations between microplumes dropped down to

background, or “well-mixed” levels. These results are consistent with results from

previous experiments using CO as a tracer gas (Acevedo-Bolton et al., 2010), where

we found that the pollutant concentration is highest at the effective plume height and

remains concentrated within a narrow vertical band around this height. For CO (a

nonbuoyant source), the effective plume height was the same as the source height.

Outside of the narrow band, we measured a diminished proximity effect and less

frequent, lower magnitude microplumes.

During the first of the two smoked cigarette experiments shown in Figure 4.4

(2nd

and 3rd

box of the box plots) where the monitors were level with the source, the

smoker exhaled slightly down and away from the monitors (between 0 and 90

degrees), while in the second experiment, the smoker intentionally blew smoke at the

monitors in front of him (this was the only experiment in which the smoker exhaled

directly at monitors). The smoker was facing the 0 degree angle monitors for both of

these experiments. When averaged radially, higher mean and median concentrations

were measured at 0.4 m (horizontal distance) during the first smoked cigarette when

the smoker was exhaling downward and away from the monitors.

An explanation for these results is suggested by the individual time series plots

for these experiments (Figure 4.5(b), (c)). In Figure 4.5 (b), the smoker blows away

from the monitors and the result is high sustained concentrations in all directions

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around the smoker. Conversely, when the smoker blows directly at the monitor along

the 0 degree axis (Figure 4.5 (c)), that monitor gets much higher concentrations than

the other 3 monitors. The monitor at 90 degrees also measured some high

microplumes that could have come from sidestream smoke from the idling cigarette

(smoker held cigarette on left knee) for both experiments. The monitors behind the

smoker and to the right of the smoker recorded few (small) peaks during the cigarette.

These two experiments also show a clear difference in the monitors along the

axis the smoker was facing. In the first of the two experiments (Figure 4.6 (a)), when

the smoker was blowing smoke down and away from the monitors, the concentrations

decreased as the distance from the source increased along the 0 degree axis. The

concentrations measured at 0.4 – 1.2 m from the source were probably a combination

of sidestream smoke rising to the level of the monitors (since the cigarette was held at

the smoker’s lap when not puffing) and mainstream smoke. For the experiment where

the smoker blew towards the 0 degree axis of the array (Figure 4.6 (b)), the first two

monitors (0.4 m and 0.8 m) measured concentrations very similar to each other. After

0.8 m from the source, the concentrations dropped off quickly. This result suggests

that, for this type of exhalation behavior, the exhaled concentrations of mainstream

smoke may persist, with little further dilution, up to 1 m away from the smoker.

Based on previous research investigating the proximity effect (Acevedo-Bolton

et al., 2010) we determined that air flow in a room tends to move in one or two

preferred directions at a time (with changes throughout the day). A preferred direction

can last up to hours at a time. Averaging data from 4 monitors for each radial distance,

we may miss some important information about the preferred direction of motion and

how that affects the magnitude of the vertical and horizontal proximity effect. In one

experiment performed in a home in Watsonville, 12 monitors were placed 0.25 m from

the home in four directions and at three heights: 0.5 m below, 0.5 m above, and 1 m

above a stick of incense that burned for 30 minutes. Ventilation was not measured

during this experiment, but both the door and window were closed. In another

experiment with the window open 1 inch, the calculated air change rate was 0.6 h-1

, so

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it is expected that the air change rate with the window closed would have been less

than this.

When averaged radially and by height, we found no vertical proximity effect,

that is – the concentrations measured at the three heights varied by only 2 – 4 g/m3

(225, 227, and 229 g/m3, respectively). We expected to find a vertical proximity

effect since we could see the plume of smoke rising. Instead of averaging each height

over the four directions, we decided to see if there was a preferred direction of motion

that the plume was traveling along. Figure 4.7 (a – d) shows time series plots of

concentrations measured by the 3 monitors in each direction around the source. These

time series show the 10-s average concentrations at each monitor in each direction. At

0.5 m below the source (black line), the concentration follows the typical pattern

expected for a well-mixed volume, rising to an equilibrium concentration of ~230

g/m3 in all directions. Very few microplumes (up to 600 g/m

3) were measured in

the first 10 min of the experiment during the buildup to equilibrium concentrations.

Average concentrations over the 30-min source period ranged from 205 – 230 g/m3.

Higher up, 0.5 m above the source (shown in red), the concentration once

again followed the mass balance model prediction, but with many more deviations,

especially at 0 and 90 degrees. Average concentrations ranged from 210 – 330 g/m3,

with the highest at 0 degrees. The higher concentration measured at this angle is

consistent with the increase in microplume activity. Different magnitude microplumes

were seen in the different directions, with the highest peak (4900 g/m3) at 270

degrees, and none at 180 degrees. More frequent microplumes were measured at 0

and 90 degrees, with magnitudes of up to 2500 g/m3 and 1000 g/m

3, respectively.

At a height of 1 m above the source (shown in green), the average

concentration range was the largest (250 – 680 g/m3). From the time series plots,

one can see that the monitors at this height measured the most frequent and highest

magnitude microplumes. The highest average concentration was measured at 90

degrees, where microplume activity lasted more than 20 min with magnitudes ranging

from 1000 – 2600 g/m3. These findings suggest that the plume from the incense stick

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rose 0.5 – 1 m in this experiment, with the most activity occurring at 1 m above the

source at 90 degrees. These results show that there is a vertical proximity effect with

buoyant emission sources, with the height of the highest concentration reflecting the

effective plume height of the source.

The results from this experiment are consistent with results from our previous

work using CO as a tracer gas (Acevedo-Bolton et al. 2010). In the non-buoyant CO

experiments, we concluded that there was a preferred direction of motion within the

home, the direction of which varied throughout the day. More microplume activity and

higher concentrations were measured at the monitors along the preferred direction of

motion.

For a real smoker, there is a preferred direction of motion for two reasons: (1)

the exhalation of smoke in front of the smoker, and (2) the movement of air within a

room. Sidestream smoke will most be affected by air movement (currents in a room)

while mainstream smoke will be most influenced by the direction the smoker is

exhaling.

4.4.2 Effect of Ventilation

Table 4.2: Average concentrations in g/m3, measured

during a smoked cigarette for two ventilation

conditions

Radial Distance from Source

Door position 0.4 m 0.8 m 1.2 m 1.6 m

closed 1612.3 1111.2 889.2 1309.1

closed 1285.6 1543.3 1073.9 564.3

open 265.0 153.8 112.2 175.1

Table 4.2 shows the average concentration measured at a radial distances of

0.4 – 1.6 m (a total of 14 monitors) from the source (a smoker) for three experiments

where the monitors were level with the breathing height of the smoker. In the first two

experiments, the doors and windows were closed. In the third experiment, both doors

were open wide (see Figure 4.1). At every radial distance from the source, the

concentrations were much higher (4 – 10 times as high) for the experiments with the

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doors closed. With the doors open, the emissions were quickly removed from the

room. The air change rate was estimated at 33 h-1

; very large for a naturally ventilated

space. This is much higher than the typical air change rate measured in CA homes

(0.5 – 2 h-1

, Wilson et al., 1996), for example.

The difference in air change rates may lead one to imagine that the increased

concentrations measured with the doors closed are caused by the build-up of

background (well-mixed) concentrations. Figure 4.8 shows the time series plots for

each of the three experiments summarized in Table 4.2. The top 2 time series plots are

of experiments with the doors closed, and the bottom time series is a plot of the

experiment with the doors open. The black lines show the concentrations measured at

the monitor closest to the smoker’s breathing zone, while the red lines show the

concentrations measured at a more-distant monitor that measured closer to a well-

mixed background concentrations (and did not measure any microplumes). In the first

two plots, the average concentration measured by the background monitors are 309

and 321 g/m3 (the average background concentration measured during the last

experiment was 45 g/m3). These background concentrations are much too small to

explain the higher concentrations measured during experiments when the doors were

closed. Instead, the measurements from the monitor closest to the smoker reveal that

there was much more microplume activity measured by these monitors at the lower air

change rate. At the very high air change rate, there was likely more mixing from

turbulence, resulting in fewer and lower magnitude microplumes.

In a study of the proximity effect in outdoor settings, Klepeis et al. (2009)

found that CO tracer concentrations downwind of a point source dropped in inverse

proportion to distance from the source. They defined a proximity sensitivity

coefficient, ξ as the ratio of concentrations at two distances divided by the ratio of the

two distances,

where a value of “1” indicates that the concentration drops exactly by half as distance

from the source doubles. In the study of CO concentrations at different distances from

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a point source, Klepeis et al (2009) calculated ξ values for a single direction ranging

from 0.7 – 1.6 (average 1.2) at all the measured wind speeds. In this study, for the high

ventilation experiment shown in Figure 4.8, ξ values for a single direction ranged from

0.27 – 2.0, with an average value of 1.04. This result supports our theory that at high

air change rates, the proximity effect becomes similar to what has been found in

outdoor settings.

4.5 Summary and Conclusions

In this study, we examined the proximity effect for an active indoor particle

source, using an array of 14 – 16 monitors at different locations measuring PM2.5

concentrations simultaneously in a laboratory building. We tested 3 different sources:

a smoldering cigarette and an incense stick, representing stationary sources with no

human activity, and a smoked cigarette, to investigate the effects of smoker movement

and behavior on the dispersion. We varied source type, source height, and ventilation

to understand the effects of plume rise and ventilation on the proximity effect as a

function of height. From this study we concluded the following:

The biggest difference between a smoldering cigarette and a smoked cigarette

was the second effective plume height resulting from exhaled mainstream

smoke. For a smoldering source, the effective plume height was above the

source, while for a smoked cigarette, the exhaled mainstream smoke caused the

highest concentrations to be measured at the height of the smoker’s breathing

zone.

The occurrence of frequent microplumes substantially elevated the mean

concentrations for those monitors closest to the source at elevations close to

the effective plume height.

The composition of secondhand smoke that a person is exposed to depends on

their position relative to a smoker. In this study, the highest concentrations

were measured from exhaled (mainstream) smoke at around the same height as

the smoker’s mouth.

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At very high air change rates, the decrease in indoor concentration with

increasing distance is similar to what has been found outdoors.

References

Acevedo-Bolton, V., Cheng, K.C., Jiang, R.T., Ott, W.R., Klepeis, N.E., and

Hildemann, L.M. (2010) The Effects of Proximity on Exposure: Beyond the

Uniform Mixing Assumption for an Active Indoor Point Source. Chapter 2 in

Acevedo-Bolton’s Ph.D. Thesis.

Baker, R.R., and Proctor, C.J. (1990) The origins and properties of environmental

tobacco smoke. Environment International, 16, 231-245.

Ferro, A. R., Kopperud, R. J., and Hildemann, L. M. (2004) Elevated personal

exposure to particulate matter from human activities in a residence. Journal of

Exposure Analysis and Environmental Epidemiology, 14, S34-S40.

Furtaw, E. J., Pandian, M. D., Nelson, D. R., and Behar, J. V. (1996) Modeling indoor

air concentrations near emission sources in imperfectly mixed rooms. Journal

of the Air & Waste Management Association, 46, 861-868.

Jiang, R.T., Acevedo-Bolton, V., Cheng, K.C., Klepeis, N. E., Ott, W.R., and

Hildemann, L. M. (2010) Determination of response of real-time SidePak

AM510 monitor to common indoor and outdoor aerosols. Chapter 2 in Jiang’s

Ph.D. Thesis

Klepeis, N. E., Gabel, E. B., Ott, W. R., and Switzer, P. (2009) Outdoor air pollution

in close proximity to a continuous point source. Atmospheric Environment, 43,

3155-3167.

Klepeis, N. E., Nelson, W. C., Ott, W. R., Robinson, J. P., Tsang, A. M., Switzer, P.,

Behar, J. V., Hern, S. C., and Engelmann, W. H. (2001) The National Human

Activity Pattern Survey (NHAPS): a resource for assessing exposure to

environmental pollutants. Journal of Exposure Analysis and Environmental

Epidemiology, 11, 231-252.

McBride, S. J., Ferro, A. R., Ott, W. R., Switzer, P. and Hildemann, L. M. (1999)

Investigations of the proximity effect for pollutants in the indoor environment.

Journal of Exposure Analysis and Environmental Epidemiology, 9, 602-621.

Nelson, W.C., Ott, W.R, and Robinson, J.P. (1994) National Human Activity Pattern

Survey (NHAPS): Use of Nationwide Activity Data for Human Exposure

Assessment., EPA Report No. EPA/600/A94/147 prepared by Maryland

University, College Park, Survey Research Center, Enivornmental Protection

Agency, Research Triangle Park, NC

Ott, W. R. (1995a) Human exposure assessment: The birth of a new science. Journal

of Exposure Analysis and Environmental Epidemiology, 5, 449-472.

Ott, W. R. (1995b). Environmental statistics, and data analysis. CRC Press, Inc.; CRC

Press.

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Ott, W.R. (2007) Mathematical Modeling of Indoor Air Quality. In: Ott, W.R.,

Stienman, A.C., Wallace, L.A. eds., Exposure Analysis, CRC Press, Taylor &

Francis, Boca Raton, FL, Chapter 18, 411-444.

Ott, W. R., Klepeis, N. E., and Switzer, P. (2003) Analytical solutions to

compartmental indoor air quality models with application to environmental

tobacco smoke concentrations measured in a house. Journal of the Air & Waste

Management Association, 53, 918-936.

Repace, J.L. (2007) Exposure to Secondhand Smoke. In: Ott, W.R., Stienman, A.C.,

and Wallace, L.A. eds., Exposure Analysis, CRC Press, Taylor & Francis,

Boca Raton, FL, Chapter 9, 201 – 236.

Repace, J.L. (2010) Personal communication.

Rodes, C.E., Kamens, R., Wiener, R.W. (1991) The significance and characteristics of

the personal activity cloud on exposure assessment measurements for indoor

contaminants, Indoor Air 2, 123-145.

Wallace, L. (1996) Indoor particles; a review. Journal of the Air and Waste

Management Association, 46: 98-126.

Wallace, L.A. and Smith, K.R. (2007) Exposure to particles. In: Ott, W.R.,

Steinemann, A.C., and Wallace, L.A., eds., Exposure Analysis, CRC Press,

Taylor & Francis, Boca Raton, FL, Chapter 8, 181-199.

Wilson A. L., Colome S. D., Tian Y., Becker E. W., Baker P. E., Behrens D. W.,

Billick I. H., Garrison C. A. (1996) California residential air exchange rates

and residence volumes, Journal of Exposure Analysis and Environmental

Epidemiology, 6, 311-326.

Witschi, H., Joad, J.P., and Pinkerton, K.E. (1997) The Toxicology of Environmental

Tobacco Smoke, Annual Review of Pharmacological Toxicology, 37, 29-52.

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Figure 4.1 Layout of the lab space used in these experiments. The two doors and the one small

window used for ventilation are located towards the bottom of the figure. The monitoring array (with

the 0 degree angle) is shown as open circles with the source at the center (shown by the star). The door

to the small office remained closed for the experiments.

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Figure 4.2 Box plots showing the distribution of 10 s average concentrations from the 4 monitors at

0.4 m from the source. In these box plots, the edges of the box are the 1st and 3

rd quartile, and the

median is shown as the solid black line in the box. The mean concentration is shown as the dotted line.

The whiskers extend to the 10th

and 90th

percentile, while the dots show the 95th

and 5th

percentile

(outliers).

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Figure 4.3 Box plots showing the distribution of 10-s averages from the 4 monitors closest to the

source from 6 different experiments, measuring concentrations from three sources. In all 6 experiments,

the source and monitors were at the same height.

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Figure 4.4 Distributions of 10 s averages at 0.4 m horizontal distance from the source for 5

experiments with a real smoker. In these experiments, the vertical distance between the smoker’s

breathing zone to the monitors was varied resulting in four distances relative to the source: 0.2 m below,

level, 0.2 m above, and 0.4 m above.

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Figure 4.5 Time series plots of the four monitors closest to the smoker (0.4 m horizontal distance)

for the same 5 experiments presented in Figure 4.4. Each plot is one experiment, with the 4 monitors at

all 4 angles shown together.

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Figure 4.6 Box plots showing the distribution of 10-s average concentrations measured at the

monitors along the 0 degree (direction the smoker was facing) axis for two experiments with a real

smoker. For the experiment shown in 6a, the smoker was exhaling smoke slightly down and away from

the monitors. In the experiment shown in 6b, the smoker was exhaling directly at the monitors in front

of him.

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Figure 4.7 Time series plots for 12 monitors 0.25 m from a burning incense stick at 3 heights in 4

directions around the source in Watsonville home. Each section shows the three monitors in one

direction (e.g. Figure 4.7 (a) shows the three monitors at 0.25 m horizontal distance at the 0 degree

angle).

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Figure 4.8 Time series plots for 3 experiments with a smoker: two with doors closed (first two plots)

and one with doors open (last plot). Each plot shows the 10 s average concentrations from 2 monitors

on the array: one monitor close to the smoker that measured the highest concentration (shown in black),

and a second monitor, far away from the smoker that measured the lowest concentration during the

experiment.

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Chapter 5: Conclusions

5.1 Major Findings and Contributions

The work presented in this thesis characterizes and quantifies the

proximity effect for indoor air pollutants in naturally ventilated homes, and examines

what factors might influence it. While naturally ventilated homes are complex systems

with many factors affecting mixing characteristics, I chose to examine the factors we

could vary and would have an important influence on mixing and the proximity effect.

The following are the major conclusions from my work:

1) Microplumes. In this thesis, I discuss how higher magnitude and more frequent

microplumes (very high concentration peaks, often lasting only a few seconds)

result in higher concentrations close to the source, but little is known about

them. Individual microplumes occasionally exceeded the upper logging limit

of our instruments (140 ppm for CO, ~8000 g/m3 for PM2.5), particularly for

CO. Current air quality standards are written based on the health effects from

integrated 24-h average concentrations. Few studies (e.g Pope et al., 2001)

have looked at the effect of shorter term exposures to increases in particle

concentration, but to the best of my knowledge, no one has studied the health

effects of these short duration, high concentration peaks. But this research

demonstrates the need for such research.

2) Ventilation. Natural and mechanical ventilation affect mixing as well as

temperature stratification. We found that this was a major factor affecting

horizontal and vertical concentration profiles in a room. Under natural

ventilation conditions at air change rates typical of California homes, we

showed that the assumption of uniform mixing can under-predict

concentrations very near the source (0.25 m) by 6 – 20 times, at the height of

maximum concentration. For the range of natural ventilation conditions

studied, concentrations were above the predicted concentration typically within

2-3 m (horizontal distance) of the source. Within 0.5 m vertical distance of

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maximum concentrations, the proximity effect was greatly diminished (more

uniform concentration profile) indicating that under natural ventilation

conditions there was a strong temperature gradient hindering vertical mixing.

These findings are important for modeling human exposure to an active source.

3) Source Type. The type of source (buoyant or nonbuoyant) and its relative

position to the receptors (people or monitors) has a large influence on where

the highest concentrations will be measured. A buoyant (hot) source will rise

until it cools to room temperature. This height depends on both the

surrounding air temperature and the temperature stratification in the room.

Typical plume rise for a smoldering cigarette or incense stick was 0.5 – 1 m for

the natural ventilation conditions we studied. We saw a distinguishable

difference between a smoldering buoyant source (such as a smoldering

cigarette, or incense) and an actively smoked cigarette. The addition of exhaled

mainstream smoke (a less buoyant source) to the smoldering cigarette resulted

in higher concentrations at different heights.

5.2 Recommendations for Future Work

1) Temperature Profiles. In the course of analyzing the data, I discovered that

there was a very pronounced vertical temperature gradient in the room which

affected vertical mixing. A better understanding of these temperature profiles

and the factors affecting them (air change rate, mixing characteristics) would

help to clarify understand non-uniform concentrations during the active source

period. In Chapter 1, I discussed that Baughman et al (1994) found that

incoming solar radiation increased mixing by a factor of 10, but at very low air

change rates. Horizontal temperature profiles including wall temperatures were

not measured during these experiments and could shed some light on mixing

characteristics. Experiments looking at the effect of incoming solar radiation

at typical residential air change rates under natural ventilation would be useful

for understanding the relative importance of these two factors to overall mixing

behavior.

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2) Smoking Behavior. In Chapter 4, I found that a smoked cigarette resulted in a

very different concentration profile than a smoldering cigarette. Further

investigation of where the smoker holds the cigarette in between puffs and the

plume rise from the smoldering cigarette is needed. A possible experiment

would be for a smoker to light a cigarette and emulate smoking a cigarette

without puffing (but moving the cigarette to and from the mouth) to see how

movement and plume height affect both exposures to people nearby as well as

the proximity effect. Also, the relative importance of mainstream vs.

sidestream smoke to a nonsmoker’s exposure deserves a closer look. This also

depends on the smoker’s behavior, such as where the cigarette is held in

between puffs and how often the smoker puffs as well as in what direction.

3) Ventilation. In this thesis, I examined the proximity effect in naturally

ventilated homes during mild summer/fall weather. We found that air change

rate was an important factor affecting the proximity effect. The effect of

mechanical ventilation was briefly looked at, but more experiments under are

important. Many people in the U.S. rely on mechanical ventilation to heat or

cool their homes in more extreme weather. In the one experiment we did using

the house fan (not air conditioning) the proximity effect was diminished

compared to another experiment with a similar air change rate using natural

ventilation, indicating increased mixing from mechanical ventilation. The

effect of heating and cooling could easily be studied, as well as the effect of

mechanical ventilation on temperature gradients.

References

Baughman, A. V., Gadgil, A. J., and Nazaroff, W. W. (1994) Mixing of a Point Source

Pollutant by Natural Convection Flow within a Room. Indoor Air, 4, 114-122

Pope, C. A., Eatough, D. J., Gold, D. R., Pang, Y., Nielsen, K. R., Nath, P., et al.

2001. Acute Exposure to Environmental Tobacco Smoke and Heart Rate

Variability. Environmental Health Perspectives 109: 711-716.

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Appendix A

Figure A1: This figure shows the frequency distribution of 5 min averages from the experiment on

9/8/08, base conditions, but half the flow rate of CO emitting. In this figure, I evaluated the effect of off

scale 15 s readings on the 5 min averages. For every 5 min average, there are 20 15-s readings. From

this plot, I decided that if more than 5 of the 15-s readings were offscale, the 5-min average was

artificially low.

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Figure A2: This figure shows the result of excluding 5-min averages that are negatively impacted by

offscale measurements. Red circles show the frequency distribution for all 5-min averages from 9/8/08

at 0.25 m from the source. For the frequency distribution shown by the black circles, I applied the

“rule” I formulated from Figure A2 (to exclude points that have more than 5 readings off scale).

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Figure A3: This figure shows the effect of increased averaging times on raw data measured at 0.25 m

from the source on 9/7/08 (ACH = 0.33 h-1, source at 0.75 m from the ground, and monitors at 1 m

from the ground). As the averaging time increases, the slope of the line gets smaller, but even at 2 h

averaging time, the slope remains very large.

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Table A1: Horizontal Rate of Spread Calculated from First Increase in SF6

Concentration in Home #1

Home #1 Horizontal Rate of Spread (m/min) Ventilation Rate

Date Monitor A Monitor B Average (h-1

)

9/2/08 1.01 0.76 0.88 0.17

9/3/08 1.62 0.45 1.03 0.57

9/4/08 1.57 0.31 0.94 0.79

9/5/08 0.96 0.40 0.68 0.41

9/6/08 2.26 0.31 1.28 1.25

9/7/08 1.63 0.44 1.04 0.33

9/8/08 1.68 0.39 1.04 0.50

9/9/08 1.03 0.41 0.72 0.40

9/10/08 2.35 0.73 1.54 0.35

Mean 1.57 0.47 1.02 0.53

Std. Dev. 0.51 0.17 0.27 0.32

SF6 monitors were placed 4 m horizontal distance on two sides of the source

Table A2: Horizontal Rate of Spread Calculated from First Increase in SF6

Concentration in Home #2

Home #2 Horizontal Rate of Spread (m/min) Ventilation Rate

Date Monitor A Monitor B Average (h-1

)

11/4/08 0.51 --a 0.51 0.37

11/5/08 6.22 0.60 3.41 0.188

11/6/08, day 0.42 0.29 0.35 2.1

11/6/08, night 0.51 --a 0.51 0.4

11/7/08, day 2.75 0.84 1.79 5.4

11/7/08, night 0.80 0.79 0.80 0.54, 1.38

11/8/08, night 0.86 5.09 2.98 0.58

Mean 1.72 1.52 1.48 1.37

Std. Dev. 2.14 2.01 1.27 1.75

SF6 monitors were placed 2.8 m horizontal distance (edges of room) on two sides of the source. a

monitor failure


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