An Inexpensive Dual-Chamber Particle Monitor:

Laboratory Characterization

Rufus Edwards

School of Social Ecology, University of California at Irvine, Irvine, CA; and School of Public Health,

University of California at Berkeley, Berkeley, CA

Kirk R. Smith

School of Public Health, University of California at Berkeley, Berkeley, CA

Brent Kirby

Chemistry Department, Princeton University, Princeton, NJ; and Aerosol Dynamics, Berkeley, CA

Tracy Allen

Electronically Monitored Ecosystems (EME), Berkeley, CA

Charles D. Litton

Pittsburgh Research Laboratory, National Institute for Occupational Safety and Health, Centers for

Disease Control and Prevention, Pittsburgh, PA

Susanne Hering

Aerosol Dynamics, Berkeley, CA

ABSTRACT

In developing countries, high levels of particle pollution

from the use of coal and biomass fuels for household cooking and heating are a major cause of ill health and premature mortality. The cost and complexity of existing monitoring equipment, combined with the need to sample many

locations, make routine quantification of household particle

pollution levels difficult. Recent advances in technology,

however, have enabled the development of a small, portable, data-logging particle monitor modified from commercial smoke alarm technology that can meet the needs of

surveys in the developing world at reasonable cost. Laboratory comparisons of a prototype particle monitor developed

at the University of California at Berkeley (UCB) with gravimetric filters, a tapered element oscillating microbalance,

and a TSI DustTrak to quantify the UCB particle monitor

response as a function of both concentration and particle

size and to examine sensor response in relation to changes

in temperature, relative humidity, and elevation are presented here. UCB particle monitors showed good linearity in

response to different concentrations of laboratory-generated

oleic acid aerosols with a coarse (mass median diameter, 2.1

m) and fine (mass median diameter, 0.27–0.42 m) size

distributions (average r2 0.997  0.005). The photoelectric

and ionization chamber showed a wide range of responses

based on particle size and, thus, require calibration with the

aerosol of interest. The ionization chamber was five times

more sensitive to fine rather than coarse particles, whereas

the photoelectric chamber was five times more sensitive to

coarse than fine. The ratio of the response between the two

sensors has the potential for mass calibration of individual

data points based on estimated parameters of the size distribution. The results demonstrate the significant potential of

this monitor, which will facilitate the evaluation of interventions (improved fuels, stoves, and ventilation) on indoor

air pollution levels and research on the impacts of indoor

particle levels on health in developing countries.

INTRODUCTION

In the developing world, concentrations and risks attributable to smoke from indoor solid fuels are much higher

than developed nations, and adverse effects are seen in

larger percentages of the population.1 Although there is a

clear rationale for analyzing these exposures to reduce the

health burden in disadvantaged populations, it also presents opportunities for analyzing the mechanisms by

which particles exert their effects in all populations. To do

so, however, reliable methods of measuring particle levels

IMPLICATIONS

Although at present not showing a low enough detection

limit to be of use in many developed-world settings, the

UCB particle monitor seems to operate well in conditions

common in hundreds of millions of developing-country

households, for which it was designed. Combined with its

low capital and operating costs, freedom from the need for

highly trained field workers, capacity for unattended longterm monitoring on batteries, quiet operation, low weight,

and small size, its potential would seem highly promising.

TECHNICAL PAPER ISSN 1047-3289 J. Air & Waste Manage. Assoc. 56:789 –799

Copyright 2006 Air & Waste Management Association

Volume 56 June 2006 Journal of the Air & Waste Management Association 789
are needed to make the link with health or to evaluate

interventions.

Unfortunately, current standard gravimetric monitoring technology is ill-suited for household studies in the

developing world because of the requirement for sensitive

balances in environmentally controlled environments for

weighing filters, the need for calibration of pumps in the

field, and the demand for careful handling of pumps,

balances, and filters from laboratory to field and back. In

addition, current battery-operated pumps are relatively

noisy and obtrusive in households, and the available batteries limit the sampling time to a day or two at most.

Finally, even when executed correctly, this technology

only provides integrated estimates of particulate mass

during the sampling period and does not provide information on temporal exposure patterns and peaks.

Currently available continuous, portable, data-logging particle monitors use light-scattering technology to

assess particle mass concentrations. These are prohibitively expensive for use in large numbers of houses and

are also limited in battery life because of active pumping

requirements. An additional problem is that the sensitivity of light-scattering chambers is dependent on the size

distribution and refractive index of the aerosols. In the

absence of calibration with the aerosol of interest, a default value is often applied, although manufacturers recommend user calibration with the aerosol being monitored. This approach is quite limited for the study of

particles in houses in the developing world, because the

size distribution and nature depends on the relative contribution of resuspended dust and combustion sources,

such as open flames, used for cooking. Furthermore, the

size distribution of combustion-generated aerosols varies

with the stages of combustion in a typical open fire or

stove, and multiple phases (evaporation, volatilization,

flaming, and smoldering) are likely to be present at the

same time, depending on the position of the fuel in the

fire. Finally, the type of fuels used in different seasons

frequently changes,2 making direct comparison difficult.

Thus, the aerosol represents a complex and changing

mixture for which calibration is difficult. The use of an

average sensitivity value to calculate an aerosol mass concentration is likely to overestimate or underestimate by

significant factors because of a lack of knowledge about

the particle size distribution over time.

A further complication is that neither gravimetric nor

current portable continuous devices are suitable for longitudinal studies with a duration 1 week, which are of

interest in current epidemiological studies that attempt to

evaluate long-term exposures. There is, thus, a clear need

for economical instruments to estimate particulate exposures that may be deployed in large numbers in the population for extended periods.

Based on the 1993 suggestion of one of the authors3

to combine modern smoke alarm and microelectronics

technologies, a particle monitor has been developed at

the University of California at Berkeley (henceforward

referred to as UCB particle monitor) to address this need.

Litton et al.4 demonstrated the potential for using combined photoelectric and ionization measurements from

such devices for real-time characterization of micrometer

and submicrometer particles as a function of aerosol mass,

surface, and diameter. This paper describes laboratory

tests of prototype versions of the UCB particle monitor in

relation to gravimetric filters, a Tapered Element Oscillating Microbalance, and a TSI DustTrak, as well as the

influence of environmental parameters on sensor performance.

EXPERIMENTAL WORK

The UCB Particle Monitor

The sensors in the UCB monitor and the theoretical basis

for their use to quantify combustion aerosols as a function

of particle mass, surface area, and diameter are discussed

in Litton et al.4 Briefly, the UCB monitor relies on sensors

from an inexpensive commercial household smoke detector that combines ionization chamber sensing (ion depletion by airborne particles) and photoelectric sensing

(optical scattering by airborne particles). Ionization

chambers demonstrate higher response to smaller particles produced from the exhausts of diesel engines or flaming combustion, whereas photoelectric-type detectors

show greater response to the larger particles produced

from smoldering combustion.5 Litton6 presents a detailed

evaluation of differences in the sensor responses.

Ionization-detector technology, commonly found in

commercial smoke detectors, uses  particles generated

from a small radioactive source (241Am) to cause a negative distortion in a small current between the charged

plates of the ionization chamber by creating ions from air

molecules. When airborne particles enter the chamber,

the ionized air molecules attach to them, and the current

is disrupted proportionately to the product of the particle

number and particle diameter of the airborne particles in

the active chamber. The photoelectric or light-scattering

sensor uses a light-emitting diode (LED) and a photodiode

that measures the intensity of scattered light. In the UCB,

the LED emits at 880 nm, and scattered light is detected at

an angle of 45 ° measured from the forward direction.

In the UCB monitor, the smoke alarm circuitry was

modified so that the sensors could be powered at consistent levels, signals could be measured continuously, and

the information could be stored in a data logger within

the monitor during programmable time intervals for subsequent downloading into a personal computer. Also

added were temperature and relative humidity (RH) sensors and a clock that is synchronized with the computer

clock before initiation of monitoring, along with the ability to interface additional sensors if desired.

Experimental

Initial tests were performed at the facilities of Aerosol

Dynamics, Inc. Test aerosols were of oleic acid (density of

0.89 g/cc and a refractive index of 1.45, typical of ambient

aerosol). Fine particles were produced by nebulizing 0.1%,

0.2%, or 0.4% solutions of oleic acid in isopropyl alcohol.

Particles 1 m (henceforward referred to as coarse particles) were produced by nebulizing a 50% oleic acid in

isopropyl alcohol solution.

A laser particle counter (Model Spectro 0.3, Climet

Instruments Co.) was used to estimate the size of the

coarse particle distribution. For the coarse particles generated with the 50% oleic acid solution, the mass median

diameter was measured as 2.1 m. Size distributions for

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790 Journal of the Air & Waste Management Association Volume 56 June 2006
the fine particles could not be measured directly, because

the laser particle counter had a detection limit of 0.3 m.

The mass median diameter for these aerosols was estimated assuming constant nebulized droplet size. For a

mass median diameter varying as the cube root of the

solution concentration, calculated mass median diameters are 0.27, 0.33, and 0.42 m for solutions of 0.1%,

0.2%, and 0.4%, respectively.

Test aerosols were passed through a 2.5-cm–diameter

polyvinyl chloride pipe with two polonium strips for

aerosol neutralization, after which it entered the bottom

of a test duct consisting of a 155-cm section of 20-cm–

diameter metal duct (Figure 1a). The main duct airflow

was provided by unfiltered air drawn from the room by a

roof blower, expanded into a 10-cm–diameter metal elbow and then expanded again to the 20-cm–diameter test

section. Low-flow velocity conditions were achieved by

closing of a flapper valve to restrict airflow into the test

duct.

Three prototype UCB particle monitors (UCB-1, -2,

and -3) were mounted along the test section, 120 ° from

each other, with a vertical separation of 18 cm. A metal

screen at the top of the test section (Figure 1b) supported

four sampling inlets for the following instruments: (1)

DustTrak (TSI Inc.) photoelectric aerosol monitor using

manufacturer’s size selection inlet for particles 1 m in

diameter (PM1); (2) tapered element oscillating microbalance ([TEOM] Rupprecht & Patashnick Co.) particulate

monitor preceded by a 2.5-mm size-cut cyclone; (3) laser

particle counter; and (4) 47-mm Teflon filter for gravimetric mass preceded by a 2.5-mm greased impactor. Flow

rate through the impactor was 3 L/min, and filters were

preweighed and postweighed on a microbalance (Sartorius MC 5) to obtain sample mass.

Figure 1. (a) Experimental setup; (b) Sampling inlets for TSI DustTrak, TEOM, laser particle counter, and 47-mm Teflon filter.

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Volume 56 June 2006 Journal of the Air & Waste Management Association 791
Air velocity was measured as the average of anemometer readings across the diameter of the test section. A

typical run at 16 cm/sec had six different concentrations

held for 10 min each. Data from each concentration period were averaged for further analysis. A typical run at 37

cm/sec was shorter (3 min per concentration).

Table 1 displays the sampling frame of experiments

encompassing the combinations of coarse and fine particles at two different air velocities using a range of different

concentrations. The range of concentrations tested is

shown with the number of different concentration levels.

Sizes shown are mass median diameters (MMD).

Static Chamber Tests

A series of static chamber tests were undertaken at the air

pollution laboratory (SE 1 Room 134) at the University of

California at Irvine. A sealed chamber 55 cm  55 cm 

55 cm made from smooth hardboard was used to reduce

electrostatic effects. Airflow from a separate combustion

chamber was drawn into the chamber through a 3.8-cm–

diameter duct and the chamber sealed with a valve. An

internal mixing fan during the first 2 min after sealing

ensured a fully mixed chamber. Particle concentrations

measured by 2 UCB particle monitors (UCB-305 and -306)

and a DustTrak (zero calibrated, PM1 greased impactor,

1.7 L/m flow rate) were averaged for a 20-min period after

mixing in a similar manner to the tests above. Mean

temperature and RH were 23 °C 0.38 and 58%  1.33,

respectively, during the tests.

RESULTS

Relationship between UCB Particle Monitor

Response and Gravimetric Mass

Figure 2 shows a comparison of the DustTrak and UCB

particle monitor photoelectric and ionization response in

comparison to gravimetric mass measurements. Both

DustTrak and UCB particle monitor show good linearity

in relation to gravimetric measurements, although the

DustTrak overestimates mass by a factor of 3 for fine

particles. Similarly, the TEOM overestimated mass by a

factor of 1.22 (r2 0.997) compared with gravimetric

mass. The photoelectric detector of the UCB particle monitor shows a similar slope relationship as the DustTrak,

which is not surprising given that both rely on light

scattering, although using different wavelengths and scatter angles. The DustTrak data presented in the following

figures have been normalized to gravimetric mass concentrations using the relationship presented in Figure 2.

Results at increased air velocities measured for shorter

periods were quite similar to the lower air velocity condition, only with more sensor noise in the ionization chamber. Little difference was observed for the photoelectric

chamber (Figure 3). Because objectives of the current

study are to evaluate UCB particle monitor performance

for applications in indoor settings, the results for the

lower air velocity are presented below.

Figure 4 shows photoelectric and ionization response

to fine particles from nebulized 0.1% oleic acid. Similar

relationships were observed for 0.2% and 0.4% oleic acid

Table 1. Particle size, air velocity, temperature (T), and relative humidity (RH) during tests.

Air

Velocity

Particle Size

Fine (MMD  0.3–0.4 m) Coarse (MMD  2.1 m)

16 cm/sec 0.08–7.4 mg/m3 (18 concentration levels) T 25  1 °C, RH 37  1% 3–19 mg/m3 (6 concentration levels) T 26  1 °C, RH 35  1%

37 cm/sec 0.05–2.9 mg/m3 (18 concentration levels) T 25  1 °C, RH 37  1% 1–13 mg/m3 (5 concentration levels) T 26  1 °C, RH 35  1%

Figure 2. DustTrak and average UCB photoelectric and ionization response compared with gravimetric mass for fine particles (0.1%, 0.2%,

and 0.4% oleic acid).

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792 Journal of the Air & Waste Management Association Volume 56 June 2006
(Table 2). Both photoelectric and ionization chambers

show good linearity, although there were significant differences in the slopes among the three UCB monitors.

Figure 5 shows the photoelectric and ionization response to coarse particles from nebulized 50% oleic acid.

Although these responses show similarly good linearity

for both detectors, the slope of the response is significantly different from the responses observed for fine particles (Table 2). In addition, the change in response of the

photoelectric detector and the ionization chamber with

particle size are in opposite directions. The photoelectric

detector is more sensitive to coarse compared with fine

particles, and the ionization detector has the reverse sensitivity trend (Figure 6).

Effect of Environmental Parameters

Ionization Chamber. The potential across the ionization

chamber is dependent on the number of ions generated

by the interaction of the  particles with air molecules.

The current will also be affected, however, by other

parameters that change the number of air molecules in

the active chamber relative to the radiation source. The

main parameters with significant effects are temperature, RH, and atmospheric pressure (and, therefore, altitude). Modeled effects of these parameters on the

potential across the ionization chamber may be described linearly, with best fit resulting in r2 of 0.97,

0.83, and 0.96 for temperature, RH and elevation, respectively (eqs 1–3).7 The initial value of the collection

electrode voltage is decreased as each parameter (temperature, RH, and elevation) is increased.

CEV volts	  
0.0083 temperature °C	  4.3177 (1)

CEV volts	  
0.0022 RH %	  4.288 (2)

CEV volts	  
0.0001 elevation m	  4.199 (3)

Figure 7 shows the theoretical response of ionization

chambers to monodisperse particles with a diameter of

100 nm and a mass concentration range of 0 –2 mg/m3

over ranges of elevation, temperature, and RH. Although

there were some variations in sensitivity, they appear

evenly distributed around the mean value and, thus, no

systematic bias would be expected in the computation of

aerosol mass. More importantly, sensitivities at fixed values of these parameters in the laboratory should also be

valid under other environmental conditions in the field.

Thus, measurement of sensor performance and mass response to aerosols in the laboratory should not require

significant adjustment under field conditions, and corrections for temperature, RH, and elevation can be made

after data collection (see Discussion below). Clearly, however, as with other monitoring instruments, responses to

aerosols of different size and composition from different

sources would result in different sensitivities.8

Photoelectric Chamber. Figure 8 shows a sample plot (UCB093) of the response of UCB photoelectric chambers to

temperature fluctuations inside a low-particle sealed environment with high-efficiency particulate–filtered air.

UCB monitors were run over a 48-hr period in which

temperature fluctuated naturally. Other UCB monitors

showed similar relationships but with slightly differing

regression slopes. As temperature increases, there is a corresponding decrease in the resting potential of the photoelectric chamber. Because calculation of mass in response to a test aerosol would involve subtraction of the

resting potential from the response to the aerosol, correction for this variability in relation to temperature is required.

Electronic Noise and Sensitivity. Figure 9 shows the standard deviation of the photoelectric response while in a

low-particle environment defined by a signal below the

detection limit of the DustTrak. The standard deviation

represents the electronic noise level of the photoelectric

Figure 3. UCB photoelectric response to fine oleic acid particles under different air velocities.

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Volume 56 June 2006 Journal of the Air & Waste Management Association 793
signal. In addition to the standard deviation of the raw

output from the sensors in mV, Figure 9 shows the reduction in the standard deviation with signal processing in

the laboratory tests presented here and further reduction

in the standard deviation from signal processing incorporated since these tests. Using a 15-min moving average

corresponds to a standard deviation of 0.087 mV or 81%

reduction in standard deviation compared with signal

processing used in the current tests. Using a standard

definition of signal-to-noise ratio of three times the standard deviation of the noise to define detection limits, the

reduction in the standard deviation represents an improvement in sensitivity provided that the particle events

that are of interest occur on a longer time scale than the

time interval used in the signal averaging.

Combustion Aerosols

Figure 10 shows the photoelectric response of two UCB

particle monitors for smoldering incense combustion

where six discrete concentrations were generated in the

sealed test chamber and monitored over a 20-min period.

There is similarly good linearity of photoelectric response

120 mg/m3 as reported by the DustTrak, although the

top data point is above the reported upper range of DustTrak with standard calibration (100 mg/m3

). DustTrak

mass was not adjusted, as in the laboratory tests, because

Figure 4. UCB photoelectric and ionization response to fine particles from nebulized 0.1% oleic acid (MMD 0.3 m): (a) Photoelectric

response; (b) Ionization response.

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794 Journal of the Air & Waste Management Association Volume 56 June 2006
no gravimetric measurements with this aerosol were

made. If the coefficients derived for oleic acid filter measurements from the laboratory tests are used, which were

similar to those reported in the literature9 to adjust both

the photoelectric mV response and the DustTrak mass,

however, the resulting slope is 0.85, demonstrating the

need for calibration of the UCB photoelectric detector

with the aerosol of interest. Based on these coefficients,

however, linearity of response of the UCB can be extended to equivalent gravimetric mass of 40 mg/m3

.

DISCUSSION

Although responses of both the photoelectric and ionization chambers show good linearity in response to both

coarse and fine particles, the slope of the response varies

significantly between coarse and fine particles. This is

summarized in Figure 6, which shows the ratio of the

ionization chamber response and the photoelectric signal

response to both fine and coarse particles. The ionization

chamber was five times more sensitive to fine than coarse

particles (measured as mV per mg/m3

), whereas the photoelectric chamber was five times more sensitive to coarse

than fine. It is well established that smoke detectors respond differently to flaming fires, which typically produce particles with smaller average diameters than those

produced from smoldering fires.5 Indeed, this difference

in response is the reason that more sophisticated commercial smoke alarms include both chamber types so that

the alarm can be triggered by either flaming or smoldering

combustion. The data in the current paper support these

observations using nebulised oleic acid particles. Litton et

al.4 report that these differences in response maybe used

to derive additional information about the nature of the

particle distribution to improve estimates of particle mass

and other characteristics. Furthermore, they report that

this information has the potential to provide additional

information on count mean diameter, the diameter of

average surface area, and the diameter of average mass.

Both the sensitivity of an ionization chamber and an

optical scattering chamber are significantly dependent on

the size distribution of the aerosols. If these sensitivities

could be defined when the sensor is exposed to an aerosol

of known size, then the resultant mass concentration

could be determined simply by dividing the voltage

change by the corresponding sensitivity. Although many

commercial continuous monitors, like the DustTrak, are

referenced at the factory to Arizona road dust or equivalent, the manufacturers recommend recalibration to the

aerosol of interest. In the absence of calibration with the

aerosol of interest, a default value is often applied. Jenkins

et al.9 reported that the factory-calibrated DustTrak overreported respirable suspended particle concentrations (approximately equivalent to particles 3.5 m in diameter)

by factors of 2.6 –3.1 compared with gravimetric measurements and was most in error when monitoring combustion smokes, including environmental tobacco smoke.

We obtained similar overestimates of mass using the

DustTrak with a greased impactor for PM1, and regression

equations indicate a slope of 3 for fine particles in relation to gravimetric mass of oleic acid.

That the two sensors of the UCB particle monitor

have substantially different sensitivities according to particle size is a characteristic that might be considered a

disadvantage but in fact offers the potential for a significant additional capability. Because the ratio of the photoelectric response to the ionization responses is dependent on the differential sensitivity to the particle size

distribution of the aerosol, the ratio of these signals provides an estimate of the particle distribution and, hence,

the mass conversion factor that may be applied to estimate the mass of combustion-generated aerosols on a real

time basis as the size distribution shifts during different

phases of combustion. Further work is being undertaken

to use and validate this capability.

The laboratory tests presented in this paper were for

relatively short periods under relatively stable conditions

and, therefore, no temperature adjustments were made.

In poor rural households in highland or temperate areas,

however, temperatures often fluctuate considerably and

frequently range from 5 to 30 °C during a 24-hr period.

For the ionization chamber, large changes in temperature

and RH during the sampling period would result in a

change in the baseline value, which, if uncorrected,

would result in erroneous mass estimates (Figure 8). Similarly, large changes in temperature during the sampling

period are likely to affect the photoelectric baseline and

result in erroneous mass estimates (Figure 8). This is particularly important for time periods with lower particle

mass, because this fluctuation would result in significant

bias in the measurements. In addition, if measurements

are made every minute over long sampling periods (frequently 48 hr), small mass errors in the baseline can

Table 2. Summary of photoelectric and ionization response to oleic acid particles (MMD in parentheses).

Response

0.1% Oleic Acid (0.27 m) 0.2% Oleic Acid (0.33 m) 0.4% Oleic Acid (0.42 m) Coarse (2.1 m)

Slopea r

2 Slopea r2 Slopea r2 Slopea r2

Photoelectric

UBC 3 44.8 0.9988 42.6 0.9999 41.2 0.9999 220.8 0.9975

UBC 2 32.5 0.9985 31.5 0.9999 30.7 0.9999 151.0 0.9967

UBC 1 34.1 0.9982 32.7 0.9999 31.7 0.9999 166.5 0.9972

Ionization

UBC 3 95.6 0.9941 84.5 0.9978 96.4 0.9943 16.3 0.9999

UBC 2 83.1 0.9973 75.0 0.9986 89.0 0.9926 15.3 0.9983

UBC 1 75.1 0.9808 73.2 0.9956 80.4 0.9947 14.1 0.9981

a

Expressed as mV response per mg/m3.

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Volume 56 June 2006 Journal of the Air & Waste Management Association 795
result in significant deviation from gravimetric estimates.

As a result of the effect of temperature and RH on the

chamber signals, the UCB particle monitor is fitted with a

temperature and RH chip so that these parameters can be

logged simultaneously with chamber outputs. Subsequently, these parameters are used to normalize both

ionization and photoelectric chamber responses during

data reduction. A further adjustment for elevation has to

be made with the ionization chamber if the field-sampling site is at a different elevation to that used for initial

calibration and establishment of reference values (Figure

8).

For fine particles, the three UCB particle monitors

demonstrated different slopes in response to oleic acid

(Figures 4 and 5). The difference in response may be

related to both position in the duct and to differences in

the resting voltage and sensitivities between the chambers

of different particle monitors. Similar tests in another

facility by Litton et al.4 revealed a similar difference in

responses of the UCB particle monitors. As with most

other monitoring instruments, therefore, characterization

of the response of each device relative to other instruments should be performed before deployment of multiple monitors in the field.

Figure 9 shows the reduction in the standard deviation of the baseline in the absence of a test aerosol as a

result of using low pass filters and subsequent averaging.

Absence of a test aerosol was defined as being below

detection of a DustTrak. Similar to other instruments, a

series of weighted averaging algorithms are available

Figure 5. Photoelectric and ionization response to coarse particles from nebulized 50% oleic acid (MMD 2.09 m): (a) Photoelectric

response; (b) Ionization response.

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796 Journal of the Air & Waste Management Association Volume 56 June 2006
within the software for the UCB particle monitor to reduce electronic noise on a 1-sec basis relative to the 1-min

logged values. Averaging of the 1-min logged values pro

vides a second filtering step for data analysis. Using a

standard definition of signal-to-noise ratio of three times

the standard deviation of the noise to define detection

Figure 7. Modeled effect of elevation, temperature, and RH on ionization chamber sensitivity.

Figure 8. Measured photoelectric responses to temperature.

Figure 6. Ionization vs. photoelectric signals for both coarse and fine particles.

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Volume 56 June 2006 Journal of the Air & Waste Management Association 797
limits,10 the reduction in the standard deviation because

of filtering represents a corresponding improvement in

sensitivity provided that the particle events that are of

interest occur on a longer time scale than the time interval used in the signal averaging. Using a 15-min moving

average, the standard deviation of the baseline corresponds to a theoretical lower detection limit for the photoelectric sensor of 17.5 g/m3 for fine particles. Because

indoor concentrations in the solid-fuel-using households

are generally higher and often much higher than this

value, this limit is not a serious constraint.

CONCLUSIONS

The laboratory experiments described here support the

use of this inexpensive device for field conditions where

fine or coarse particle levels exceed a few tens of g/m3

. In

particular, the experiments showed the following: (1)

UCB particle monitors showed good linearity in response

to different concentrations of laboratory-generated oleic

acid aerosols with a constant particle size distribution; (2)

theoretical model prediction of ion chamber response

indicated linear dependency of the resting potential on

temperature, RH, and elevation; (3) in laboratory tests,

the photoelectric chamber showed a dependency of the

resting potential on temperature; given wide fluctuations

of temperature and RH in many households in the developing world, continuous correction for these parameters

is necessary in the reduction and analysis of data; (4)

sensors and data logging for temperature and humidity

have been incorporated into the device for this purpose;

(5) the photoelectric and ionization chambers show a

wide range of sensitivity based on particle size; the ionization chamber was about five times more sensitive to

fine than coarse particles, whereas the photoelectric

Figure 9. Percentage reduction in standard deviation of measured photoelectric baseline relative to laboratory tests presented above.

Figure 10. UCB response to smoldering incense test aerosol.

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798 Journal of the Air & Waste Management Association Volume 56 June 2006
chamber was five times more sensitive to coarse than fine;

this results in a ratio of sensitivities of 25 between the

two chambers for the different size ranges; (6) like other

light-scattering particle monitors, each device needs to be

each calibrated against the aerosol of interest; (7) the

detection limit of the device is 17 g/m3 for fine particles using a 15-min moving average; at longer averaging

times, however, the lower detection limit should be reduced; and (8) the upper detection limit was not determined in our tests, but linearity extended to an equivalent gravimetric mass of 40 mg/m3

.

ACKNOWLEDGMENTS

Funding provided by the Household Energy and Health

Programme of the Shell Foundation, United Kingdom, to

develop a low-cost particle monitor for use in developing

countries is gratefully acknowledged. The authors would

also like to thank Michael Johnson and Gian Allen for

their help on this project.

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About the Authors

Rufus Edwards is an assistant professor in environmental

health science, and policy at the University of California at

Irvine. Kirk R. Smith is a professor in environmental health

sciences and the Brian and Jennifer Maxwell Endowed Chair

in Public Health at the University of California, Berkeley. Tracy

Allen is founder and president of Electronically Monitored

Ecosystems. Charles D. Litton is a senior scientist at the

Pittsburgh Research Laboratory. Susanne Hering is founder

and president of Aerosol Dynamics. Brent Kirby is a Ph.D.

student in the Chemistry Department, Princeton University.

Address correspondence to: Rufus Edwards, Environmental

Health, Science, and Policy, 268 Social Ecology I, Irvine, CA

92697-7070; phone: 1-949-824-4731; fax: 1-949-824-

2056; e-mail: edwardsr@uci.edu.

Edwards et al.

Volume 56 June 2006 Journal of the Air & Waste Management Association 799