accessible tools for classification of exposure to particles

Accessible tools for classification of exposure to particles

Michael Brauer a,*, Sumeet Saksena b,1

a School of Occupational and Environmental Hygiene, The University of British Columbia, 2206 East Mall,

Vancouver, Canada BC V6T 1Z3b Centre for Environmental Studies, Tata Energy Research Institute, Darbari Seth Block, Habitat Place, Lodhi Road,

New Delhi 110003, India

Received 22 August 2000; accepted 14 December 2001

Abstract

In this manuscript we describe various alternative tools to estimate exposure to particles. We stress methods that are

cost effective and widely available to those throughout the world. The use of surrogate measures arises from the need to

estimate exposures of large populations where individual measurements are not feasible, for predictive modeling or to

assess exposures rapidly before personal monitoring campaigns can be implemented. In addition, an understanding of

the relationship between exposures and surrogate variables can be useful in helping to identify mitigation strategies to

reduce exposures. We have separated the various alternative exposure measures by the scales of impact, describing

approaches to assess regional, urban and household indoor air quality. In particular, we emphasize scenarios that are

relevant to particle exposures that may be experienced in developing countries as a result of domestic energy use for

cooking and heating. In all cases the approaches we describe are applicable to large populations as the data collection

techniques are relatively inexpensive and specifically applicable on a population basis for risk assessment, epidemiology

or to evaluate determinants of exposure and health outcomes. The ultimate use of the assessed exposures will determine

the relevance of potential surrogate measures.

� 2002 Elsevier Science Ltd. All rights reserved.

Keywords: Exposure assessment; Particles; Environmental epidemiology; Risk assessment

Contents

1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1152

2. Exposure classification for different scales of impact . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1152

2.1. Regional. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1152

2.1.1. Remote sensing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1152

2.1.2. Visibility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1153

2.2. Urban . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1153

2.2.1. Relationships between personal exposures and ambient concentrations . . . . . 1153

2.2.2. Visibility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1154

2.2.3. Road distance and traffic counts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1155

2.3. Indicator pollutants for regional and urban scales . . . . . . . . . . . . . . . . . . . . . . . . . . 1155

2.4. Household . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1155

Chemosphere 49 (2002) 1151–1162

www.elsevier.com/locate/chemosphere

*Corresponding author. Tel.: +1-604-822-9585; fax: +1-604-822-9588.

E-mail address: [email protected] (M. Brauer).1 Present address: East–West Center, 1601 East–West Road, Honolulu, HI 96848.

0045-6535/02/$ - see front matter � 2002 Elsevier Science Ltd. All rights reserved.

PII: S0045-6535 (02 )00245-X

mail to: [email protected]

2.4.1. Fuel and stove type . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1156

2.4.2. Time spent cooking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1157

2.5. Ventilation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1157

2.5.1. Indicator pollutants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1157

2.5.2. Biological monitoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1158

2.5.3. Selection of surrogate measures for household sources. . . . . . . . . . . . . . . . . 1158

3. Methods to evaluate surrogate measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1159

4. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1159

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1159

1. Introduction

In this manuscript we describe various alternative

tools to estimate exposure to particles. We stress meth-

ods that are cost effective and widely available to those

throughout the world, although in some cases compu-

tationally somewhat complex. Exposure, as defined by

the US National Academy of Sciences (NAS, 1991) is

‘‘an event that occurs when there is contact at a

boundary between a human and the environment with a

contaminant of a specific concentration for an interval

of time’’. By this and other widely accepted defini-

tions, exposure inherently has a human activity/location

component in addition to concentration measurements.

Accordingly, simple exposure classification methods can

in some cases borrow from the activity/location com-

ponent of exposure without requiring sophisticated

measurements. Methods for collecting time-activity data

relevant to particle exposure assessment are described in

detail elsewhere in this issue (Freeman and Saenz de

Tejada, 2002). We focus on the use of surrogate mea-

sures to assess particle exposure as particle exposure

measurement techniques are discussed in detail in other

manuscripts in this issue (Jantunen et al., 2002; Wilson

et al., 2002).

We have separated the various alternative expo-

sure measures by the scales of impact, describing

approaches to assess regional, urban and household in-

door air quality. In particular, we emphasize scenar-

ios that are relevant to particle exposures that may

be experienced in developing countries as a result

of domestic energy use for cooking and heating. In all

cases the approaches we describe are applicable to

large populations as the data collection techniques

are relatively inexpensive and specifically applicable on

a population basis for risk assessment, epidemiology or

to evaluate determinants of exposure and health out-

comes. In all determinations of exposure, the specific

level of data quality, precision and accuracy that is re-

quired will depend upon the ultimate use of the data,

whether it be to comply with a regulation, to assess the

impact of various interventions to reduce exposure, to

inform decision makers or to conduct epidemiologi-

cal studies. Review of the literature indicates that the

three most common uses of exposure measures are (i)

testing and quantifying relationships between exposure

and health outcomes, (ii) conducting comparative risk

assessment in the absence of health data and (iii) iden-

tifying factors (therefore possible interventions) that in-

fluence exposure (therefore health outcomes). Balanced

against the needs for accurate and precise data are the

increased costs and limited feasibility of applying ad-

vanced exposure measurements to all individuals in a

population. In many cases, simple sample size calcula-

tions may estimate the precision requirements of the

exposure information in order to detect differences in

exposure of a given magnitude. In most cases, for ex-

ample, even when actual exposure measurements are

collected they are often only collected for a small pop-

ulation subset, such as subjects in a research study. The

results of such an assessment can then be applied to a

larger population, provided the original subjects have

been randomly selected from a representative popula-

tion.

2. Exposure classification for different scales of impact

As described above, the specific requirements of

the exposure assessment will depend upon, amongst

other factors, the size of the population and the area

of suspected impacts. We outline indirect measures of

exposure that are appropriate for different scales of

impact from regional air pollution to household expo-

sures.

2.1. Regional

2.1.1. Remote sensing

Recently, technical advances have made possible the

rapid dissemination of remote sensing data which can be

used to estimate ambient particle levels over regional

scales. To date remote sensing tools have been used to

provide estimates of particle levels in the assessment of

large scale dust storms, volcanic eruptions and forest fires

(Falke et al., 2001). In these assessments, two main sat-

ellite sensors have been commonly used. The advanced

1152 M. Brauer, S. Saksena / Chemosphere 49 (2002) 1151–1162

very high resolution radiometer (AVHRR) has been used

to directly detect dust storms and smoke from fires (Fang

and Huang, 1998; Wooster et al., 1998). Another remote

sensing technique which has been used to estimate aero-

sol levels is the Total Ozone Mapping Spectrometer

(TOMS) which measures the UV absorption of aerosols.

The TOMS data are composites for 2–3 days depending

upon the frequency of satellite passes over the region of

interest. While resolution is limited, to approximately

35� 45 km2 grids, this level of resolution is sufficient to

identify the spatial extent of the plumes from major

aerosol events. The TOMS is not very sensitive to aero-

sols below about 1.5 km and therefore is not useful to

detect urban air pollution sources. Due to these limita-

tions, remote sensing has not been commonly used for

human exposure assessment except at a very crude scale.

Newly launched and future generations of satellites,

including the MODIS sensor, will also be able to directly

estimate the mass concentration and size distribution of

aerosols in the atmosphere. As remote sensing techniques

are limited by the fact that the measurements obtained

relate to aerosol loading within the total height of the

vertical air column, perhaps their greatest utility is in

identifying the spatial extent of large area particle plumes.

However, when used in combination with traditional

ground based monitoring, these techniques can provide

quantitative information on the ground level concentra-

tions of particulates in urban and regional air masses.

While these remote sensing methods can presently

provide only semi-quantitative estimates of ambient air

concentrations they may still be useful tools for expo-

sure assessment due to their spatial global coverage,

including many areas without routine monitoring net-

works, and their ability to provide information in nearly

real-time. Consequently, remote sensing measurements

can provide public health authorities and the general

public with early warning system information regarding

potential exposures from large-scale episodes (WHO,

1999; Falke et al., 2001).

2.1.2. Visibility

In situations of forest fire related particulate air

pollution, visibility and relative humidity measurements

have also been used to estimate particle levels (see

HAZE GUIDE, Version 3, Integrated Forest Fire

Management Project (IFFM), Samarinda, INDONE-

SIA http://www.iffm.or.id/HazeGuide3.html accessed

July 13, 1999, for an example). This method is based on

the relationship between the particle concentration and

visibility reduction, as described by the Koschmeider

equation (Hinds, 1982).

2.2. Urban

At the urban air pollution scale, most cities

throughout the world have some type of ambient air

monitoring network which measures several major gas-

eous and in most cases, particulate air pollutants. The

section on indicator pollutants discusses some relation-

ships between particles and other ambient air pollutants.

Depending upon the setting, measurements of gaseous

pollutants may be reasonable surrogates for particle

concentrations as they may arise mainly from the same

sources. This approach has been used, for example, to

try to distinguish different particle sources such as

transported and locally generated particles, in epidemi-

ological analyses (Burnett et al., 1997; Burnett et al.,

1998; Burnett et al., 1999).

2.2.1. Relationships between personal exposures and

ambient concentrations

In urban areas with an existing ambient monitoring

network that provides some level of information on

particle concentrations in ambient air, the actual as-

sessment of exposure requires an understanding of the

relationship between ambient concentrations and per-

sonal exposures. Since this topic and the implications for

the interpretation of epidemiological study results has

been discussed in detail recently (Wilson et al., 2000),

our coverage of ambient-concentration–personal expo-

sure relationships is limited. Spatial variability in

ambient particulate levels, differential penetration of

particles to the indoors due to building characteristics,

ventilation and filtration systems, and indoor sources of

particles may all affect the relationship between expo-

sures and ambient concentrations. In locations with

limited spatial variability in ambient particle concen-

trations a single ambient monitor may accurately reflect

concentrations throughout the area. For example, sev-

eral studies of spatial variability of ambient particles

indicate that within urban areas, particularly for re-

gional-source fine particles (sulfates), spatial variation

is minimal (Ozkaynak et al., 1996; Suh et al., 1997).

However, where the major particulate sources are local

in origin, or for particle components that are specific to

local sources (for example, elemental carbon from

vehicle exhaust) spatial variation may be more pro-

nounced (Cyrys et al., 1998; Roorda-Knape et al., 1998;

Jannsen et al., 1997b). Further, ambient monitoring

stations are unlikely to capture localized, short duration

exposure peaks such as those observed near roadways.

For example, using fast-response nephelometers Balogh

measured short bursts of PM2:5 up to 45 lg/m3 as a

diesel bus goes past (Balogh et al., 1994). It is likely that

those near major traffic sources are commonly exposed

to short bursts of particle concentrations higher than

would be recorded by an averaging ambient monitor

(Brauer et al., 1999). In one example where personal

monitoring was designed to specifically evaluate the

impact of traffic on personal exposures, exposure to

individual components of particulate matter, such as

M. Brauer, S. Saksena / Chemosphere 49 (2002) 1151–1162 1153

http://www.iffm.or.id/HazeGuide3.html

elemental carbon, were found to be associated with

traffic counts (Kinney et al., 2000).

While it may be expected that indoor particle con-

centrations accurately represent personal exposures to

particles, studies of personal exposure to particles have

demonstrated increased personal exposures compared to

both indoor and outdoor concentrations (Watt et al.,

1995; Ozkaynak et al., 1996; Wallace, 1996; Janssen

et al., 1998). This excess personal exposure, labeled the

‘‘personal cloud’’, has been attributed to proximity to

particle-generating sources, such as cooking or envi-

ronmental tobacco smoke, and indoor activities which

resuspend deposited particles such as cleaning or walk-

ing on carpet (Ozkaynak et al., 1996). Some studies have

suggested that the personal cloud effect is mainly due to

coarse particles resuspended by personal activity, as

these are more easily resuspended than fine particles

(Ozkaynak et al., 1996; Wallace, 1996; Brauer et al.,

1999). In an experimental study, Brauer et al. (1999)

found that the personal cloud effect was greater for an

experiment in which a subject was active as opposed to

being sedentary. Particulate resuspension from clothing

has also been measured (Yakovleva et al., 1999). Monn

et al. (1997) reported that in homes where inhabitants

were present and conducted normal daily activities

during monitoring, PM10 and PM2:5 I/O ratios were

above one; homes without inhabitants present during

monitoring had ratios below one. In this study, ETS, gas

stoves and occupant activity levels were found to be

important indoor sources. Activities, such as dusting,

vacuum cleaning and spraying have also been sugges-

ted as important particle-generating activities (Spengler

et al., 1981; Clayton et al., 1993).

The PTEAM study evaluated major factors affecting

indoor particle concentrations during daytime and

nighttime. Outdoor concentrations, smoking and cook-

ing were found to be important factors associated with

indoor PM levels. Indoor PM concentrations were

negatively correlated with house volume and air ex-

change rates (Ozkaynak et al., 1996).

Other studies have also shown ETS to be a major

contributor to personal exposures and that exclusion of

cases with ETS exposure improves the correlation be-

tween personal and ambient (Janssen et al., 1998). For

example, Monn et al. (1997) observed a low correlation

between personal and indoor levels (r ¼ 0:39) which

improved after excluding ETS exposed cases (r ¼ 0:71).In a review of the three largest studies of indoor air

particles in the US, Wallace (Wallace, 1996) summarized

that the single largest indoor source of fine particles is

cigarette smoke, for homes with smokers.

Although it is clear that indoor exposures, in par-

ticular those associated with environmental tobacco

smoke and cooking, are major contributors to personal

fine particulate exposure, exposures to particles of am-

bient origin are highly correlated with ambient particle

concentrations (Janssen et al., 1997a; Janssen et al.,

1998; Janssen et al., 1999; Ebelt et al., 2000; Sarnat et al.,

2000; Wilson et al., 2000). These studies have assessed

the degree to which each subject�s exposures follow the

day-to-day changes in ambient concentrations and have

indicated that ambient concentrations are good surro-

gates for exposures to particles of ambient origin in time

series epidemiologic studies. Sulfate (SO2�4 ), has been

suggested as a reliable estimate of exposure to ambient

particles produced in combustion processes (Lippmann

and Thurston, 1996) and can be used as an indicator of

particles of ambient origin (Wilson et al., 2000). Sulfate

aerosols penetrate effectively into indoor environments

and have no major indoor sources (Dockery and Spen-

gler, 1981a,b). High correlations between personal and

ambient concentrations of sulfate have also been found

for various populations including children (Suh et al.,

1992a), adults (Brauer et al., 1989; Ebelt et al., 2000;

Sarnat et al., 2000) and a population of older adults with

cardiorespiratory conditions, which spent little time

outdoors (Stieb et al., 1998). Daytime personal expo-

sures of particulate sulfur have been shown to be highly

correlated (r ¼ 0:88) with levels measured directly out-

side of the subjects� homes (Ozkaynak et al., 1996).

2.2.2. Visibility

Visibility information has been used in several cases

to retrospectively estimate particle exposures in situa-

tions where no ambient monitoring data were available.

It should be stressed however, that visibility is really a

surrogate measure of ambient concentrations of parti-

cles and not actual exposure. One general conclusion of

these efforts is the need to produce region-specific esti-

mates in order to incorporate specific meteorology

and particle composition. A detailed discussion of the

methodology is described by Abbey et al. (1995) who

developed estimates based on the relationship between

visibility measurements and limited PM2:5 monitoring

data. To summarize, the method involves use of air-

port visibility data to estimate the extinction coefficient

using a modified Koschmeider formula: bext ¼ 18:7� Cðhumidity correction factorÞ=V ðdistance in milesÞ. Theextinction coefficient, a measure of haziness, bext, is de-fined as bext ¼ K=visual range, where K is the Kosch-

mieder constant. The extinction coefficient is in units of

km�1 and it is proportional to the concentration of light

scattering and absorbing aerosols and gases. The value

of K is determined by both the threshold sensitivity of

the human eye and the contrast of the visible objects

against the horizon sky. Several studies have suggested

the value of K to be 1.9 (Griffing, 1980; Dzubay et al.,

1982; Stevens et al., 1984; Ozkaynak et al., 1985; Husar

et al., 1995). Then the extinction coefficients are re-

gressed against PM2:5 data incorporating seasonal effect

variables. In an estimation of PM2:5 from airport visi-

bility data in 12 cities, Ozkaynak found a mean


R2 ¼ 0:43, which improved to increased to 0.58 with

addition of TSP in the regression model (Ozkaynak

et al., 1985). Abbey, for California sites, found R2 ¼ 0:67(Abbey et al., 1995).

2.2.3. Road distance and traffic counts

Other surrogate measures for assessment of exposure

to traffic-related particulate air pollution are measures of

the distance to the nearest road and traffic counts.

Several studies have demonstrated relationships between

road distance and health outcomes. For example, in-

creased respiratory symptoms in children are associated

with living near a freeway and with traffic density, es-

pecially truck traffic (van Vliet et al., 1997). To estimate

the relationship between road distance and particle lev-

els, a study of spatial variability in particulate concen-

trations has shown that PM2:5 concentrations near

major roads were 30% greater than at a background

location not influenced by local traffic (Janssen et al.,

1997b). Black smoke levels were 2.6 times higher at the

roadside locations, indicating the important contribu-

tion of diesel exhaust to traffic-related PM2:5 emissions.

Higher indoor concentrations of traffic-related particles

have been measured in homes in high traffic areas rela-

tive to low traffic areas (Fischer et al., 2000). Recently

the spatial variability of ultrafine particles has been in-

vestigated and results indicate a strong relationship be-

tween ultrafine particle levels and traffic levels (Buzorius

et al., 1999). Particle counts have also been shown to be

highly correlated with traffic levels (Harrison et al.,

1999). An assessment of particulate matter near urban

roadways (Balogh et al., 1994) has shown that direct

tailpipe emissions, especially diesel vehicle emissions, are

more important contributors to mobile source PM2:5

emissions than re-suspension of settled particulate.

2.3. Indicator pollutants for regional and urban scales

The use of surrogate pollutants are generally not

required for the assessment of ambient particle concen-

tration on the urban and regional scales. This is because

particle monitoring is an essential component of most

urban air quality monitoring stations and it is unusual

for other air pollutants to be measured in the absence of

particle monitoring. Further, while gaseous pollutants

may be highly correlated with particles in large regions

or in urban areas, the specific relationships will depend

upon local conditions (Brook et al., 1997). Some use has

been made of indicator pollutants to delineate specific

sources of particles in urban environments, for example,

CO and NO are sometimes used as indicators of motor

vehicle particulate emissions. In a recent analysis of

particles and mortality in 20 large US cities, correlations

(including all cities) with PM10 were 0.53 and 0.45 for

NO2 and CO, respectively (Samet et al., 2000). Within

individual cities the correlations are expected to be

higher. Gaseous pollutants have also been incorporated,

along with measurements of particle composition, in

factor analysis approaches to classifying sources of

particles in ambient air. Such source apportionment

techniques are discussed in more detail in this issue

(Morawska et al., 2002).

2.4. Household

This section reviews the various exposure indicators

that researchers have used mainly in attempts to test the

association between indoor air pollution from cooking

fuels and health outcomes. As several recent review

articles have discussed, emissions from cooking fuels are

a major source of indoor air pollution in developing

country settings in which unvented stoves are used for

cooking (Smith, 1993a,b; Vedal, 1998; Bruce et al., 2000;

Smith et al., 2000). Nearly 50% of the world�s popula-

tion, almost all of these in developing countries, rely on

biomass fuels (wood, dung and crop residues) for their

domestic energy needs, primarily cooking and heating

(Smith et al., 2000). Indoor exposures associated with

these fuels have been associated with a number of health

impacts. In particular, exposure to biomass combustion

products has been identified as a major risk factor for

acute respiratory infections (ARI). ARI are the leading

cause of infant mortality in developing countries. In

addition to the risks of infants, the women who are

cooking are also at risk for chronic respiratory diseases

as well as adverse pregnancy outcomes. Due to the high

exposures experienced in these settings and the large

numbers of people exposed, there is enormous public

health importance associated with indoor air pollution

in developing countries.

In contrast to urban air pollution studies in developed

countries, routine monitoring network data are not

available to aid in the assessment of exposure to house-

hold air pollution in developing countries. Accordingly

many epidemiologic studies use surrogate variables for

exposure assessment. A limited number of studies have

conducted measurements with the aim of validating sur-

rogates for further use in epidemiologic studies. Most of

these studies have been descriptive in nature, attempting

to only measure average concentration of pollutants

(mainly particles and carbon monoxide) and describe

the frequency distribution of the data. Only a few have

systematically studied (through hypothesis testing, cor-

relation analysis, etc.) the impact of surrogate variables

on the levels of concentration/exposure. These variables

have been either called determinants of exposure or ex-

planatory variables. If a variable is found to be signifi-

cantly related to exposure, it can be used as a surrogate

for the exposure. It must be remembered, however, that

not all variables that significantly affect exposure can

serve as meaningful surrogates––some examples are

season, time of day and altitude.


2.4.1. Fuel and stove type

The type of fuel mainly used by the household for

cooking––biomass, animal wastes, kerosene, etc.––has

been the most common choice of indicator, typically as

a dichotomous variable (using wood or not using

wood) and sometimes as a categorical variable with

multiple values (wood, dung, kerosene, coal,. LPG,

etc.). Epidemiological studies in which exposures were

not measured suggest that fuel type is an important

variable in predicting health outcomes, yet these studies

do not provide much information with respect to the

quantitative relationship between fuel type and expo-

sure. For example, fuel type variables have been asso-

ciated with reduced lung function (Behera et al., 1994;

Behera, 1997), acute lower respiratory illness mortality

and morbidity (Kossove, 1982; Penna and Duchiade,

1991; de Francisco et al., 1993), obstructive airways

disease (Dennis et al., 1996), cor pulmonale (Padmavati

and Arora, 1976), chronic bronchitis and chronic air-

ways obstruction (Perez-Padilla et al., 1996; Pandey,

1988), lung cancer (Sobue, 1990), eye ailments (Mohan

et al., 1989; Mishra et al., 1997b) and tuberculosis

(Mishra et al., 1997a). One problem in using fuel type

as an indicator is that in practice households may use

more than one type of fuel––on different days, in dif-

ferent seasons, for different meals in a day, and even

burning a mix of fuels at the same time. Behera (1997)

and Behera et al. (1994) have taken into account the

use of multiple fuels and shown that use of mixed fuels

can also lead to deleterious effects on pulmonary

function. In a rigorous examination of the role of

confounding factors in testing the association between

indoor air pollution and respiratory health of women

in hills of Guatemala, researchers discovered a

strong association between type of fire (open fire vs.

chimney woodstoves) and respiratory health (Bruce

et al., 1998).

Building upon these epidemiologic relationships, an

increasing number of studies have validated the use of

fuel type indicators by comparison with measurements

of air pollutants. Nearly all of these studies have been

cross-sectional in design and have indicated that the

mean concentrations of key pollutants, including par-

ticulate matter, are higher for solid fuels as compared to

the cleaner liquid and gaseous fuels (Raiyani et al., 1993;

Smith et al., 1994; Brauer et al., 1996; Ellegard, 1996).

While the mean values conform to this pattern, it has

been noted that there is a high degree of variance and

overlap in the distribution of values across fuel groups.

This has been ascribed to the influence of other factors

such as ventilation, etc. In most cases, area sampling has

been conducted in kitchens and in some cases in other

rooms, as indicators of personal exposure. Smith et al.

(1994) used personal PM10 sampling to indicate

that average cook�s personal levels during cooking of

PM10 in households using wood or biomass were about

1100 lg/m3, while these were 530 and 420 lg/m3 for

kerosene and LPG respectively in Pune, India.

In a study in Mexico PM10 and PM2:5 levels were

compared across biomass and LPG user groups using

the gravimetric method with inertial impactors (Brauer

et al., 1996). Sampling duration was approximately 9 h.

The mean concentration of PM2:5 during cooking was

found to be 888 lg/m3 for biomass users and 325 lg/m3

for LPG users. In the case of PM10 the mean concen-

tration for biomass users was 1143 and 480 lg/m3 for

LPG users. Results also indicated that mean levels over

the longer sampling duration were also higher in the case

of biomass users. Using a cascade impactor, researchers

in Ahmedabad––a city in India––compared particulate

levels across cattle dung, wood, coal, kerosene, and LPG

user groups (Raiyani et al., 1993). Both the TSP and

size-fractionated levels were found to be higher in the

dung, wood, and coal groups as compared to the kero-

sene and LPG groups. But TSP levels across kerosene

and LPG groups were found to be similar. With the

availability of personal and portable size-fractionating

particle samplers and given current knowledge regarding

the size distribution of biomass smoke particles, PM2:5

or respirable particulate matter samplers should be used

to selectively sample smoke emissions. Dirt floors and

high levels of ambient coarse particles from agricultural

activities and unpaved roads in many rural developing

country settings may result in high TSP and/or PM10

exposures which are not indicative of indoor cooking

exposures.

In Guatemala, Naeher et al. (1996a) compared TSP,

PM10, and PM2:5 levels across three types of stoves–open

fire, LPG, and planchas (improved stoves). Sampling

was conducted on a near 24 h basis. An impactor was

used to measure PM10 and a cyclone for measuring

PM2:5. Results indicated significantly higher levels of

particulate matter (all sizes) in the case of open fire as

compared to either LPG or the plancha, but LPG and

planchas were found to result in similar PM levels. In a

three-city study (Lusaka, Maputo, and Hanoi), Ellegard

(1996) measured RSP levels during cooking (PM5 using

a cyclone) across five fuel categories: electricity, char-

coal, kerosene, wood, and coal. Results indicated that

the fuels could be grouped into two exposure catego-

ries––high exposure (coal and wood) and low exposure

(kerosene, electricity and charcoal) based on the geo-

metric means. But a high degree of overlap of levels was

observed across all fuel types.

One potential explanation for the observed overlap

in exposures within users of ‘‘low exposure’’ fuels are

emissions from the foods themselves. In developed

country households where indoor cooking is performed

with cleaner fuels or with vented stoves, emissions from

cooking foods have been shown to be significant sources

of particle exposure (Ozkaynak et al., 1996; Brauer et al.,

1999). In addition to stove and ventilation characteris-


tics, overlap may also result from penetration of emis-

sions from neighboring homes, waste burning and other

unidentified particle sources.

For biomass fuels, the type of stove (traditional vs.

improved, clay vs. metal, etc.) has been another impor-

tant determinant that has been studied. About a dozen

studies have examined this issue through cross-sectional

surveys. While many studies have shown that improved

stoves are associated with reduced exposure, some have

indicated that the degree of reduction is not as high as

desired, and some have even found no influence of stove

type (Ramakrishna, 1988). Before accepting the use of

stove type as an indicator we recommend that further

research be conducted using longitudinal designs (be-

fore-and-after type of studies) to test the effect of this

variable on exposure.

2.4.2. Time spent cooking

Next to fuel type the most popular choice of exposure

indicators have been time spent daily in cooking, the

number of years cooked, and a combination of both.

Again, these indicators have been used in epidemiologic

analyses but have not been subject to rigorous valida-

tion. It is acknowledged that the major use of time spent

cooking is in the retrospective assessment of chronic

exposures, and is therefore inherently difficult to vali-

date. These indicators include the average time spent

cooking in the household (Pandey et al., 1989), increased

duration of cooking (He et al., 1991; Behera, 1997),

average time per day spent near the fire (Pandey, 1984),

years of cooking with wood (Dennis et al., 1996) and

hour-years of exposure (years of exposure multiplied by

average hours of exposure per day (Perez-Padilla et al.,

1996). Though not a quantified measure of time, regular

carriage of the infant on the mother�s back while cook-

ing has been used as a proxy for exposure to examine the

effect of smoke on ALRI in a study conducted in

Gambia (Armstrong and Campbell, 1991).

2.5. Ventilation

The role of ventilation as a determinant factor has

been indirectly addressed through the use of variables

such as type of house, materials used in construction of

walls and roofs, number of rooms, location of cooking,

etc. In general, consistent patterns regarding the impact

of ventilation have not emerged from observational

studies. Ramakrishna (1988) found roof type to be a

statistically significant determinant for TSP and CO

concentration during cooking in south Indian villages

but not in north Indian villages of the sample. Kitchen

location was found to affect CO concentration in the

north Indian villages. Menon (1988) found roof type to

be a significant factor for TSP and CO but not kitchen

type. Brauer et al. (1996) and Menon (1988) have also

examined the role of kitchen volume on pollutant con-

centration. While the material used to construct walls

and roofs is relatively simple to measure (other than the

fact that multiple materials may be in use in a house) the

location and type of kitchen poses a greater problem in

defining what it really means. This is because of the wide

variety of patterns. For example, kitchens could be

partly covered, thus in many cases it is not possible to

say whether it is a case of indoor cooking or outdoor

cooking. One way to get around this problem is to define

more than two possible values for kitchen type. There

may also be seasonal patterns in the choice of cooking

location. Kitchens could be attached to rest of the house

or be independent. Standardization of such definitions

is certainly a need.

2.5.1. Indicator pollutants

This section discusses the possibility of identifying

surrogate pollutants for developing country particle ex-

posures. The need for surrogate measures arises because

of the difficulties in measuring particulate matter using

the traditional gravimetric methods, especially in the

field. The reasons are mainly to do with (a) filter han-

dling and weighing, and (b) flow rate measurement and

maintenance. This becomes even more difficult when size

selective devices are used. Continuous monitors based

on light scattering may be applicable to field measure-

ments although use of these devices requires consider-

ation of the particle size distribution and composition as

well as relative humidity. It is for these reasons that

researchers have recently evaluated surrogate pollutants.

CO has often been examined for this role. The use of

SO2 and NO2 is ruled out because these are not emitted

by all the fuel-stoves. PAHs and VOCs, while being

common pollutants to all the fuel types, are even more

difficult and costly to measure.

Some studies have reported the correlation coefficient

between particulate matter and CO. Kitchen area 22-h

gravimmetric PM2:5 and diffusion staintube CO con-

centrations were measured in homes with open-fire and

improved wood cookstoves in two studies in Guatemala,

one in three test houses and one in 15 open-fire and 25

improved––stove houses (Naeher et al., 1996b). CO

personal samples were also taken for mother and child.

Correlations between kitchen-area CO and PM2:5 levels

were high (R2 ¼ 0:89–0.94), as were those between the

personal samples for mother and child (R2 ¼ 0:95–0.99).In general, the correlations were lower for less polluted

conditions. The CO/PM2:5 ratio averaged 12.3–14.4 and

13.1–16.2 for open fires and improved stoves. These

results generally support the use of CO staintubes as a

proxy for PM2:5. It was also observed that correlations

were stronger over a 24 h averaging period as compared

to shorter sampling periods such as a cooking session.

Ramakrishna (1988) estimated the correlation (R2)

between TSP and CO to be between 0.64 (south Indian

villages) and 0.30 (north Indian villages). In three hilly


villages of Garhwal Himalaya the correlation coefficient

was found to be significant but low (R2 ¼ 0:44) (Saksenaet al., 1992). In a simulated village kitchen using a

standard burn cycle the R2 between TSP and CO was

found to be 0.46, and between TSP and RSP (d50 ¼ 5

microns) the R2 was 0.49. In both cases the correlation

was significant (Gupta et al., 1998). In all these studies

CO was measured using dosimeters that work on the

electrochemical principle. It is clear that CO is possibly a

better surrogate for finer particles than for the coarser

particles, which is logical in a combustion process.

Certainly in small studies the use of passive stain tubes

to measure CO is a cheaper option than using personal

samplers to measure PM. However, for very large scale

studies the overall costs of using such tubes could also

become very high.

2.5.2. Biological monitoring

An alternative to the measurement of exposure and a

potential improvement on the use of surrogate variables

is the use of biological monitoring. Unfortunately no

biomarkers have been validated as markers of exposure

to particles. Limited work has suggested that urinary

methoxylated phenols may be used as an indicator of

exposure to wood smoke but to date this method has not

been validated in field studies (Dills et al., 2001). Fur-

ther, there are numerous difficulties, both logistical and

cultural, associated with collecting urine and/or blood

samples in developing country settings. Exhaled carbon

monoxide is another potential biomarker of exposure to

biomass smoke, although the relationship with particle

levels may be variable.

Perhaps the most advanced investigation of a bio-

marker in the assessment of exposure to household

biomass smoke was conducted by Ellegard (1997) who

proposed that eye irritation in the form of tears or

smarting eyes during cooking was a useful determinant

of indoor air pollution from cooking related sources. An

analysis of data from three cities (Lusaka, Maputo, and

Hanoi) showed that tears are more prevalent in condi-

tions of higher particulate pollution. The correlation

between tears and carbon monoxide was found to be

weak. Persons experiencing tears were also found to

have more respiratory symptoms. Using the prevalence

of tears provides a good indicator of groups that are at

higher risk of health impairment due to air pollution.

Surveying for this condition is simple and non-intrusive,

which makes it a useful screening indicator, though it

cannot replace more thorough epidemiological investi-

gations.

2.5.3. Selection of surrogate measures for household

sources

Fuel type, as an indicator, possibly the simplest one,

has been used in demonstrating the adverse impact of

indoor air pollution on health. This is true in the case of

acute and chronic diseases, and in the case of children

and women. Perhaps because of a perception of fuels

such as kerosene and LPG being far cleaner than bio-

fuels, researchers have been led to believe that merely

using ‘‘fuel types’’ would not lead to exposure misclas-

sification. This assumption needs further field verifica-

tion. Recent research indicates that while emissions and

even concentration levels of pollutants from cleaner

fuels and stoves are lower in comparison to biofuels,

human exposures could still be similar (or at least not

substantially lower) owing to activity patterns of the

subjects, ventilation factors, other socio-economic pa-

rameters, etc. (Saksena, 1999). Other important issues

are the use of mixed fuels (also primary vs. secondary

fuels) and past usage patterns vs. current usage patterns.

Perhaps the most important concern is whether fuel

type is solely an environmental indicator or whether it

is also a socio-economic indicator (therefore also an

indicator of other confounding factors such as mal-

nutrition, overcrowding, etc.). Using sophisticated sta-

tistical techniques a limited number of studies have

attempted to address this concern with mixed results.

Future research could examine the relationship between

exposure and quantity of fuel consumed and combus-

tion efficiency.

Time spent daily in cooking and number of years

cooked are also commonly used indicators. This cate-

gory of exposure indicators is not as easy to measure as

fuel type but has the advantages of (a) there being a

lesser chance of these variable being indicators of other

confounders, and (b) being conceptually linked to ex-

posure assessment (crudely defining exposure to be the

product of concentration and time). Some issues of

concern here are (a) the usual questionnaire based sur-

veys do not yield accurate data on time usage, and (b)

there is a need to distinguish between the total time

spent cooking and the actual time spent near the fire,

and to distinguish between the mother�s and the infant�sactivity patterns. Including questions related to activity

patterns in large demographic and socio-economic sur-

veys (even possibly in census surveys) could provide

valuable data. This should however be supplemented by

sample surveys in which other methods, preferably ob-

servational, are employed to test the accuracy of the

questionnaire based methods.

Indicators related to housing and ventilation are also

useful and easily measured. In fact some of these may

already be available through government surveys (ex-

ample: Census of India 1991: Basic amenities). Here

there is a need to standardize the definitions of variables

being used. A recent study has indicated that tears while

cooking are strongly related to levels of particulate

matter and to other health outcomes, and therefore it

could serve as an indicator, preferably as a screening

indicator. However, there is no consensus regarding


whether this ought to be treated as an indicator of ex-

posure or of health.

3. Methods to evaluate surrogate measures

There are two main methods to identify possible

surrogate measures. One is to consider exposure as a

dependent variable and then search for possible deter-

minants/predictors––the independent variables. Another

approach is to still consider exposure as the dependent

variable but to search for other dependent variables that

are strongly associated with exposure. In the first ap-

proach one can decide how far down the cause-effect

chain one wishes to go in the process of identifying

causative factors. It has to remembered that while all

proposed surrogates should have a high degree of sta-

tistical association with the actual exposure metric, not

all variables that have a high degree of association with

exposure can be called meaningful surrogates.

Experimental and observational studies can be used

to identify determinants of exposure (Burstyn and Tes-

chke, 1999). In experimental designs, factors expected

to influence exposure are selected using theoretical

models or prior evidence from literature. In many cases,

the main study question is not the identification of

exposure determinants, but quantification of the mag-

nitude of effect. Study conditions are altered in a con-

trolled way under the direction of the investigator. The

main effects under study are altered under investiga-

tive control, while other factors vary naturally. Obser-

vational studies are conducted under actual conditions

without investigator control. This approach requires far

more documentation than the previous approach. In-

vestigator control of the variety of determinants studied

exists only through the selection of varied sites, times,

groups, etc.

Burstyn et al. (1999) highlight the following data

analysis issues to ascertain determinants of exposure:

(a) Transformation of the exposure variable: log-trans-

formation of the data prior to data analysis is a com-

mon practice, as most variables are log-normally

distributed.

(b) Correlation of predictor variables: independence be-

tween predictor variables is an important issue in

modelling exposure. Examples include time devoted

by people to various tasks, between location and

job, etc. Techniques like regression analysis require

that all independent variables be uncorrelated. In

situation where there are many correlated variables,

principal component analysis can be used to identify

the optimal number of independent �factors�.(c) Empirical model building: t-tests, ANOVA, or Krus-

kal–Wallis tests can be used to test the association

between exposure and surrogates that are categorical

variables. Multiple regression analysis can be used

when the variables are continuous (interval or ratio

scaled).

(d) Interpretation of results: in many cases, regression

coefficients reflect the direct effect of the determi-

nant. However, a negative coefficient can also repre-

sent tasks or conditions for which the exposures are

lower than the reference level represented by the in-

tercept in the model, but which are passively and not

actively reducing exposure.

4. Conclusion

We have described a series of potential surrogate

measures for particle exposure assessment relative to

regional, urban and developing country household ex-

posures. The use of surrogate measures arises from the

need to estimate exposures of large populations where

individual measurements are not feasible, for predictive

modeling or to assess exposures rapidly before personal

monitoring campaigns can be implemented. In addition,

an understanding of the relationship between exposures

and surrogate variables can be useful in helping to

identify mitigation strategies to reduce exposures. The

ultimate use of the assessed exposures will determine the

relevance of potential surrogate measures. Clearly fur-

ther validation work, including measurements of expo-

sures in combination with measurements of surrogate

variables, is needed for many of the potential surrogate

measures before they can be applied to external datasets.

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