Designing monitoring networks to represent outdoor human exposure

Designing monitoring networks to represent outdoorhuman exposure

Judith C. Chow a,*, Johann P. Engelbrecht a, John G. Watson a,William E. Wilson b, Neil H. Frank c, Tan Zhu d

a Desert Research Institute, 2215 Raggio Parkway, Reno, NV 89512, USAb US Environmental Protection Agency, National Center for Environmental Assessment, MD 52,

Research Triangle Park, NC 27711, USAc US Environmental Protection Agency, Office of Air Quality Planning and Standards, MD C304-01,

Research Triangle Park, NC 27711, USAd Nankai University, Environmental Science & Engineering Institute, Tianjin 300071, China

Received 12 January 2000; accepted 28 November 2001

Abstract

Measurements of outdoor human exposure to suspended particulate matter (PM) are always constrained by

available resources. An effective network design requires tradeoffs between variables measured, the number of sampling

locations, sample duration, and sampling frequency. Sampling sites are needed to represent neighborhood and urban

spatial scales with minimal influences from nearby sources. Although most PM measurements for determining com-

pliance with standards are taken over 24-h periods every third to sixth day, outdoor human exposure assessment re-

quires measurements taken continuously throughout the day, preferably over durations of 1 h or less. More detailed

particle size and chemistry data are also desirable, as smaller size fractions and specific chemicals may be better in-

dicators of adverse health effects than total mass samples.

� 2002 Elsevier Science Ltd. All rights reserved.

Keywords: Network design; Air quality; PM2:5; PM10; Human exposure

Contents

1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 962

2. Sampling objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 962

3. Sampling uncertainties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 964

4. Environmental sampling theories. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 964

5. Spatial scales . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 966

6. Temporal scales . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 967

6.1. Sampling period . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 967

6.2. Sampling duration. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 968

6.3. Sampling frequency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 969

Chemosphere 49 (2002) 961–978

www.elsevier.com/locate/chemosphere

*Corresponding author. Tel.: +1-775-674-7050; fax: +1-775-674-7009.

E-mail address: [email protected] (J.C. Chow).

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

PII: S0045-6535 (02 )00239-4

mail to: [email protected]

7. Designing an outdoor exposure network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 971

8. Particulate monitor siting criteria. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 972

9. Evaluating zones of representation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 974

10. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 974

Acknowledgement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 974

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 974

1. Introduction

Integrated human exposure to suspended particulate

matter (PM) is a combination of indoor and outdoor

concentrations weighted by the time spent in each en-

vironment (Moschandreas et al., 2002). Although many

people spend a significant portion of their lives indoors,

they are still exposed to ambient concentrations when

they leave a building or when outdoor PM infiltrates.

Ambient PM (especially the size range of 0.1–1.0 lm inaerodynamic diameter) can constitute a large fraction of

indoor levels (e.g., Chao and Tung, 2001; Koponen et al.,

2001; Liu and Nazaroff, 2001) that are enhanced by

contributions from cooking, smoking, house dust, and

bioaerosols. While outdoor concentrations can be reg-

ulated by public policy, increments from indoor emis-

sions are controlled by the resident and are not subject

to external standards. Empirical relationships between

adverse respiratory and circulatory effects and outdoor

PM have been observed (Vedal, 1997; Watson et al.,

1997; Gordon et al., 1998; US Environmental Protection

Agency, 2002) and are the basis for National Ambient

Air Quality Standards (NAAQS) in the United States

(US Environmental Protection Agency, 1997) and else-

where. It is important to install and operate monitoring

networks that represent the exposure of large popula-

tions to outdoor air. This paper specifies the issues that

must be considered in implementing such a network.

Outdoor PM measurements must consider sampler

locations, sample durations, sampling frequencies, and

the variables measured. Sampling networks are con-

strained by access, available technology, and monitoring

resources. Tradeoffs must be made to obtain the most

information relevant to specific objectives for a given

resource expenditure. There is no simple formula ap-

plicable to all monitoring objectives and resource levels.

This discussion draws on theoretical concepts and past

experience to provide guidance on where and when to

monitor, what to measure, and how to make tradeoffs

for a variety of sampling objectives. The examples apply

to measurements of PM2:5 and PM10 (particles with 50%

cut points at aerodynamic diameters of 2.5 and 10 lm,respectively) that are regulated by NAAQS. Current PM

NAAQS use community-representative monitors and

spatial and temporal averaging to better represent

exposure and health effects (Watson et al., 1997). Al-

though general concepts and definitions related to en-

vironmental sampling have been published (Wilson and

Spengler, 1996; Baron and Willeke, 2001; Cohen and

McCammon, 2001), these are not consistent or neces-

sarily applicable to PM.

2. Sampling objectives

Ambient and workplace air quality measurements are

made for several purposes, and these purposes influence

sampling locations, durations, frequencies, variablesmea-

sured, and number of samples required to represent a

population:

• Compliance with air quality standards: The United

States and other countries have established ambient

air quality standards for PM in outdoor air to protect

public health. Indoor air standards are available

for industrial workplaces (Samet and Spengler, 1991;

Yocom and McCarthy, 1991) but are not common

for household and office air. Particle measurements

for compliance are intended to determine if PM

mass concentrations exceed existing standards and

if trends show improvements in air quality. US PM

standards currently require PM2:5 and PM10 mass

concentrations taken on filters by officially desig-

nated measurement systems (Watson and Chow,

2001a). Samples of 24-h duration are typically taken

every third or sixth day to estimate annual averages

and 98th percentile or one expected exceedance per

year concentrations. Sampling locations are intended

to represent large populations rather than nearby

source contributions or regional background levels.

Compliance monitoring requires long-term measure-

ments over many years, depending on the form and

values of the standards. US PM standards require

a minimum of three years to determine attainment

or non-attainment. Since public policy, financial or

criminal penalties, or litigation may result from these

measurements, they must be well documented and of

unquestionable consistency.

• Alerts: Pollution levels approaching or exceeding con-

centrations that cause immediate and adverse health

consequences require decisions to issue public warn-

ings or to impose temporary emissions reductions.

Many communities in the US curtail wood-burning

during winter when concentrations appear to be ris-

962 J.C. Chow et al. / Chemosphere 49 (2002) 961–978

ing. The real-time network in Mexico City is used to

shut down industries and reduce traffic during high

pollution episodes. In situ continuous monitors with

fast response times and telemetry to a central loca-

tion are required for alerts. High accuracy and preci-

sion are not required, as long as a high danger

threshold can be quickly detected. The data returned

by alert networks must be monitored, either auto-

matically or manually, and disseminated to officials

and the public by news broadcasts or over the inter-

net. While quantitative, continuous, real-time moni-

tors have been used for several gaseous pollutants

and particle measurements, they have not been com-

pletely perfected for some PM mass surrogates (Wat-

son et al., 1998; McMurry, 2000).

• Implementation of a standard: The causes of excessive

concentrations are not always evident from emissions

inventories or compliance measurements. Observ-

ables other than mass, such as PM chemical composi-

tion and size distribution, must be measured for these

purposes. Measurements are needed with 5 min to

hourly time resolution to observe the effects of diur-

nal changes in emissions and the surface mixing layer.

Multi-year monitoring periods are needed to detect

the effects of emissions reductions that may be ob-

scured by year-to-year meteorological variability.

Table 1 summarizes several of the useful PM observ-

ables and methods available for their measurement.

• Atmospheric process research: Excessive particle con-

centrations result from complex interactions involv-

ing emissions and meteorology. Primary particles

are directly emitted from sources, while secondary

particles form in the atmosphere from gaseous pre-

cursor emissions. Networks for process research

Table 1

Measurement parameters for PM

Observable Filter-based integrated method Commercially available in situ continuous method

Mass Gravimetry (Chow, 1995) Beta attenuation monitor (BAM)

(Nader and Allen, 1960; Spurny and Kubie, 1961; Lilienfeld and Dulchinos,

1972; Husar, 1974; Cooper, 1975; Lilienfeld, 1975; Sem and Borgos, 1975;

Cooper, 1976; Macias and Husar, 1976; Jaklevic et al., 1981; Courtney et al.,

1982; Klein et al., 1984; Wedding and Weigand, 1993; Speer et al., 1997)

Tapered element oscillating microbalance (TEOM) (Patashnick and

Hemenway, 1969; Patashnick, 1987; Patashnick and Rupprecht, 1991;

Meyer et al., 1992; Rupprecht et al., 1992; Allen et al., 1997)

Continuous ambient mass monitoring system (CAMMS, a pressure drop

tape monitor) (Babich et al., 2000)

Elements X-ray fluorescence

(Watson et al., 1999)

Semicontinuous elements in aerosol system (SEAS) for elements of Al, Ca,

Fe, Cu, Cr, Mn, Zn, Cd, As, Sb, Pb, Ni, V, and Se (Kidwell and Ondov,

2001; Ondov and Kidwell, 2002a,b)

Inductively coupled plasma–mass

spectrometry (Bettinelli et al., 1989;

Tanaka et al., 1998; Chin et al.,

1999)

Inductively coupled plasma–emis-

sion spectrometry (Harman, 1989)

Ions Ion chromatography

(Chow and Watson, 1999)

Flash volatilization for sulfate or nitrate (Watson et al., 1998; Stolzenburg

and Hering, 2000)

Automated colorimetry Particulate sulfate by chemical reduction (Watson, 2002)

Atomic absorption spectrophoto-

metry (Chow et al., 2002c)

Continuous gas and particle speciation monitor by ion chromatography for

particulate sulfate, nitrate, and ammonium, and for gaseous hydrochloric

acid, nitrous oxide, nitric acid, sulfur dioxide, and ammonia

Carbon Thermal optical reflectance

(Chow et al., 1993, 2001)

Ambient particulate carbon monitor for organic and elemental carbon

(Rupprecht et al., 1995)

Thermal optical transmission

(Birch and Cary, 1996)

Thermal combustion for organic and elemental carbon (Sunset Laboratory,

Forest Grove, OR)

Thermal manganese oxidation

(Fung, 1990)

Aethalometer (Hansen et al., 1984, 1988, 1989; Hansen and Rosen, 1990)

and Radiance particle soot absorption photometer (PSAP, Bond et al., 1998;

Quinn et al., 1998) for black carbon

Differential thermal combustion or

desorption (Cachier et al., 1989;

Schmid et al., 2001)

J.C. Chow et al. / Chemosphere 49 (2002) 961–978 963

require simultaneous gas and particle measurements

at the surface and aloft with novel measurement

devices. Simultaneous surface and upper air meteoro-

logical monitoring is also needed. Research networks

are often operated for a few days or weeks during

pollution episodes and need sampling locations rep-

resenting various spatial scales.

• Determination of health effects: PMmeasurements are

needed to estimate exposure for use in epidemiologi-

cal studies, to assess human exposure, and to identify

toxic components of PM mass. Particle size, chemical

composition, and particle number are needed with

other variables that might cause respiratory and car-

diovascular distress. For acute epidemiology and ex-

posure studies, 1-h or continuous measurements may

be needed as well as 24-h measurements. For studies

of chronic epidemiology, measurements that inte-

grate over a week to a month may be more cost-effec-

tive than hourly or daily concentration averages, but

these must continue for many years. For dosimetric

studies and modeling, particle size distributions and

their changes with relative humidity and temperature

must be known. Health outcomes are affected by

non-pollution variables, such as temperature and

weather, that are often unquantified.

• Determination of ecological effects: Chemical concen-

trations in rain, fog, and dew are required to under-

stand the contributions of PM to soiling of surfaces,

damage to materials, and wet and dry deposition of

acidity and toxic substances to surface water, soil,

and plants. Some differentiation into particle size is

needed to determine dry deposition.

• Determination of radiative effects: PM directly affects

climate and visibility by scattering and absorbing

light. PM indirectly affects climate by providing cloud

condensation nuclei, thereby changing the albedo

and lifetimes of clouds. To understand these effects,

information on refractive index (including the ratio

of scattering to absorption), size distribution, and

changes in particle size with relative humidity is re-

quired. Measurements are needed that correspond

to daylight hours rather than over an entire 24-h

period (Watson, 2002).

As noted above, measurement attributes appropriate

for one of these objectives are not necessarily adequate

for others, even though the same variables are quanti-

fied.

3. Sampling uncertainties

The first uncertainty is sampling error due to popu-

lation variability, which can be assessed from a random

sample, while the second uncertainty is ascribed to ex-

perimental error from inadequate sampling procedures

and imprecise monitoring or analytical equipment. Bi-

ases may arise from:

• Sample number: Monitoring results, expected to be

free of bias, are obtained by randomly selecting

samples (observations) or by measuring the total

population, as in continuous monitoring. Sampling

conducted at set intervals, such as a specific time of

the day or day of the week, may be biased even

though a set sampling schedule is more practical to

implement. US PM networks have typically applied

a sixth-day sampling strategy to randomize effects

of emissions that differ by day of the week.

• Particle size fraction: Fine particles (PM2:5) are more

uniformly distributed over a region. PM10, PMcoarse

(difference between PM10 and PM2:5), and total

suspended PM (TSP, particles with aerodynamic

diametersK 40 lm) contain larger particles that candeposit close to the source (Kotchmar et al., 1987;

Wilson and Suh, 1997). PM2:5 also contains second-

ary sulfates and nitrates that form from sulfur diox-

ide and oxides of nitrogen gases emitted into the

atmosphere and tend to be more uniformly distrib-

uted in space than directly emitted particles com-

posed of carbon and geological materials.

• Particle composition:Water-soluble and volatile com-

pounds can bias PM measurements depending on the

temperature and relative humidity history of the

sample. Special problems are caused by: (1) water ab-

sorbed by the particles that increase PM mass; (2)

volatile and semivolatile organic compounds (VOCs

and SVOCs) that evaporate from particles, thereby

reducing mass; (3) semivolatile inorganic compounds

such as ammonium nitrate that evaporate and reduce

mass; and (4) inorganic and organic gases such as

ammonia, sulfur dioxide, oxides of nitrogen, and

polar organic compounds that adsorb onto particles

or filter media, thereby increasing mass. Non-volatile

particles include most fugitive dust, sulfates, and sta-

ble organic compounds.

4. Environmental sampling theories

Environmental sampling networks have been studied

in hydrology (Andricevic, 1990; Kassim and Kottegoda,

1991; Woldt and Bogardi, 1992; Meyer et al., 1994),

meteorology (Gandin, 1970), and the geological sci-

ences (Camisani-Calzolari, 1984; de Marsily et al., 1984;

Russo, 1984). Only a few of these concepts have been

adapted to air quality networks (Munn, 1981). Statisti-

cal methods take advantage of the fact that most air

quality measurements are correlated either in time at the

same location or in space with other monitors in a net-

work. Networks can be optimized by examining time

series correlations from long measurement records or


spatial correlations among measurements from many

nearby monitors (Munn, 1975; Elsom, 1978; Hands-

combe and Elsom, 1982). Munn (1981) identifies

four types of correlation analysis: (1) time correlation

(autocorrelation) at one site; (2) cross-correlation of

several pollutant concentrations at one site; (3) spatial

correlations among simultaneous measurements at dif-

ferent sites; and (4) spatial correlations among different

sites with time lags. Other statistical measures include:

(1) the coefficient of geographic variation (Stalker and

Dickerson, 1962a,b; Stalker et al., 1962), (2) structure

functions (Goldstein et al., 1974; Goldstein and Lan-

dovitz, 1977a,b), (3) cluster analysis (Sabaton, 1976),

(4) principal component analysis (Peterson, 1970; Sab-

aton, 1976), (5) the variational principle (Wilkins, 1971),

and (6) linear programming (Darby et al., 1974; Hou-

gland, 1977).

Statistical sampling includes four approaches: (1)

random sampling, (2) systematic sampling, (3) judg-

mental sampling, and (4) geostatistical sampling. Ran-

dom sampling locates sites by chance, without taking

into consideration the sources of pollutants (Nesbitt and

Carter, 1996). Random placement is accomplished by

specifying boundaries of a rectangular domain, gener-

ating x and y coordinates from a uniform-distribu-

tion random number generator limited by the domain

boundaries, and placing samplers as close to these co-

ordinates as practical. Random sampling designs have

several advantages: (1) sampling bias is eliminated or at

least minimized; (2) simplicity of implementation with

no knowledge assumed about the spatial and temporal

distribution of concentrations; and (3) objective selec-

tion of sampling locations. The disadvantages are that:

(1) many sampling locations are needed for an accept-

able sampling error; (2) there is large potential for re-

dundancy in a network with many locations; and (3)

there is large risk of poorly representing exposures in a

network with few locations.

Borgman et al. (1996) show that for a 95% confidence

interval, an accepted error of 1 lg/m3 and a variance, r2,of 6.5 (lg/m3)2, the estimated minimum number of ob-servations (n) can be calculated from:

ffiffiffi

np

¼ 1:965r ð1Þ

yielding n ¼ 25. This is reasonable if an annual averageis being estimated, if there are a large number of PM

sampling sites, samplers are randomly distributed in

space, and the measurements are independent.

Random network siting is not usually practical for

air quality monitoring. Prior knowledge, though some-

times incomplete, is always available concerning the

sources and meteorology that affect PM concentrations

in an area. Sampler siting constraints of power, security,

accessibility, and minimum separations from nearby

emitters and obstructions impose logistical constraints

that prevent a purely random selection of measurement

locations. A community exposure monitoring philoso-

phy is not served by a random-sampling network design.

Systematic sampling overlays a grid of squares with

equal dimensions and spacing on a study domain with

one sampler assigned to each grid cell. This ‘‘area

method’’ (Noll and Miller, 1977) is most applicable in

flat terrain with a few large point sources or with nu-

merous area sources. Samplers are placed as close to the

center of the cell as practical. This method minimizes

sampling bias because of its regular sampler spacing.

However, systematic sampling requires a substantial

number of samplers, depending on the size of the study

domain, and most of these samplers supply redundant

information where PM concentrations are spatially

uniform. Owing to the large number of sites, costs of

random and systematic sampling designs may be pro-

hibitively high except for short periods during which

spatial uniformity is being evaluated.

Judgmental sampling (Nesbitt and Carter, 1996) uses

knowledge of source emissions and sensitive recep-

tor locations, coupled with mechanisms for pollutant

transport, to locate measurement sites. Noll and Miller

(1977) call this the ‘‘source orientation method’’. Air

pollution models can be used to assist in this judgment,

but this requires exceptional accuracy of the model

formulation and input data. Few areas have good esti-

mates of particle and precursor gas emissions, especially

from mobile and area sources. Complex terrain and

meteorology, as well as simulating secondary aerosol

formation, also present challenges to currently available

models for suspended particles.

Judgmental sampler locations may be determined by

data from an existing monitoring network or by iden-

tifying the locations of pollutant sources and inferring

pollutant transport from data analysis of emissions and

wind measurements. Short-term experiments involving

spatially dense measurements and modeling may assist

in making or verifying judgments. Compliance moni-

toring networks almost always use judgmental sampling

strategies that consider where source emissions are lo-

cated in relation to population centers and meteorology.

Judgmental sampling may be used to monitor hot spots

for compliance monitoring or to identify sites repre-

sentative of community exposure for epidemiology.

Nesbitt and Carter (1996) combine judgmental and

systematic sampling by applying the following steps: (1)

identify potential sources of contamination or hot spots

using existing measurements or models, (2) place a grid

system over these areas, (3) sample at these grid points,

(4) define a systematic grid at points that yield positive

contamination, and (5) use the systematic grid to assess

the remainder of the study area.

Fig. 1 shows how a judgmental sampling strategy

compares with a combined judgmental and systematic

sampling strategy. The concentration isopleths can


be interpolated from spatially dense measurements or

produced by an air quality model. The judgmental

strategy, by itself, misses areas of significant concentra-

tions, while the combined judgmental and systematic

strategy covers the areas of significant concentration

that have not previously been monitored.

Another hybrid method for determining potential

locations of PM samplers is based on geostatistical

sampling (Journel, 1980; Russo, 1984; Kassim and Kot-

tegoda, 1991; Trujillo-Ventura and Ellis, 1991; Rouhani

et al., 1992a,b; Borgman et al., 1996). Kriging is a

common method for interpolation to estimate unknown

values from existing spatial data (Volpi and Gambolati,

1978; Lefohn et al., 1987; Venkatram, 1988). Kriging

uses the correlation structure to produce an estimator

with the least mean square error and results in reduced

sample size compared to other methods.

Guidance for PM compliance networks (Watson

et al., 1997) is based on judgmental network design.

These PM networks should be periodically evaluated

and redesigned to better define PM concentration gra-

dients, human exposure, and source contributions.

Because of their nature and sources, fine particles

such as PM2:5 are difficult to model (Seigneur et al.,

1997; Seigneur, 2001). Numerical models for PM are still

under development and can only be used qualitatively

for network design. Source-oriented models simulate

atmospheric diffusion or dispersion and estimate concen-

trations at defined receptors. Numerical source models

can be grouped as kinematic, first-order closure, or

second-order closure models (Bowne and Londergan,

1983). Kinematic models are the simplest both mathe-

matically and conceptually. These models simplify the

non-linear equations of turbulent motion, thereby per-

mitting a closed analytical approximation to describe

pollutant concentration (Green et al., 1980). First-order

closure models are based on the assumption of an iso-

tropic pollutant concentration field. Consequently, tur-

bulent eddy fluxes are estimated as being proportional

to the local spatial gradient of the transport quantities.

Eulerian grid models, Lagrangian particle models, and

trajectory puff/plume models are included in this cate-

gory. Second-order closure models involve a series of

algorithm transformations of the equations of state,

mass continuity, momentum, and energy by using the

Boussinesque approximation and Reynolds decomposi-

tion theory (Holton, 1982; Stull, 1988).

5. Spatial scales

Monitoring sites should be selected to represent

several spatial scales as defined below (US Environ-

mental Protection Agency, 1997). Distances indicate the

diameter of a circle, or the length and width of a grid

square, with a monitor at its center:

• Collocated or indoor scale or ducted emissions (1–10

m): Collocated monitors are intended to measure

the same air. Collocated measurements are often used

to define the precision of the monitoring method.

Different types of monitors are operated on collo-

cated scales to evaluate the equivalence of mea-

surement methods and procedures. The distance

between collocated samplers should be large enough

to preclude the air sampled by any of the devices

from being affected by any of the other devices but

small enough so that all devices obtain air containing

the same pollutant concentrations. Effluent pipes and

smoke stacks duct emissions from industrial sources

to ambient air. Pollutants are usually most concen-

trated in these ducts and are monitored to create

emissions inventories as well as to determine compli-

ance with emissions standards. There may be some

variability across the duct that can be compensated

for by longer averaging times and traverses to obtain

a composite sample.

• Microscale (10–100 m): Microscale monitors are

most often used to assess human exposure. These

monitors show differences from compliance monitors

when the receptor is next to a low-level emissions

source, such as a busy roadway. Ambient compliance

monitoring site exposure criteria avoid microscale in-

fluences even for source-oriented monitoring sites,

while source emissions monitors avoid them because

they represent emissions from a variety of sources.

Microscale sites are usually operated for short peri-

ods to define the zones of representation for other

sites and to estimate the zones of influence for ducted

and non-ducted emitters. These sites are also used to

estimate emission rates and compositions for non-

ducted sources such as suspended road dust (e.g.,

Gillies et al., 1999).

• Middle scale (100–500 m):Middle-scale monitors are

also used for human exposure studies (e.g., Engelbr-

echt and Swanepoel, 1998; Watson and Chow, 2001b;

Chow et al., 2002a), to evaluate contributions from

Fig. 1. Examples of judgmental and hybrid sampling strategies.


large industrial facilities, and to evaluate the zones of

representation of compliance sites. They are also used

for process research to examine rapid changes in pol-

lutant composition, dilution, and deposition. For air

quality research, vertical resolution of pollutant con-

centrations on this scale (e.g., measurements on roofs

of tall buildings or hilltops) elucidates mechanisms

of day-to-day carryover, long-range transport, and

nighttime chemistry that cannot be understood by

surface measurements.

• Neighborhood scale (500 m to 4 km): Neighborhood-

scale monitors are used for compliance to protect pub-

lic safety and show differences that are specific to

activities in the district being monitored. The neigh-

borhood-scale dimension is often the size of emissions

and modeling grids used for air quality source appor-

tionment in large urban areas, so this zone of repre-

sentation of a monitor is the only one that should be

used to evaluate such models. Sources affecting neigh-

borhood-scale sites typically consist of small individ-

ual emitters, such as clean, paved, curbed roads,

uncongested traffic flow without a significant fraction

of heavy-duty vehicles, or neighborhood use of resi-

dential heating and cooking devices (Chow et al.,

1992, 1999, 2002b; Chow and Watson, 2001).

• Urban scale (4–100 km): Urban-scale monitors are

most common for ambient compliance networks

and are intended to represent the exposures of large

populations. Urban-scale pollutant levels are a com-

plex mixture of contributions from many sources that

are subject to areawide control. Urban-scale sites

are often located at higher elevations or away

from highly traveled roads, industries, and residential

wood-burning appliances. Monitors on the roofs of

two- to four-story buildings in the urban core area

are often good representatives of the urban scale

(Engelbrecht et al., 1998).

• Regional-scale background (100–1000 km): Regional-

scale monitors are typically located upwind of urban

areas and far from source emissions. Regional mon-

itors are not necessary to determine compliance,

but they are essential for determining emissions

reduction strategies. A large fraction of certain pollu-

tants detected in a city may be due to distant emitters,

and a regional (rather than local) control strategy

may be needed to reduce outdoor exposure. Re-

gional-scale concentrations are a combination of nat-

urally occurring substances as well as pollutants

generated in urban and industrial areas that may be

more than 100 km distant (Chow et al., 1996). Re-

gional-scale sites are best located in rural areas away

from local sources, and at higher elevations.

• Continental-scale background (1000–10 000 km):

Continental-scale background sites are located hun-

dreds of kilometers from emitters and measure

a mixture of natural and diluted manmade source

contributions. Anthropogenic components are at

minimum expected concentrations. Continental-scale

monitors determine the mixture of natural and an-

thropogenic contaminants that can affect large areas.

The InteragencyMonitoring of PROtected Visual En-

vironments (IMPROVE) network has been used ex-

tensively in the US to determine PM2:5 compositions

and their effects on visibility in National Parks and

Wilderness Areas (Eldred et al., 1997).

• Global-scale background (>10 000 km): Global-scale

background monitors quantify concentrations trans-

ported between different continents as well as natu-

rally emitted particles and precursors from oceans,

volcanoes, and windblown dust. These are located

in isolated spots such as McMurdo in Antarctica

(Mazzera et al., 2001), Mauna Loa in Hawaii

(Holmes et al., 1997), and Barrow, Alaska (Polissar

et al., 1999).

The zone of representation for a monitoring site is

often not evident in the absence of measurements from

nearby locations. Chow et al. (1999) show that larger

concentrations within urban areas are often represented

by samplers located near roadways or in neighborhoods

with construction. Most of the sites complying with the

urban-scale criteria denoted above showed similar

concentrations. Regional concentrations were achieved

within distances outside the urban area that are com-

parable to the dimensions of the city and are fairly

similar even over large distances. Zones of representa-

tion are different for different pollutants.

6. Temporal scales

Assessment of outdoor human exposure requires

time scales ranging from minutes to years. These are

defined by sampling period, sample duration, and sam-

pling frequency.

6.1. Sampling period

PM concentrations vary from year to year and season

to season, and multi-year monitoring is needed to detect

some of these changes. Fig. 2 shows annual average and

maximum PM2:5 concentrations from 1991 through 1998

at Fresno, CA, measured with a dichotomous sampler

operating on a sixth day sampling frequency. Substan-

tial differences in annual average concentrations occur

from year to year at the Fresno site. The average of the

yearly 98th percentiles (the second-highest value corre-

sponds to the 98th percentile for the sixth-day sampling

schedule) exceeded the 24-h PM2:5 NAAQS of 65 lg/m3

between 1991 and 1995. For the more recent period

between 1996 and 1998, this average did not exceed 65

lg/m3, although several individual values were in excess


of the 24-h NAAQS. These extreme values have been

relatively rare occurrences, as the 75th percentile is

substantially lower than the highest values. The high

values tend to influence the average more than the me-

dian (Fig. 2).

Different seasons may also show substantial concen-

tration variability. Fig. 3 shows that the highest seasonal

average concentrations occur during winter at Fresno.

These seasonal averages are lower during fall (with the

exceptions of 1993 and 1995), but still substantially

higher than those for spring and summer. Maximum 24-

h PM2:5 concentrations are found during both winter

and fall. With rare exceptions, spring and summer

experience low averages of 10 lg/m3 or less and lowmaximum concentrations. Fig. 3 also shows that PM2:5

concentrations vary among winter seasons of different

years.

6.2. Sampling duration

PM2:5 and PM10 have typically been measured as 24-

h averages because this was the approximate time nee-

ded to extract enough particles onto a filter for accurate

gravimetric analysis. In situ particle measurement

technologies (Klein et al., 1984; Patashnick, 1987; Chow,

1995; Tsai and Cheng, 1996; Watson et al., 1998;

McMurry, 2000) have been used to approximate hourly

particle concentrations in some networks and pro-

vide detailed information about how outdoor exposure

changes throughout the day and at different locations.

Fig. 4 illustrates diurnal PM10 variations between

weekdays and weekends at various locations in Las

Vegas, NV, during 1995 (Chow and Watson, 1997).

Median (i.e., 50th percentile) PM10 concentrations for

weekdays and weekends in the north (Craig Road),

southeast (East Charleston), east downtown (City Cen-

ter), and west (Walter Johnson) sides of the Las Vegas

Valley are shown. For both weekdays and weekends, the

two sites on the fringes of Las Vegas (Craig Road and

Walter Johnson) exhibit similar patterns. The two urban

sites (East Charleston and City Center) exhibit similar

patterns. All four sites show a pronounced decrease in

the size of the morning peak on weekends. The urban

sites show concentrations peaking later than the other

two sites with persistence into the early morning hours

on both weekdays and weekends. In the early afternoon

on weekends, all sites have similar 50th percentile con-

centrations of about 20 lg/m3. PM10 decreases quickly

during late evening at the less-urban sites, which is ex-

pected when people return to their homes after work. At

the downtown urban sites, however, traffic near casinos

and restaurants continues through the late evening.

Traffic, in conjunction with low mixing heights and light

winds at this time of day, is largely responsible for the

high PM10 concentrations into the early morning hours

at these sites. Fig. 5 shows a different temporal pattern

resulting from residential coal combustion in South

African townships during the early mornings and late

afternoons when cooking fires in the braziers and stoves

are lit (Engelbrecht and Swanepoel, 1998). Peak pollu-

tion levels vary greatly from background values to

nearly 1400 lg/m3 depending on wind speed and direc-tion.

Chow (1995) observed that US particulate standards

are based on health relationships with measurements

Fig. 2. Annual average and maximum PM2:5 from 1991 to 1998 at the California Air Resources Board�s dichotomous sampler networksite in Fresno, CA. Whiskers and boxes in plot represent the 10th, 25th, median, mean (thick line), 75th, and 90th percentiles. Circles

represent data points above the 90th and below the 10th percentiles.


acquired from networks designed to determine compli-

ance with previous standards. For suspended particles,

this practice has perpetuated 24-h filter sampling

that does not recognize some of the potentially high

short-term concentrations that may be a hazard. Shorter-

duration measurements that are collocated with long-

duration measurements for a limited time are needed to

evaluate the extent to which the longer-duration mea-

surements are achieving their desired objectives. The

hourly measurements shown in Figs. 4 and 5 can also be

used for alerts, even though 24-h measurements are

adequate for determining compliance with current

NAAQS.

6.3. Sampling frequency

Long-term averages need less frequent samples than

monitoring for extreme events. Table 2 compares the

Fig. 3. Seasonal summaries of PM2:5 from 1991 to 1998 at the California Air Resources Board�s dichotomous sampler network site inFresno, CA. Whiskers and boxes in the plot represent the 10th, 25th, median, mean (thick line), 75th, and 90th percentiles. Circles

represent data points above the 90th and below the 10th percentiles.

Fig. 4. Diurnal variations of hourly PM10 mass concentrations acquired with beta attenuation monitors (BAM) during winter 1995 at

several locations in Las Vegas, NV, for: (a) weekday, and (b) weekend (Chow and Watson, 1997).

Fig. 5. Diurnal variations of 30-min PM10 concentrations ac-

quired with tapered-element oscillating microbalances (TEOM)

in Qalabotjha, South Africa, during stagnant atmospheric

conditions.


annual average and standard deviation, highest, second-

highest, and several upper percentiles of 24-h PM10

concentrations for samples taken at daily and second-,

third-, sixth-, twelfth-, and thirtieth-day intervals. US

PM2:5 standards require sampling frequencies of every

day to every sixth day, with more frequent sampling in

communities with concentrations near or in excess of the

standard.

Table 2 shows that annual averages for PM10 are

reasonably consistent with sampling frequencies up to

one in twelve days. However, the maximum and second-

highest concentrations are very sensitive to sampling

frequency, regardless of the lag between samples. The

upper percentiles also have the possibility of significant

error, especially when relatively few samples are avail-

able. The US PM2:5 and PM10 air quality standards are

expressed in terms of percentiles or single expected ex-

ceedance concentrations over three-year intervals to in-

crease the consistency of the measurement. If values for

maximum concentrations and precise peak value statis-

tics are desired, however, sampling frequencies must be

continuous and complete. Extreme values are rare events

that do not always obey standard statistical distributions

(de Nevers et al., 1977). These generalizations from

Table 2 for PM10 are consistent with those found from

other locations and time periods (Watson et al., 1981).

Distributions of environmental data can often be

summarized in terms of a simple mathematical function

with two parameters (Walpole and Meyers, 1978). The

lognormal distribution is most applicable to underlying

populations of pollutant concentrations (de Nevers et al.,

1979). The lognormal distribution describes many

naturally occurring processes such as gold ore concen-

tration in mines (Krige, 1978), geology, agriculture,

medicine, psychophysical phenomena, and even literary

research (Johnson and Kotz, 1970). Fig. 6 illustrates a

lognormal distribution for PM10 concentrations from a

South African region characterized by heavy industry,

coal mines, coal-fired power generation and residential

coal-burning smoke. The normal distribution also de-

scribes various naturally occurring processes (Albert and

Horwitz, 1996). Many such continuous distributions of

random variables can be represented by the character-

istic bell-shaped curve (Walpole and Meyers, 1978) as

illustrated in Fig. 7. The statistics for this distribution

are relatively simple to calculate and have been applied

widely to naturally occurring processes. Statistical in-

ference, which deals with the estimation of population

parameters and tests of hypotheses, is readily executed

on samples that show at least approximate Gaussian

distributions (Albert and Horwitz, 1996). Samples dif-

fering from a Gaussian distribution can be dealt with by

applying non-parametric statistics (Siegel, 1956; Ott,

1995) or transforming the data (Box et al., 1978). The

Central Limit Theorem states that the averages of ran-

dom sample sets taken from any population, regardless

of distribution, will show an approximate Gaussian

distribution in the limit as the number of sample sets

approaches infinity (Williams, 1978). Fig. 7 shows the

distribution of 100 average values from Leandra, South

Africa. From a sample average and a known standard

deviation, a confidence interval for the population mean

(l) at various levels of confidence can be calculated(Walpole and Meyers, 1978) as:

Table 2

Effects of sampling frequency on statistical indicators for daily PM10 concentrations (lg/m3) acquired with beta attenuation monitors

Sampling

frequency

Annual average

� standard deviationMin Max Second max 99th% 98th% 95th% 90th%

Every day 80� 63 6 316 309 286 246 206 170

2nd day 89� 63 10 198 286 277 251 206 182

3rd day 87� 62 6 284 263 264 245 208 170

6th day 89� 62 10 263 233 250 236 219 170

12th day 78� 60 10 233 202 227 221 202 146

30th day 101� 74 38 263 104 255 247 222 181

1998 data from Leandra, Mpumalanga, South Africa, courtesy of Eskom.

Fig. 6. Graphical representation of daily PM10 continuous

ambient measurements from Leandra, South Africa, taken for

the 1998 calendar year. The distribution closely resembles the

lognormal distribution.


�xx� za=2rffiffiffi

np < l < �xxþ za=2

rffiffiffi

np ð2Þ

where �xx is the sample average for n observations, r2 isthe population variance, and za=2 is the value of thestandard deviation leaving an a=2 area at the tail ends ofthe normal distribution.

In the case of the Leandra samples, and for a known

PM10 population average and standard deviation of

80� 63 lg/m3, the 95% confidence interval is calculatedfor a sample size of 30 as between 57 and 103 lg/m3. Ifthe population standard deviation is not known, it can

be approximated by the sample standard deviation. A

longer sampling interval is required to estimate the pop-

ulation mean with a higher confidence interval (Walpole

and Meyers, 1978). The ideal situation would be to

sample everyday so there would be no uncertainty about

the population mean.

The confidence interval also provides an estimate of

the precision of the sample mean (Walpole and Meyers,

1978), i.e., the statistical uncertainty or sample error

which is generally expressed as the difference between

the population and the sample means. This is estimated

based on the population standard deviation and the

sample size:

e ¼ za=2rffiffiffi

np ð3Þ

Table 3 presents the errors for various sample sizes,

given the known standard deviation of 63 lg/m3. Thisemphasizes the importance of estimating the population

variance from a reasonably large (n > 30) randomsample before setting the sample size for a full-scale

study.

Table 4 compares the statistics of samples collected

on certain days of the week and every sixth day with a

random sample selected for its similarity to the popu-

lation (Walpole and Meyers, 1978). The sample means

are tested for their similarity to the random sample ac-

cording to the null hypothesis. All calculated t-statistics

fall within the tabulated critical range of �1.98. Thetabulated critical value at a 0.05 level of significance

(Walpole and Meyers, 1978) implies that sampling on

any weekday is acceptable. However, the every-sixth-day

sampling schedule reports a t-statistic close to zero and

shows the greatest similarity to the random sample and

is the preferred sampling frequency. This is due to the

fact that every-sixth-day sampling gives an equal num-

ber of samples on each day of the week. The Sunday

sample varies most from the random sample and sub-

sequently introduces the greatest bias. This is explained

by the fact that a large proportion of the Leandra PM

originates from residential coal combustion in neigh-

boring townships, and that cooking activities on Sun-

days differ from those on normal working days.

7. Designing an outdoor exposure network

Watson et al. (1997) outline the following steps that

can be followed when designing an outdoor exposure

network:

1. Identify political boundaries of populated areas: Plot

populated entities (Census Bureau Metropolitan Sta-

tistical Areas, counties, zip code areas, census tracts,

or census blocks). Identify where the majority of the

Table 3

The variation of random error with sample size for the 1998 data from Leandra, South Africa

Sample size (n)

365 182 122 61 30 12 6

Sampling frequency, days

(approximate)

1 2 3 6 12 30 60

Error (lg/m3) 6 9 11 16 23 36 50

Interval (lg/m3) �xxa� 6 �xx� 9 �xx� 11 �xx� 16 �xx� 23 �xx� 36 �xx� 50a�xx represents the population mean. At 95% interval, za=2 ¼ 1:96 given the known standard deviation of 63 lg/m3 in Eq. (3).

Fig. 7. Graphical representation of 100 random sample means

of 30 observations each, from Leandra, South Africa, calcu-

lated from continuous daily measurements for the 1998 calen-

dar year. The distribution is approximately Gaussian.


people live. Identify a grouping of populated entities

that define a contiguous area and designate this as the

planning area.

2. Identify natural air basins: Compare outer boundaries

of the study area on a topographic map showing ter-

rain that might engender trapping, channeling, or

separation of source emissions from populated areas.

When terrain features are near the planning area

boundary, add or subtract population entities to cor-

respond as closely as possible to the terrain features.

When terrain features are significant within the plan-

ning area boundary, identify potential community

monitoring zones that are separated by ridges, lakes,

or valleys, or that are bounded on one edge by a sea-

coast.

3. Locate existing air quality monitoring sites: Plot the

locations of existing monitoring sites, if any, within

the planning area. Examine the extent to which these

correspond to populated areas. Identify large dis-

tances between existing sites, and identify sites that

appear to represent the same sizes of populated areas.

Evaluate the justification for excluding existing sites

outside of the initial planning area boundaries. If

these are community-oriented sites, extend the initial

monitoring boundaries with populated entities to in-

clude these sites. Alternatively, evaluate these sites for

potential as special monitoring, transport, or back-

ground sites.

4. Locate emissions sources and population: Plot major

land use within the populated entities within the cat-

egories of commercial, residential, industrial, or agri-

cultural. Plot the major roadways. Plot emissions

from major point sources for primary PM, sulfur di-

oxide, and oxides of nitrogen. Use a gridded emis-

sions inventory or maps of source type and density,

if available. Each monitoring site in a monitoring

zone should be affected by similar emission sources.

Determine which populated areas coincide with or

are in close proximity to areas of high source density

and which are in areas of low source density. When

evaluating community exposures to emissions, con-

sider populations at work and leisure activities, as

well as at home. Population density is important both

for determining exposure and for estimating emis-

sions from vehicles, cooking, wood-burning, etc.

5. Identify meteorological patterns: Plot wind directions

and speeds, vertical temperature structure, and fre-

quencies of fogs by season. Determine how these vary

within and around the initial area to be represented

by monitoring. Extend the dimensions of community

monitoring zones that include large source emissions

in the downwind direction, using terrain as a guide

for potential channeling.

6. Compare PM concentrations: Estimate the spatial

uniformity of average and maximum concentrations

from previous measurements or model calculations

within the potential community monitoring zones

for annual, seasonal, and maximum PM concentra-

tions. Combine zones surrounding neighborhood-

scale monitors for which these concentrations are

similar.

7. Locate sites:Where existing sites are within each mon-

itoring zone, give them first priority of PM2:5 monitor-

ing when they meet siting criteria. Where monitoring

zones do not contain existing sites, select new sites.

8. Particulate monitor siting criteria

There are many local sources of suspended particles,

and sampling sites may be affected by these sources if

they are too close. Internal requirements are those for

Table 4

Introduction of bias into results by selecting specific weekdays on which to sample

Daily average

PM10 (lg/m3)Popu-

lation

53 Randomly

selected days

Days of the week Every

6th daySun Mon Tue Wed Thu Fri Sat

Mean 80 79 66 83 93 82 80 84 75 78

Std dev. 63 63 47 65 62 66 77 61 63 67

Min 6 7 9 13 23 7 9 9 6 7

Max 316 233 202 316 248 286 309 238 263 263

90th percentile 170 180 124 169 202 153 191 168 167 176

95th percentile 206 201 139 189 210 232 261 192 209 212

98th percentile 246 215 192 217 227 246 299 200 222 233

99th percentile 286 224 197 267 238 266 304 219 242 247

Sample pair Ran53-

Ran53

Ran53-

Sun

Ran53-

Mon

Ran53-

Tue

Ran53-

Wed

Ran53-

Thu

Ran53-

Fri

Ran53-

Sat

Ran53-

6th Day

t-statistic 0 1.1446 �0.3308 �1.0391 �0.1825 �0.0540 �0.3623 0.3100 0.0515

Weekday means are compared to that of a random sample of 53 days, at a 0.05 level of significance. Samples from a daily PM10 data set

(lg/m3) acquired with beta attenuation monitors. 1998 data from Leandra, Mpumalanga, South Africa, courtesy of Eskom.


operating the needed instruments, while external criteria

address site surroundings to achieve specific monitoring

purposes. Internal criteria include:

• Long-term site commitment: Sites are meant to mea-

sure trends as well as compliance, and a long-term

commitment from the property owner for continued

monitoring is required. Public buildings such as

schools, fire stations, police stations, recreation halls,

and hospitals often have more stability and a motive

for public service than do private or commercial

buildings.

• Sufficient operating space: A large, flat space, elevated

at least 1 m but no more than 14 m above ground

level, is needed to place monitors and monitoring

probes. The space available for samplers should be

at least 5 m distant and upwind (most common wind

direction) from building exhausts and intakes and at

least 2 m from other monitoring instruments� inletsand exhausts, as well as away from walls, parapets,

or penthouses that might influence air flow. Buildings

housing large emitters, such as coal-, waste-, or oil-

burning boilers, furnaces or incinerators, should be

avoided.

• Access and security: Access to the sampling platform

should be controlled by fencing or elevation above

ground level. Sampler inlets should be sufficiently dis-

tant (>10 m) from public access to preclude purpose-ful contamination from reaching them in sufficient

quantities to bias samples. Access should be con-

trolled by a locked door, gate, or ladder with docu-

mentation of site visitations and the purposes of

those visits.

• Safety: Wiring, access steps, sampler spacing, and

platform railings should comply with all relevant

codes and workplace regulations, as well as common

sense, to minimize potential for injury to personnel or

equipment.

• Power: Power should be sufficient for the samplers to

be operated on a long-term basis, as well as for spe-

cial study and audit samplers to be located at a site.

Where possible, a separate circuit breaker should be

provided for each instrument to prevent an electrical

malfunction in one monitor from shutting off power

to the other monitors at the site.

• Environmental control: Environments surrounding

monitoring instruments should be maintained within

the manufacturers specifications for proper instru-

ment function. Most filter-based PM samplers are

designed to operate under a wide range of environ-

mental conditions and can be located outdoors in

most types of weather. Several continuous moni-

toring methods may require environmental shelters

with temperature and humidity controls to protect

their electronic sensing and data acquisition mecha-

nisms.

These criteria may be tightened or relaxed for special-

purpose, transport, and background monitors. For ex-

ample, battery-powered portable monitors (Baldauf

et al., 2001) may be located on utility poles at various

elevations to assess zones of influence and zones of

representation for sources and receptors (Chow et al.,

1999, 2002b).

External siting criteria refer to the environs sur-

rounding a measurement location, and these differ de-

pending on the zone of representation intended for a

specific monitoring site:

• Exposure: Large nearby buildings and trees extend-

ing above the height of the monitor may present bar-

riers or deposition surfaces for PM. Certain trees

may also be sources of PM in the form of detritus,

pollen, or insect parts. Interferences can be mini-

mized by locating samplers > 20 m from nearby treesand twice the difference in elevation from nearby

buildings or other obstacles.

• Distance from nearby emitters: The monitor should

be outside the zone of influence of sources located

within the designated zone of representation for the

monitoring site. Neighborhood and urban zones of

representation are needed for community-oriented

compliance monitors. These should generally be at

least 1 km from very large, visibly identifiable source

areas occupied by major industries such as cement

and steel production or ore processing. A minimum

distance of �50 m from busy paved highways is usu-ally outside the road�s immediate zone of influencefor a rooftop monitor. These siting criteria were

established for PM10 monitoring siting (US Environ-

mental Protection Agency, 1987), and they have pro-

ven their validity in PM10 network design. For larger

than middle-scale monitoring, no unpaved roads with

significant traffic or residential wood-burning appli-

ances should be located within 100 m of the monitor-

ing location. Background monitoring sites should be

located >100 km from large population centers, and>100 m from roads and wood-burning (burning is

common, though often intermittent, in camping, for-

ested, and agricultural areas).

• Proximity to other measurements: Other air quality

and meteorological measurements can aid in the in-

terpretation of high PM levels, and with all other

considerations being equal, PM2:5 sites should give

preference to existing sites that make other measure-

ments. For example, high local wind gusts may ex-

plain high PM readings as caused by wind blown

dust. These gusts are often localized, and would not

be detected on a more distant monitor. Similarly, a

strong correspondence between hourly carbon mon-

oxide and PM readings would indicate that locally

emitted vehicle exhaust is a large contributor at that

site. This conclusion would be more tenuous if the


carbon monoxide measurements were not collocated.

In particular, collocating PM2:5 and PM10 monitors

will provide information on both fine and coarse

fractions of suspended particles.

9. Evaluating zones of representation

A site originally selected to represent community

exposure (generally on a neighborhood or urban scale)

may have its zone of representation change owing to

long-term changes in land use or short-term events that

affect that particular site. The following approach can be

taken to evaluate individual site representation:

• Annual site surveys: The land use and sources around

a monitoring site may change from year to year, espe-

cially in high-growth areas. Maps should be updated

as part of the annual measurement network sum-

mary, and the setbacks from emissions sources and

obstructions should be re-evaluated to ascertain that

they are still met.

• Records of intermittent events: High PM2:5 or PM10

concentrations may have corresponded to a specific

event, such as construction or a fire, occurring near

the measurement location. Visual events should be

recorded as part of the measurement network main-

tenance, and these should be summarized at the end

of each year for inclusion in the annual network eval-

uation report.

• Satellite monitoring studies: To evaluate the zones of

representation of sites, many monitors may be lo-

cated at different distances around and between

monitors in a monitoring zone. Spatial uniformity

measures can be determined for these temporary lo-

cations and compared to those from the sites within

the monitoring zone to evaluate how well the long-

term sites represent population exposures to PM.

10. Conclusions

Designing an effective PM sampling network to rep-

resent outdoor human exposure requires more than

simply selecting measurement locations. For a given

level of resources, a balance is needed between the

number of sites, the sampling period to be represented,

sample duration and frequency, and the variables mea-

sured. These are different for different network objec-

tives. Sampling designs that determine compliance with

standards are the most common air quality networks,

and relatively consistent guidance is available for these

networks. These criteria are not sufficient for other

monitoring objectives, and more specific guidance is

needed for each of these other objectives.

Acknowledgement

This paper was supported in part by EPA. However,

it has not undergone technical review by EPA. There-

fore, the views are those of the authors and do not

represent EPA policy. Mention of trade names or

commercial products does not constitute EPA endorse-

ment or recommendation.

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