# On the Optimum Sampling Time for the Measurement of Pollutants in Ambient Air

Authors: RichardJ. C. Brown; Daniel Hood; AndrewS. Brown
Journal: Journal of Automated Methods and Management in Chemistry
 (2008-01-03)

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## Abstract

No measurement
result of any kind is complete without an associated statement of
uncertainty. In general, the aim is to minimise this measurement
uncertainty to the extent that the measurement made is fit for
purpose, or the uncertainty meets an acceptable level such as a
‘‘data quality objective’’. This paper
demonstrates how the sampling time used to measure a pollutant in
ambient air over a given time period, particularly with automatic
instruments, can affect the uncertainty of the measurement result.
It is shown that the uncertainty of the overall result depends
critically on how the precision characteristics of the measuring
instrumentation vary with analyte abundance. An example of the
determination of these characteristics for mercury vapour
measurement and the conclusions that may be drawn about optimum
sampling times to minimise uncertainty are presented. For
situations where uncertainty is minimised by the use of long
sampling periods, the implications of this strategy on the
‘‘information richness’’ of the data
and on the detection of short-term episodes, as well as peak
concentrations, are discussed.


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## Body

## 1. Introduction

Air pollution continues to be an issue of great concern to the
scientific and medical communities, as well as to members of the general
public, the media, and environmental pressure groups [1].
Several
requirements drive the need for accurate air quality measurements, including measurement of the exposure of the
general population to a variety of toxic compounds, assessment of compliance with
legislative limits or similar target values, informing policy development and
assessing the effectiveness of abatement strategies, and providing air quality information for the general public and
to input into other scientific research.
As a result, legislative limits have now been imposed on some elements
and compounds in ambient air. An
example of this is the EU Air Quality Framework Directive’s Fourth Daughter Directive (DD) [2],
which requires the measurement of benzo(a)pyrene, Ni, As, and Cd in the PM10 size
fraction (particulates with an aerodynamic diameter of 10 μm or less) of ambient air, total gaseous mercury (TGM), and the deposition of Ni, As, Cd, and Hg. This
DD includes ‘‘data quality objectives’’ that specify requirements for minimum
data capture and time coverage of measurements, and for the maximum
uncertainties ascribable to the measurement results. Therefore, it is important to
work to minimise the
uncertainty of these measurements so that they are fit for purpose in this context [3, 4]. This is particularly important during the
development of standard methods [5, 6]. Many parameters can have an influence on the
uncertainty of the measurement of a pollutant concentration over a given time
period; among these, sampling time is an important, but rarely considered,
variable.This paper examines the effect of sampling time on the overall
measurement uncertainty for automatic instruments that operate by accumulating
a pollutant sample for a given period, prior to making a measurement of the
collected pollutant in question.
Examples of this type of measurement strategy include the trapping of mercury vapour by gold-coated silica traps followed by
thermal desorption and measurement with atomic fluorescence, and the trapping of hydrocarbons by
polymer-based absorption tubes followed by thermal desorption and measurement
with gas chromatography. Whilst the
emphasis of this paper is on automatic techniques for air quality monitoring
(where there is usually no user intervention during the measurement process),
the general principle described here is also extendable to manual measurements
(where there is user intervention during the measurement process, often between
the sampling and analysis phases). It is shown that the uncertainty
of the overall result depends critically on how the precision characteristics
of the measuring instrumentation vary with analyte abundance.


## 2. Experimental

The principles described in this paper associated with how the
precision characteristics of measuring instrumentation vary with analyte abundance have been
illustrated using vapour-phase mercury measurements. Sampling for vapour-phase mercury
took place using low-volume
pumps (KNF Neuberger NMP 05) to draw air through ‘‘Amasil’’ (gold-coated silica)
adsorption tubes at a rate of 100 mL ⋅ min−1. A very short sampling
line consisting of approximately 5 cm of PTFE tubing connects the adsorption
tube to the ambient environment and is preceded by a 0.8 μm pore size filter to
remove particulates. Analysis of vapour-phase mercury samples takes
place using a PS Analytical Sir Galahad II analyser with
a fluorescence detector. The instrument is calibrated by the use
of a gas-tight syringe, making multiple
injections of known masses of mercury vapour onto the permanent absorption tube
of the analyser.
The
known masses of mercury vapour are generated using  mercury
in glass bell jar
apparatus, which allows a saturated vapour of mercury to develop within the air
in the bell jar,
which is in equilibrium with the atmosphere via a capillary tube [7].


## 3. Results and Discussion

Consider a given time periodt,
over which an average value for the mass concentration of a pollutant is
required. Consider that this measurement
is made by an automatic instrument, which accumulates the pollutant over a sampling period (defined by the user), ti,
prior to measuring the amount, or mass, of collected pollutant. Given that a negligible ‘‘changeover’’ time
between samples is assumed, we may write(1)tti=n,
where n is the number of sampling periods of equal
length during the whole time period. (Where sample ‘‘changeover’’ time
is not negligible, or data capture is less than 100%, the requirements of ISO
11222:2002 must also be considered [8].) It is assumed that the dominant uncertainty in such a measurement is the
repeatability of the instrumental response; this will be a very good
approximation for pollutants at low mass
concentrations. It
is also assumed that the instrument does not drift significantly over time and
that the uncertainty in the volume of air sampled  is assumed to be very small in comparison to the analytical step. This is usually a sound approximation,
especially for accurately mass flow controlled systems [9].
Moreover, it will be supposed that other systematic uncertainties do not vary with
sampling time or pollutant mass concentration. Two different scenarios will be
considered: when small variations in mass concentration are observed during the time period t, and when large variations in mass
concentration are observed during the time period t.


### 3.1. Scenarios Characterised by Small Variations in Mass Concentration during the Time Periodt

In this situation, it is assumed that the mass concentration of the pollutant being measured does
not show large variations over the sampling periodt.
Indeed,
for the monitoring of vapour-phase mercury in background locations, this is borne out by the results such
as in Figure 1, which shows the monthly average vapour-phase mercury mass
concentration at Eskdalemuir (a rural background monitoring site) from February
2005 to January 2006 (inclusive). As can
be seen, the measured values are all
consistent with the yearly average, within the uncertainty of the measurement,
over this period.Figure 1
The monthly average
vapour-phase mercury mass concentration measured at Eskdalemuir, UK,
from February 2005 to January 2006. The error bars represent the expanded measurement uncertainty at the 95% confidence interval. The dotted line represents the unweighted average over this period.The average mass concentration,γ¯,
measured over time period, t,
is given by(2)γ¯=∑i=1nγin,
where γi is the average mass concentration measured
over sampling time ti. The  squared standard combined uncertainty in γ¯, uc2(γ¯),
is given by(3)uc2(γ¯)=∑i=1n[ci⋅u(γi)]2,where u(γi) is the uncertainty in γi, and ci is the sensitivity coefficient of γi. Since(4)ci=δγ¯δγi=1n,it follows that
(5)uc2(γ¯)=∑i=1n[u(γi)n]2.If we assume that γi≈γi+1≈⋯γn−1≈γn, then it follows from the assumptions
above that u(γi)≈u(γi+1)≈⋯u(γn−1)≈u(γn) for a stable analytical instrument. Thus,
to a good approximation, (6)uc2(γ¯)≈n⋅[u(γi)n]2,so(7)uc(γ¯)≈u(γi)n,which is a known result for a series of measurements showing little
variation [8, 10]. Assuming that the instrument
repeatability is the dominant contribution to uncertainty at low mass concentrations such as those
found in ambient air to a first approximation, the uncertainty in γi is given by(8)u(γi)≈σi,where σi is the repeatability of the instrument for the
measurement of γi. Therefore, (9)uc(γ¯)≈σin. Therefore, the relative uncertainty in γ¯, uc,rel(γ¯),
is given by(10)uc,rel(γ¯)≈σi,reln,
where σi,rel is the relative repeatability of the
instrument for the measurement of γi. At first inspection, it may appear that the relative uncertainty in the mass concentration measured
over time period t decreases as ti decreases and, therefore, as n increases.
However, this is not necessarily the case, and to understand why, it is
necessary to examine the effect of the variation of sampling time on the measurement
procedure, and, in particular, how σi,rel varies with the amount, or mass, of pollutant
being measured.Measuring systems are either heteroscedastic, where the absolute
standard deviation of repeat measurements varies as some function of the
quantity of analyte being measured or, in the limiting case, homoscedastic, where the absolute
standard deviation of repeat measurements does not vary as the quantity of
analyte being measured changes. Although
most measuring systems are heteroscedastic, over a small range of analyte
quantity, they can usually be approximated to homoscedastic behaviour. However,
since the sampling times (and therefore the accumulated pollutant mass) under
consideration in this study can vary over several orders of magnitude, we must
consider that the measuring system will show some heteroscedasticity. Heteroscedastic behaviour can be empirically
modelled by a Horwitz-type function (often used to characterise interlaboratory reproducibility) [11, 12]
of the form(11)σi=a⋅mib.Thus, (12)σi,rel=a⋅mi(b−1),
where a and b are constants (a≥0, b≥0) characterising the individual measuring
system in question, and mi is the mass of pollutant (interchangeable for
amount, given knowledge of the relative molecular or atomic mass of the
pollutant) collected during each sampling period ti. If the initial condition of small variations
in mass concentration during the time period is met, then the mass of pollutant
collected during each sampling period will be approximately proportional to the
sampling time, and, therefore, inversely proportional to the number
of sampling periods, mi∝ti∝n−1,
such that(13)σi,rel=k⋅1n(b−1),
where k is a constant similarly characterising the
individual measuring system in question.
Substituting into (10) gives
(14)uc,rel(γ¯)≈k⋅1n(b−1)n.
The expected change in uc,rel(γ¯), Δuc,rel(γ¯),
as the number of sampling periods increases from 1 to n,
may be expressed as(15)Δuc,rel(γ¯)≈n−b⋅n.
This is simply the ratio of the value of uc,rel(γ¯) for n sampling periods to the value of uc,rel(γ¯) for 1 sampling period. Equation (15) is plotted for various b in Figure 2.Figure 2
The relationship
between the expected change in the relative standard uncertainty of the average
mass concentration and the number of sampling periods, for various values ofb between 0 (top line) and 1 (bottom line), in
increments of 0.1.Figure2 shows that for a measuring system that is purely
homoscedastic, the limiting case where b=0,
the relative uncertainty of the average mass concentration increases as the
number of sampling periods increases. In
reality, b>0 for almost all cases. As b increases, the
increase in the uncertainty of the average mass concentration upon increasing
the number of sampling periods becomes smaller.
When b=0.5, the
uncertainty of the average mass concentration is independent of the number of
sampling periods. For values of b>0.5, the
relative uncertainty of the average mass concentration begins to fall as the
number of sampling periods increases. The exponent b may also take values above 1, in which case
the trend of decreasing uncertainty for increasing numbers of sampling periods
would continue.This analysis has shown that in order to minimise the uncertainty of
the average measured mass concentration, it is vital to determine the exponentb for the measurement system in question. As an example, the system used to measure samples on the UK Heavy Metals Monitoring Network [13]—a PS Analytical Sir Galahad II instrument—has been examined to determine how its
repeatability characteristics vary with the mass of analyte introduced; this
data is presented in Figure 3.Figure 3
The relationship
between normalised relative repeatability and normalised analyte mass for the
PS Analytical Sir Galahad II instrument.As expected from (12), a plot between normalised
relative repeatability and normalised analyte mass on a logarithmic scale, as
shown in Figure 3, yields a reasonably linear relationship with a gradient of
approximately −0.5, which is equal to b−1,
yielding in this case b≈0.5. Hence, in the case of the Sir Galahad II
instrument the data suggests that little difference in the uncertainty of the
average mass concentration will be observed by altering the sampling period. Of course, before a decision is made on the
optimum number of sampling periods, the characteristics of each measuring device must be determined, as these will
vary between instruments and techniques [14].


### 3.2. Scenarios Characterised by Large Variations in Mass Concentration during the Time Periodt

For situations where the quantity being measured exhibits significant changes with time (e.g., at a roadside monitoring site),
the uncertainty of the average value of a series on consecutive measurements
is, by definition, the square root of the second moment about the mean [8, 10, 15],
which when expressed in a discrete way becomes(16)uc2(γ¯)=1n∑i=1n[u(γi)]2.In this way, the uncertainty in the average mass concentration value
is equal to the square root of the sum of the squared uncertainties associated
with the measurements over each sampling period. It is clear, therefore, that the relative
uncertainty in the average mass concentration is minimised by minimising the
uncertainty of the individual sampling periods. To simplify this situation, we may approximate (16) to(17)uc(γ¯)≈u¯(γi),
where u¯(γi) is the mean uncertainty over all individual
sampling periods. (When there is no variation in u(γi),
as in situations characterised by small variations in concentrations, this
approximation becomes an equality.) Unlike (7), there is no dependence on n at this stage.
Similarly to (10), (18)uc(γ¯)≈σ¯i,uc,rel(γ¯)≈σ¯i,rel, where σ¯i and σ¯i,rel are the average repeatability, and average
relative repeatability, respectively, of the instrument over all sampling
periods. Thus, similarly to (10) to (15), it follows that(19)Δuc,rel(γ¯)≈n1−b. Equation (19) is plotted in Figure 4.Figure 4
The relationship
between the change in the relative standard uncertainty of the average mass
concentration and the number of sampling periods, for various values ofb between 0 (top line) and 1 (bottom line, coincident with the abscissa), in increments of 0.1.For the situation indicated in Figure4, for b<1, the relative uncertainty of the
average mass concentration increases as the number of sampling periods within
the time period increases. Therefore, the uncertainty in this situation would be minimised by using as few sampling
periods as possible. When b=1, the relative uncertainty of the
average mass concentration is independent of the number of sampling periods. Only for situations when b>1   is the
uncertainty   minimised by increasing the number of sampling periods.


### 3.3. The Effect of Sampling Time on the Information Contained within the Time Series

In the circumstances described above, decreasing the number of sampling periods can, under some circumstances, decrease the uncertainty of the average measured concentration. However, this is at the expense
of the information contained within the time series data. Clearly, the number of data points obtained during the  time period,t,
is directly proportional to the number of sampling periods, n.
Whilst
the average of the data over the time period, t,
will remain unchanged, the detail of the concentration fluctuations will be
lost, especially short-term, high-concentration
peaks. This effect is displayed in Figure 5 for PM10 mass
concentration at Marylebone Road
in London
during 2006 [16],
which shows how the detail in the data is lost as the number of sampling
periods decreases. This is further evidenced by the decrease in
the relative standard deviation of the data, and the peak mass concentration,
as the sampling period increases, shown in Figure 6.Figure 5
The measured PM10 mass concentration at Marylebone Road in London during 2006 displayed, from top
to bottom: hourly (offset by +400 μg ⋅ m−3), six-hourly (offset by +300 μg ⋅ m−3), daily (offset by +200 μg ⋅ m−3), weekly (offset by +100 μg ⋅ m−3), and monthly no soffset.Figure 6
The variation in relative standard deviation (red bullets) and peak
concentration (blue bullets) of measured PM10 mass concentrations at Marylebone Road, London, 2006, with changing sampling periods.Obviously for some applications such as odour monitoring [17], determination of short-term peak
concentrations is important. The same is true for PM10 measurements, where limits are set not only for annual average concentrations,
but also for the number of 24-hour averages above a threshold level [18]. Knowledge of short-term peak concentrations
can also be of great value in air pollution modelling [19]. However, the case is different for those
compounds for which average exposure over long periods of time, rather than
acute exposure over very short periods, is thought to be most important, or
for those compounds whose effect is cumulative such as mercury. In these situations, it is better to have data with the lowest uncertainty so that health studies,
abatement strategies, and air quality policy can be best assessed and
informed.


## 3.1. Scenarios Characterised by Small Variations in Mass Concentration during the Time Periodt

In this situation, it is assumed that the mass concentration of the pollutant being measured does
not show large variations over the sampling periodt.
Indeed,
for the monitoring of vapour-phase mercury in background locations, this is borne out by the results such
as in Figure 1, which shows the monthly average vapour-phase mercury mass
concentration at Eskdalemuir (a rural background monitoring site) from February
2005 to January 2006 (inclusive). As can
be seen, the measured values are all
consistent with the yearly average, within the uncertainty of the measurement,
over this period.Figure 1
The monthly average
vapour-phase mercury mass concentration measured at Eskdalemuir, UK,
from February 2005 to January 2006. The error bars represent the expanded measurement uncertainty at the 95% confidence interval. The dotted line represents the unweighted average over this period.The average mass concentration,γ¯,
measured over time period, t,
is given by(2)γ¯=∑i=1nγin,
where γi is the average mass concentration measured
over sampling time ti. The  squared standard combined uncertainty in γ¯, uc2(γ¯),
is given by(3)uc2(γ¯)=∑i=1n[ci⋅u(γi)]2,where u(γi) is the uncertainty in γi, and ci is the sensitivity coefficient of γi. Since(4)ci=δγ¯δγi=1n,it follows that
(5)uc2(γ¯)=∑i=1n[u(γi)n]2.If we assume that γi≈γi+1≈⋯γn−1≈γn, then it follows from the assumptions
above that u(γi)≈u(γi+1)≈⋯u(γn−1)≈u(γn) for a stable analytical instrument. Thus,
to a good approximation, (6)uc2(γ¯)≈n⋅[u(γi)n]2,so(7)uc(γ¯)≈u(γi)n,which is a known result for a series of measurements showing little
variation [8, 10]. Assuming that the instrument
repeatability is the dominant contribution to uncertainty at low mass concentrations such as those
found in ambient air to a first approximation, the uncertainty in γi is given by(8)u(γi)≈σi,where σi is the repeatability of the instrument for the
measurement of γi. Therefore, (9)uc(γ¯)≈σin. Therefore, the relative uncertainty in γ¯, uc,rel(γ¯),
is given by(10)uc,rel(γ¯)≈σi,reln,
where σi,rel is the relative repeatability of the
instrument for the measurement of γi. At first inspection, it may appear that the relative uncertainty in the mass concentration measured
over time period t decreases as ti decreases and, therefore, as n increases.
However, this is not necessarily the case, and to understand why, it is
necessary to examine the effect of the variation of sampling time on the measurement
procedure, and, in particular, how σi,rel varies with the amount, or mass, of pollutant
being measured.Measuring systems are either heteroscedastic, where the absolute
standard deviation of repeat measurements varies as some function of the
quantity of analyte being measured or, in the limiting case, homoscedastic, where the absolute
standard deviation of repeat measurements does not vary as the quantity of
analyte being measured changes. Although
most measuring systems are heteroscedastic, over a small range of analyte
quantity, they can usually be approximated to homoscedastic behaviour. However,
since the sampling times (and therefore the accumulated pollutant mass) under
consideration in this study can vary over several orders of magnitude, we must
consider that the measuring system will show some heteroscedasticity. Heteroscedastic behaviour can be empirically
modelled by a Horwitz-type function (often used to characterise interlaboratory reproducibility) [11, 12]
of the form(11)σi=a⋅mib.Thus, (12)σi,rel=a⋅mi(b−1),
where a and b are constants (a≥0, b≥0) characterising the individual measuring
system in question, and mi is the mass of pollutant (interchangeable for
amount, given knowledge of the relative molecular or atomic mass of the
pollutant) collected during each sampling period ti. If the initial condition of small variations
in mass concentration during the time period is met, then the mass of pollutant
collected during each sampling period will be approximately proportional to the
sampling time, and, therefore, inversely proportional to the number
of sampling periods, mi∝ti∝n−1,
such that(13)σi,rel=k⋅1n(b−1),
where k is a constant similarly characterising the
individual measuring system in question.
Substituting into (10) gives
(14)uc,rel(γ¯)≈k⋅1n(b−1)n.
The expected change in uc,rel(γ¯), Δuc,rel(γ¯),
as the number of sampling periods increases from 1 to n,
may be expressed as(15)Δuc,rel(γ¯)≈n−b⋅n.
This is simply the ratio of the value of uc,rel(γ¯) for n sampling periods to the value of uc,rel(γ¯) for 1 sampling period. Equation (15) is plotted for various b in Figure 2.Figure 2
The relationship
between the expected change in the relative standard uncertainty of the average
mass concentration and the number of sampling periods, for various values ofb between 0 (top line) and 1 (bottom line), in
increments of 0.1.Figure2 shows that for a measuring system that is purely
homoscedastic, the limiting case where b=0,
the relative uncertainty of the average mass concentration increases as the
number of sampling periods increases. In
reality, b>0 for almost all cases. As b increases, the
increase in the uncertainty of the average mass concentration upon increasing
the number of sampling periods becomes smaller.
When b=0.5, the
uncertainty of the average mass concentration is independent of the number of
sampling periods. For values of b>0.5, the
relative uncertainty of the average mass concentration begins to fall as the
number of sampling periods increases. The exponent b may also take values above 1, in which case
the trend of decreasing uncertainty for increasing numbers of sampling periods
would continue.This analysis has shown that in order to minimise the uncertainty of
the average measured mass concentration, it is vital to determine the exponentb for the measurement system in question. As an example, the system used to measure samples on the UK Heavy Metals Monitoring Network [13]—a PS Analytical Sir Galahad II instrument—has been examined to determine how its
repeatability characteristics vary with the mass of analyte introduced; this
data is presented in Figure 3.Figure 3
The relationship
between normalised relative repeatability and normalised analyte mass for the
PS Analytical Sir Galahad II instrument.As expected from (12), a plot between normalised
relative repeatability and normalised analyte mass on a logarithmic scale, as
shown in Figure 3, yields a reasonably linear relationship with a gradient of
approximately −0.5, which is equal to b−1,
yielding in this case b≈0.5. Hence, in the case of the Sir Galahad II
instrument the data suggests that little difference in the uncertainty of the
average mass concentration will be observed by altering the sampling period. Of course, before a decision is made on the
optimum number of sampling periods, the characteristics of each measuring device must be determined, as these will
vary between instruments and techniques [14].


## 3.2. Scenarios Characterised by Large Variations in Mass Concentration during the Time Periodt

For situations where the quantity being measured exhibits significant changes with time (e.g., at a roadside monitoring site),
the uncertainty of the average value of a series on consecutive measurements
is, by definition, the square root of the second moment about the mean [8, 10, 15],
which when expressed in a discrete way becomes(16)uc2(γ¯)=1n∑i=1n[u(γi)]2.In this way, the uncertainty in the average mass concentration value
is equal to the square root of the sum of the squared uncertainties associated
with the measurements over each sampling period. It is clear, therefore, that the relative
uncertainty in the average mass concentration is minimised by minimising the
uncertainty of the individual sampling periods. To simplify this situation, we may approximate (16) to(17)uc(γ¯)≈u¯(γi),
where u¯(γi) is the mean uncertainty over all individual
sampling periods. (When there is no variation in u(γi),
as in situations characterised by small variations in concentrations, this
approximation becomes an equality.) Unlike (7), there is no dependence on n at this stage.
Similarly to (10), (18)uc(γ¯)≈σ¯i,uc,rel(γ¯)≈σ¯i,rel, where σ¯i and σ¯i,rel are the average repeatability, and average
relative repeatability, respectively, of the instrument over all sampling
periods. Thus, similarly to (10) to (15), it follows that(19)Δuc,rel(γ¯)≈n1−b. Equation (19) is plotted in Figure 4.Figure 4
The relationship
between the change in the relative standard uncertainty of the average mass
concentration and the number of sampling periods, for various values ofb between 0 (top line) and 1 (bottom line, coincident with the abscissa), in increments of 0.1.For the situation indicated in Figure4, for b<1, the relative uncertainty of the
average mass concentration increases as the number of sampling periods within
the time period increases. Therefore, the uncertainty in this situation would be minimised by using as few sampling
periods as possible. When b=1, the relative uncertainty of the
average mass concentration is independent of the number of sampling periods. Only for situations when b>1   is the
uncertainty   minimised by increasing the number of sampling periods.


## 3.3. The Effect of Sampling Time on the Information Contained within the Time Series

In the circumstances described above, decreasing the number of sampling periods can, under some circumstances, decrease the uncertainty of the average measured concentration. However, this is at the expense
of the information contained within the time series data. Clearly, the number of data points obtained during the  time period,t,
is directly proportional to the number of sampling periods, n.
Whilst
the average of the data over the time period, t,
will remain unchanged, the detail of the concentration fluctuations will be
lost, especially short-term, high-concentration
peaks. This effect is displayed in Figure 5 for PM10 mass
concentration at Marylebone Road
in London
during 2006 [16],
which shows how the detail in the data is lost as the number of sampling
periods decreases. This is further evidenced by the decrease in
the relative standard deviation of the data, and the peak mass concentration,
as the sampling period increases, shown in Figure 6.Figure 5
The measured PM10 mass concentration at Marylebone Road in London during 2006 displayed, from top
to bottom: hourly (offset by +400 μg ⋅ m−3), six-hourly (offset by +300 μg ⋅ m−3), daily (offset by +200 μg ⋅ m−3), weekly (offset by +100 μg ⋅ m−3), and monthly no soffset.Figure 6
The variation in relative standard deviation (red bullets) and peak
concentration (blue bullets) of measured PM10 mass concentrations at Marylebone Road, London, 2006, with changing sampling periods.Obviously for some applications such as odour monitoring [17], determination of short-term peak
concentrations is important. The same is true for PM10 measurements, where limits are set not only for annual average concentrations,
but also for the number of 24-hour averages above a threshold level [18]. Knowledge of short-term peak concentrations
can also be of great value in air pollution modelling [19]. However, the case is different for those
compounds for which average exposure over long periods of time, rather than
acute exposure over very short periods, is thought to be most important, or
for those compounds whose effect is cumulative such as mercury. In these situations, it is better to have data with the lowest uncertainty so that health studies,
abatement strategies, and air quality policy can be best assessed and
informed.


## 4. Conclusions

This paper has described how the uncertainty of a time series
average can vary as a function of the length of the individual sampling
periods. The average of the time series will remain
constant regardless of sampling periods. The uncertainty of the average has been
investigated for scenarios, where the pollutant concentration is not expected to vary significantly, and
for scenarios    where a larger variation in
concentration is expected. It has been shown that the
outcome is crucially dependent on how the precision characteristics of the
measuring instrumentation vary with analyte abundance. For the example of vapour-phase mercury
measurement on the UK Heavy Metals Monitoring Network, it has been shown that the uncertainty of the average is not strongly affected
by sampling time.The choice of sampling period is a balance between achieving
fit-for-purpose data with an acceptable uncertainty, and the requirements for
the use of the measurement data. Generally, one should act so as to minimise the
uncertainty of the measurement data; and in some cases, this will require longer sampling periods to be used. This is acceptable for the
measurement of pollutants, which cause adverse health effects
by cumulative or long-term, low-level
exposure. Many
such pollutants are only required by legislation to be reported on an annual
average basis, and thus long sampling periods can
be easily accommodated. However,
as has been observed, the use of long sampling periods has a detrimental effect
on the information richness of the measurement data. For pollutants where adverse health effectsare caused by the peak concentration
over short periods, or where legislation demands short-term average data to be
reported, or sampling campaigns designed to measure short-term peak
concentrations of any pollutant, long sampling periods are not acceptable. In
these cases, the choice of sampling periods must
be balanced between meeting these demands and delivering fit-for-purpose
uncertainties.



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*Source: 814715-2008-03-13.xml*