# Module 4: Introduction to Aerosols

Hello, and welcome to the module Introduction

to Aerosols. My name is Gurumurthy Ramachandran, and I’m

a faculty member at the University of Minnesota School of Public Health in the Division of

Environmental Health Sciences. By the end of this module, learners should be

able to do the following: Define the term aerosol and other related terms,

interpret summary properties of particle

distributions, predict the relationship between aerosol sources and the resulting particle sizes,

and finally, describe various approaches to

measuring airborne particles. In everyday usage, the term aerosol refers to a

product such as a perfume, paint, or a

pharmaceutical delivered in the form of spray or mist from a container with a liquid and

a propellant inside it, as shown in this diagram. However, the word aerosol has a much broader

definition. We can define an aerosol as a suspension of

liquid or solid particles in a gas. The sizes of

aerosol particles span a wide range. They can be as small as one-thousandth of a

micrometer or 1 nm. To put this in perspective, 1 nanometer is

roughly two to five times larger than the size of

small gas molecules. At the upper end, particles can be as large as

one hundred to two hundred micrometers. There are a number of sources that generate

aerosol particles, both natural and manmade. This picture shows some of the sources of

naturally occurring aerosols, including volcanic

emissions on the right hand side, smoke from forest fires, volatile organic

chemical emissions from plants, and these

emissions then undergo chemical reactions smoke from forest fires, volatile organic

chemical emissions from plants, and these

emissions then undergo chemical reactions to form particles, dust from desert sand storms,

and emissions of dimethyl sulfide from the

plankton in oceans that then undergo further reactions to form sulfate particles or

sulfate aerosol. Aerosols are responsible for the beautiful red

sunsets that we have all seen, the annual forest

fires in the Western United States. They cause farther away hills to appear lighter

than nearer hills due to the scattering of light by

aerosol particles. Rainbows are observed due to the interaction of

sunlight with water droplets suspended in air, and of course, the smoke and ash from volcanic

eruptions as we have mentioned before. Aerosols are also created by human activities. For example, the very fine particles in fumes

that are generated during welding at high

temperatures, the much larger dust particles that are generated when we cut a piece of

concrete using a mechanical saw, the smoke

from industrial smokestacks, the fine mist that is generated when we spray a

field with pesticides from an airplane, the smoke

when we grill a piece of meat, and, of course, cigarette smoke. It is useful to get a sense of the range of particle

sizes of aerosols from different sources. As we discussed before, aerosol sizes can

range from one-thousandth of a micrometer or 1

nm to roughly 100 to 200 µm. So, this covers around five orders of magnitude To put this in perspective, we can relate this

order of magnitude range to the range of sizes

from a pea to a very large building. Windblown dusts are created by mechanical

processes such as erosion of rocks over

geological time scales, and these have particles in the range of roughly

1 µm to several hundred micrometers. Mining dusts are also created by mechanical

processes such as the crushing of ore bearing rocks, and these have roughly the

same size range: slightly less than 1 µm to

around 100 µm. The lengths of asbestos fibers are also between

1 µm to around 100 µm. Biological spores are also roughly in this size

range. The sizes of airborne bacteria cover a smaller

range from around half a micrometer to around

10 µm. What is commonly referred to as the urban

aerosol is made of emissions of particles from

vehicles of various kinds, emissions from industrial sources such as

power plants, and particles created by chemical reactions between gases such as

ozone and volatile organic chemicals. The range of these particles is from around 50

nm, or 0.05 µm, to about 5 µm. Metal smelting fumes range from around 10 nm

to around 500 nm. Vehicular exhaust, also a result of combustion

reaction, is roughly in the same size range. Viruses range from around 10 nm to 100 nm in

size. We commonly refer to particles less than 1 µm

or 1000 nm as fine particles. Particles less than

100 nm are called ultrafine. This is also the range of what we call

nanoparticles, although this term typically refers only to particles that have been intentionally

created in an engineering process. To get a perspective of these small sizes, it is

useful to remember that the width of the human

hair is about 70 to 75 micrometers. The wavelengths of colors in the visible

spectrum of light range from around 380 to 780

nm. In addition to size, particles come in a wide

variety of shapes, ranging from fibers where we

have the length of the particles much greater than the width of the fibers, to chain

agglomerates that are made of chains of small

particles. Particles can be spherical, in various crystalline

shapes. Pollen come in a variety of shapes with

interesting surface features. Water droplets are roughly spherical And all these shapes where the length and width

and height of the particles are roughly the same, although not exactly the same, are called

isometric, unlike fibers that have one dimension such as

the length much larger

than another such as the height or width. This slide shows the array of shapes associated

with nanoparticles with the same composition. All these particles, even though they have very

different shapes, have the same composition

chemically. They are all made of zinc oxide, and this

illustrates the range of shapes that

nanoparticles can take. Another important property of particles is their

density. Depending on their composition, particle density

can vary widely. Water droplets have a density of 1000 kg per

cubic meter. The metal lead has a density of 11,300 kg per

cubic meter. It is useful to compare these with the density of

air, which is roughly 1 kg per cubic meter. So water has a density that is almost 1000

times greater than that of air. The physical and chemical processes that

generate aerosol particles determine the range

of particle sizes. In mechanical processes, energy is supplied to

a bulk material to break it into smaller particles. For example, during cutting, blasting, or

crushing for solid particles, or spraying in the

case of liquid particles, the sizes of the particles depend on the ratio of

the amount of energy supplied and the energy of the adhesive bonds holding

the parent material together. Now we will watch three videos that show

aerosol generation from various mechanical

processes. The three processes that we will see now are

cutting off the concrete block, a coal mining

operation, and finally spray painting. In this video, the worker is using a saw to cut

through a piece of concrete and generating dust

in the process. Here, we see a diesel operated vehicle dumping

crushed coal onto a pile, so some of the

particles are mechanically generated coal dust, but we can also see the black diesel

combustion smoke being emitted by the vehicle. This smoke is made of much smaller particles

than the coal dust, and this is an example of a

mixed aerosol. In this video, we see a car being sprayed with a

liquid paint. The hose supplies high pressure air to the liquid

in the container and breaks it up into very small

spray droplets. Particles can also be created by molecular

processes such as those shown in this slide. Molecules of a vapor or gas can come together

by themselves to form small clusters in a

process called nucleation. This can happen when a gas that is saturated

with a vapor undergoes rapid cooling and, thus,

creates conditions for these nuclei to form. These nuclei are roughly the size of a

nanometer. An example is the formation of primary soot

particles from the tailpipe emissions of vehicles. Once these nuclei are formed, they can

continue to increase in size by colliding and

sticking to each other, a process called coagulation. They may

increase in size by condensation – that is

molecules of the vapor condense onto the surface of particles, or

decrease in size by evaporation, that is

molecules leave the particle surface. Thus once an aerosol is created, either

mechanically or by nucleation, its particles can

change in size by these mechanisms. These phenomena play an important role in the

exposure to aerosols that human beings face. Depending on where people are located with

respect to these phenomena, they would be exposed to different aerosols sizes and

concentrations. To summarize what we have learned so far,

chemical processes such as combustion, welding and those in nanoparticle reactors lead

to particle sizes less than 1 µm in size and frequently less than one-tenth of a

micrometer, or 100 nm, in size. These are

referred to as ultrafine or nanoparticles. and frequently less than one-tenth of a

micrometer, or 100 nm, in size. These are

referred to as ultrafine or nanoparticles. Mechanical processes on the other hand, such

as grinding or sanding, lead to much larger

sizes, ranging from 1 µm to several 100 µm. The concept of an equivalent diameter is very

important in describing particle sizes. It is easy to see that two spherical particles with

the same diameter, for example an air bubble and a lead particle, will behave very differently in

air. Likewise, particles with different shapes could

behave similarly under some conditions. How can we describe the sizes of particles

when they come in different shapes, and

chemical and physical properties? How can we describe the sizes of particles

when they come in different shapes, and

chemical and physical properties? In defining an equivalent diameter, we first

imagine a spherical particle and then we keep

some property unchanged, that is invariant between the real particle and the imaginary

sphere, and then we describe the real particle in

terms of the diameter of this imaginary sphere. In other words, the equivalent diameter is the

diameter of the sphere that has the same value of a physical property as that of the particle in

question. In this slide, we have an actual particle that is

irregularly shaped and is made of a material with

a density rho sub P. How can we describe the size of this irregularly

shaped particle? We can imagine several different physical

properties that we can use as the basis for

defining an equivalent diameter. For example, we can imagine a sphere with the

same projected surface area as our actual

particle. We think of the projected surface area of a

particle as the area of the shadow cast by the

particle on a projection screen, and then the diameter of this sphere is the

projected surface area equivalent diameter. Alternatively you can imagine a sphere with the

same total surface area as the surface area of

the actual particle The diameter of this sphere is the surface area

equivalent diameter. We can also imagine a sphere with the same

volume as our actual particle The diameter of this sphere is the volume

equivalent diameter. As a final example, we can imagine a spherical

water droplet with the same speed of falling through air, also known as its terminal settling

velocity, as our actual particle. The diameter of this water droplet is called the

aerodynamic equivalent diameter, or simply the

aerodynamic diameter. We will learn about the aerodynamic diameter in

greater detail in a later module. Next we discuss the topic of particle size

distributions. A monodisperse aerosol is one in which all the

particles are of the same size. It is almost never found in nature, and it is

artificially constructed or engineered in very highly controlled circumstances, and it’s very

difficult to make monodisperse aerosols. highly controlled circumstances, and it’s very

difficult to make monodisperse aerosols. A polydisperse aerosol, on the other hand, is

one in which there is a range of particle sizes Almost all natural and workplace aerosols are

polydisperse. Now, we will discuss the statistics of particle

size distributions. At a very basic level, if we have a collection of a

number of particles with different diameters, then we can calculate the mean and standard

deviation of the particle sizes. The arithmetic mean diameter is a measure of

the central tendency of the distribution of

particle sizes. If N is the total number of particles in a sample,

or collection of particles, and n sub i is the

number of particles with size d sub i, then the arithmetic mean is calculated using

this formula, where we multiply each size d sub

i by the number of particles with that size n sub i, and then summing this over all particle

sizes. This sum of the products is then divided by the

total number of particles, N, or summation of n sub i, to yield the average diameter, or the

arithmetic mean diameter, denoted by d hat. The median diameter is the value below which

50% of the particle diameters are, and above

which 50% of the particles lie. It is, thus, the 50th percentile of particle

diameters. The standard deviation of particle sizes is

calculated using this formula. We find the difference between each particle

size from the arithmetic mean diameter, square

the difference, multiply this by the number of particles of that size, n sub i, and

then sum this across all the size ranges, and take the sum and divide this by the total

number of particles minus one. After that, we take the square root of this

quantity. The standard deviation is a measure of the

variability in particle size. Let us consider an aerosol where we have been

able to determine some equivalent diameter for a large number of particles using some

aerosol measurement instrument. The instrument in this example counts and

classifies the particles into seven size ranges or

size bins, as shown here: 10 to 50 nm, 50 to 80 nm, and so on all the way

up to 890 to 1260 nanometers. 10 to 50 nm, 50 to 80 nm, and so on all the way

up to 890 to 1260 nanometers. The total number of particles counted is 832. We want to calculate the arithmetic mean,

median, and standard deviation of this collection

of particles, but from this table, it looks like we only know

the ranges within which the particles sizes lie,

and not each individual particle’s size. Therefore, we will make a simplifying

assumption. We will assume that within each

size bin, all the particles are of the same size. Therefore, we will make a simplifying

assumption. We will assume that within each

size bin, all the particles are of the same size. We will further assume that this is the midpoint

of the size bin. Although this is an assumption, it is not too far

from the truth, and so it is a justifiable

assumption. We can now add a third column to our table that

lists the midpoint diameter for each size range. This allows us to calculate the various terms

needed to calculate the arithmetic average. Now, the fourth column is obtained by

multiplying the second column n sub i, or

particle count, with the third column d sub i, or the midpoint of the size interval to obtain the

product n sub i times d sub i, and we do this for

each size range. At the bottom of this column, we can add up all

the n sub i times d sub i terms. We can now go to the next slide, where we use

this information to calculate the arithmetic mean

d hat. Using formula that I described earlier, the

arithmetic mean can be calculated as 142 nm. The median diameter can also be calculated

very simply. As we see, there are 832 particles in all, and the

median diameter is the middle particle. If we can line up these 832 particles in

ascending order, then in fact there are two

middle particles, the 416th and 417th particle. So we should take the average of these two

middle particles, however, since we don’t know the sizes of the

individual particles, we can at best say that the

median is somewhere between 50 and 80 nm. Next, we have to calculate the standard

deviation. We can now look at the fifth column. This

column shows the product of n sub i and (d sub

i-d hat) squared. At the bottom of this column, we can add up all

these product terms. Now we go to the next slide again and look

where we use this summation in the formula for

the standard deviation. We take the summation, divide it by N-1 or

832-1, and then take the square root of this

whole quantity. This gives us a standard deviation of 186 nm. This gives us a standard deviation of 186 nm. While the calculations we did for the mean,

median, and standard deviation are useful to some extent, they do not give us a feel for the

shape of the distribution. We can construct a histogram of these data as

shown in the figure. Here, the particle diameter is on the horizontal x

axis, and the particle count is on the vertical y

axis. This histogram gives us a better sense of how

the particles are distributed among the various size ranges, that is, the shape of the

distribution. However, this type of a plot has an inherent

limitation in that the shape of the histogram depends very much on the size

ranges that we choose. For example, we could combine the ranges 140

– 270 nm and 270 – 560 nm to form a new range

140 – 560 nm with 96 + 53, or 149 particles. For example, we could combine the ranges 140

– 270 nm and 270 – 560 nm to form a new range

140 – 560 nm with 96 + 53, or 149 particles. I’m showing the original and the modified

histogram here, and we see the height of this

new size interval that we have created is I’m showing the original and the modified

histogram here, and we see the height of this

new size interval that we have created is much greater than either of the original two size

intervals. Our histogram’s shape now looks different. The shape of our distribution should not depend

on the size intervals in our instrument; it should

not depend on the instrument design. This is not acceptable. To avoid this kind of a problem, we can calculate

the particle count per nanometer as shown in

the fourth column. We are dividing the count in each size range by

the width of that size range, so, we end up with

a count per micrometer column. So, this is our second try at drawing a useful

histogram. The advantage of this type of histogram is that

the interval heights are independent of size

interval. Additionally, since the height of each block or

rectangle is n sub i divided by delta d, which is

the interval size, and the width is the interval size delta d, the area of each block of the

histogram is equal to the number of particles, or

n sub i, in that size range. Therefore, the total area of all the blocks of the

histogram is equal to the total number of

particles, that is, the sum of n sub i, or N. . Well, I am still not satisfied. One drawback to

this type of a histogram is that the heights of the intervals are still dependent on the number of particles collected in that

particular sample. So, if we had collected twice the number of

particles, but with the same distribution in the

various bins, as shown in the histogram on the bottom, the

heights of our intervals would have been

doubled, leaving the shape unchanged. So, let us make one more adjustment. We can calculate the fraction of particles per

nanometer in each size interval instead of the

absolute number of particles per nanometer. The fifth column in this table shows the fraction

of particles per nanometer in each size interval. We obtain this by dividing the fourth column,

which shows the count per nanometer, by the

total number of particles, that is, 832. So, now let us try the histogram one more time. In this histogram, the area of each rectangular

block is equal to the fraction of particles in that

size interval. Therefore, the total area of all the blocks

combined is equal to 1.0. This histogram can be said to be independent of

the type of sampling instrument we use, that defines the size of our bins, and also the

number of particles we happened to collect. It is more representative of the distribution of

particles in the environment, independent of our

instrument and sampling conditions. If we want, we can do one more thing. We can draw a smooth curve through the

midpoints of the tops of the rectangular blocks

to obtain a particle size distribution curve. This is an approximation to what is called the

probability density function. Just like the sums of the areas of all the

rectangles in the histogram is 1.0, Just like the sums of the areas of all the

rectangles in the histogram is 1.0, the area under the curve of the probability

density function is also 1.0. We can now take a closer look at this

histogram. We see that the histogram has a

skewed shape. Particles appeared to be distributed in a skewed

manner, and there are more particles in the

smaller size ranges than in the larger size ranges, and it is important to note that the x

axis, the horizontal axis, is a linear scale. However, if we change the horizontal axis from a

linear to a logarithmic scale, the particle

distribution appears to be more symmetric and we can fit a symmetrical looking curve to

the tops of the histogram. This brings us to what we call lognormal

distributions. Aerosol size distributions are seldom

symmetrical. They are typically positively

skewed with a long tail to the right. While the frequency distributions of particle

sizes have a skewed shape, the log of the particle sizes often have a

symmetrical, or Gaussian, or normal

distribution. Thus, the particle sizes are said to be log-

normally distributed. Thus, the particle sizes are said to be log-

normally distributed. The lognormal distribution is good way to

describe particle size distributions in workplaces

as well as in ambient environments. The statistics of lognormal distributions are very

similar to what we have done before. The lognormal distribution is described by the

geometric mean and geometric standard

deviation. This is analogous to the arithmetic mean and

standard deviation that we have learned to

calculate earlier. The geometric mean diameter, d sub g, can be

calculated as shown in this formula. You can see that this is similar to the formula for

the arithmetic mean except that the diameter is

replaced with the log of diameter. The geometric standard deviation, sigma sub g,

is calculated as shown in this formula. You can see that this is similar to the formula for

the standard deviation, except that the diameter

is replaced with the log of diameter. We can further modify this equation using one of

the rules of logarithms, that is, that the difference between the logarithms of two

quantities is the logarithm of the ratio of those

two quantities.

0:30:20.066,0:30:20.000

Log di minus log dg is equal to the log of di

divided by dg. Log of di minus log of dg is equal to the log of di

divided by dg. Thus, both of these expressions are similar to

that for the arithmetic mean and standard deviation, except that the diameter is

replaced with the log of diameter. The geometric mean and geometric standard

deviation completely describe a lognormal

distribution. The geometric mean is a central measure of the

size of the aerosol and the geometric standard deviation is a measure of the variability

of particle sizes in the aerosol. While the geometric mean has units of diameter,

the geometric standard deviation is a ratio of two

diameters, and, therefore, it is dimensionless. It cannot take a value of 0 since particle size

cannot be zero. It cannot take a value of 0 since the particle size

cannot be zero. We can use what we just learned to calculate

the geometric mean and geometric standard

deviation for our data set. The first three columns are the same as before.

The third column shows the midpoints of the

size intervals, but since for lognormal calculations we need to

use the log of diameter, the fourth column is the

logarithm of the midpoint diameters, or log (di). The fifth column is obtained by multiplying the

second column n sub i with the fourth column log (di) to obtain the product n sub i times log

(di). At the bottom of this column, we can add up all

the n sub i times log (di) across all the size

bins. We can now go to the next slide where we use

this information to calculate log of (d sub g), and for our data set, we calculate log of (d sub

g) to be equal to 1.96. Then, we can exponentiate it, or, in other words,

calculate ten raised to the power of 1.96, and

this comes out to be 92. So our geometric mean diameter in 92 nm.

We now go back to the previous slide and look

at the sixth column. This column shows the product of ni and (log di-

log dg) squared, and we calculate this for each

size interval. At the bottom of this column, we obtain the sum

of all these terms across each size interval. We now go back to the next slide where we use

this summation in the formula for the standard

deviation as shown below. We take the summation, divide it by N-1, or

832-1, and then take the square root of this

quantity. This gives us a standard deviation of 2.29. This gives us a standard deviation of 2.29. At this point, we should remember that for this

very data set, we had calculated an arithmetic

mean of 142 nm, and now we see that the geometric mean is 92

nm. This is a defining characteristic of lognormal

distributions that are skewed, in that their arithmetic means are always greater

than their geometric means We can also a plot the cumulative distribution of

the particles Here, the first two columns in the table are the

same as in previous slides. and the third column shows the cumulative

count up to a given particle size. So, we can

read this column in the following way: there are 120 particles less than 50 nm; there

are 120 plus 380 or 500 particles less than 80 nm; there are 120 plus 380 plus 146 or 646

particles less than 140 nm and so on. Finally there are 832 particles, that is, all the

particles, are less than 1260 nm. We can now construct a fourth column which

shows the cumulative percentage. Here, 14% of the particles, that is 120 out of the

832 particles, are less than 50 nm, 60% of the particles are less than 80 nm and so

this corresponds to the 500 out of the 832

particles, and finally 100% of the particles, all 832

particles, are less than 1260 nm. These data can be plotted as shown in the figure

on this slide. On the horizontal axis, we again have particle

diameter on a linear scale, and the vertical axis contains the cumulative percentage, again on a

linear scale. contains the cumulative percentage, again on a

linear scale. When the x axis is a linear scale, the

cumulative distribution rises and then levels off

at 100%. Nothing remarkable. When the x axis is a linear scale, the

cumulative distribution rises and then levels off

at 100%. Nothing remarkable. When the x axis is a linear scale, the

cumulative distribution rises and then levels off

at 100%. Nothing remarkable. This is to be expected. However, there is a

different way of plotting the same data. We can plot the diameter on a logarithmic scale

and the cumulative fraction on a probability

scale. This is called a log probability plot. The figure on this slide is an example of the log

probability plot. The particle size on the vertical

axis is on a logarithmic scale. The particle sizes go from 10 to 100 to 1000 to

10000 nm in equal intervals. The horizontal axis

is on a probability scale, and the intervals on this scale are wider at either

end and more compressed in the middle of the

range. The lower end does not go to zero but

approaches zero, and the upper end does not

reach 100% but approaches 100%. If we plot the cumulative fraction data on such a

graph, the data approximately fall on a straight

line. We can draw a best fit line through the data

points. The fact that all the data fall on a straight line

indicates that the data come from a log normal

distribution. That is the characteristic of the log probability

plot, that if we have a log normal distribution, then the cumulative percentage plot will fall on a

straight line. From this plot, we can read off important

statistical parameters of the log normal

distribution. The median diameter is at the 50th percentile,

that is 50% of the particles are less than this

diameter, and this corresponds to the 50th percentage point on the cumulative

percentage axis.

0:38:21.066,0:38:21.000

This corresponds to the geometric mean

diameter. This corresponds to the geometric mean

diameter. The geometric standard deviation, which is a

measure of the variability or the spread of the distribution, is defined as the ratio of the 84th

percentile to the 50th percentile. For this particular data set, the geometric mean

diameter, which corresponds to the 50th

cumulative percentage point, is 95 nm, as shown here, and the geometric standard

deviation is the ratio of the 84th percentile

diameter to the 50th percentile diameter. This is equal to 240 nm divided by 95 nm, and

this is equal to 2.5. So, the geometric standard

deviation is 2.5 in this case. This is equal to 240 nm divided by 95 nm, and

this is equal to 2.5. So, the geometric standard

deviation is 2.5 in this case. If you remember, we had also calculated the

geometric mean and geometric standard

deviation using a table of calulations. We obtained a value of 92 nm for the geometric

mean and 2.29 for the geometric standard

deviation. As we see, the two sets of values, one obtained

through tabular calculations and the other one obtained graphically, are very similar

to each other, as we should expect. But the graphical method is far simpler,

provided, of course, that you have a log

probability graph on a spreadsheet. We had discussed earlier that the geometric

standard deviation is a dimensionless number

as it is the ratio of two diameters. In the graphical method, we determine it by the

ratio of the 84th to the 50th percentile. Since the 84th percentile can never be less than

the 50th percentile, it follows that the geometric

standard deviation can never be less than one. In fact, the smallest value that the geometric

standard deviation can take is one, and this

happens when all the particles in an aerosol have the same exact diameter. That is, it is a

monodisperse aerosol . In this case, the 84th percentile is the same as

the 50th percentile, and every other percentile

for that matter. The larger the geometric standard deviation, the

more variability there is in particle size. The concentration of an aerosol can be

expressed in several different ways. First, we can express it as the number of

particles per cubic meter of air, or N, as shown

in the first row of this table. If we have particles of diameter d expressed in

units of nanometers, we can also calculate the

total surface area of all the particles with this diameter as N times pi times the square of the

diameter, where pi times the square of diameter is the surface area of one particle assuming that

the particles are spherical. This is the surface area concentration in

nanometers squared per cubic meter of air. We can calculate the volume of these particles

as N times pi times the cube of the diameter

divided by six. This is the volume concentration in nanometer

cubed per cubic meter of air. We can also multiply the volume concentration

by the density of the particles in the appropriate

units to get mass concentration in milligrams per cubic

meter of air. In this instance, the density of the particles is

expressed in milligrams per nanometer cubed

for the units to come out right. We can apply the relationships described in the

previous slide to each size bin in the particle

count table we encountered earlier The top histogram is the same as before, except

that instead of plotting particle count in each

size bin on the vertical axis, we are showing the count fraction in each

particle size range divided by the width of that

size range on a log scale. We can take the midpoint of each size interval

to be the representative diameter for all the

particles in that size interval. This diameter can then be used to convert count

in that size range to either surface area or volume, using the relationships shown in the

previous slide. Even though the size bin 50 to 80 nm has the

most number of particles, these particles contribute very little to the surface area and

almost none to the volume. contribute very little to the surface area and

almost none to the volume. Even though there are very few number of

particles in the size bin 890 to 1260 nm, these particles contribute a disproportionate

amount to the surface area and volume. This is because the surface area is proportional

to the diameter squared and the volume is

proportional to the diameter cubed, and so we see that the histograms change in

their shape as we go from a histogram based on particle count, to one based on surface area, to

one based on volume of the particles. This slide shows the type of size distribution by

mass that we can expect from some of the

activities we have discussed earlier. For instance, we saw a video of a worker using a

saw to cut a concrete block. This is a mechanical operation, and as we have

discussed earlier we expect to see a coarse

dust being generated. The histogram on the left shows the particle

mass distribution as a function of aerodynamic

diameter. Particles between roughly 2 to 20 µm

contributed most to the mass of the aerosol. The histogram on the right is typical of the

scenario when diesel operated machinery is used to haul crushed coal as we saw on the

second video. The diesel exhaust contributes particles

between roughly 0.1 to 0.5 µm while the coal dust contributes the most mass from

particles between roughly 1 to 20 µm. This is referred to as a bimodal distribution, and

reflects contributions to particle mass from two

aerosol sources. In the next segment of this module, we will

discuss the various elements of an aerosol sampling system and various sampling

strategies. At the bottom of the slide, we can see

schematically the components of an aerosol

sampling system. A particle laden air is drawn through a sampling

inlet. Unlike when we sample for gases and vapors,

the design of the aerosol sampling inlet ensures that only a specific size range of particles is

sampled. The aerosol then passes through a transport

section where some of the aerosol may be lost. This could be something as simple as a length

of duct or a tube where particles may deposit

and may not make it to the measurement zone. The aerosol then enters the sensing zone or

collection medium which captures the particles

or measures some relevant property. This can be a filter that collects airborne dust

that can then be analyzed by microscopy,

gravimetry, or other chemical means. It can also be a zone through which the aerosol

moves and interacts, for example, with a light beam, and in the process some

optical property gets sensed. Other elements of a sampling system include a

flow measurement device and the pump for

forcing the air through the system. Even though there are a wide array of aerosol

measuring instruments, they all share this basic

overall configuration. Depending on the reasons for sampling and

instrument design, several sampling strategies

can be employed: active vs. passive sampling, area vs. personal

sampling, and grab sampling vs. integrated

sampling. In active sampling the aerosol is drawn in

through a pump onto a collection medium, that is, an external source of energy is needed

to move the air through the sampling train. The photograph on the left shows aerosol

samplers on a rooftop. Air is drawn through the sampling inlet using an

electrical pump, and we can see electrical wires coming out of the pumps being connected to

outlets for power supply. The photograph on the right shows a person

wearing a sampler connected to a battery

operated pump. The battery is typically low power and, hence,

only a small flow rate of air can be drawn using

this pump, compared to the pumps on the left. In passive sampling, aerosol particles are

transported by gravity, diffusion, and inertia onto

the sampling surface. There is no external power source. The passive sampler shown in this photograph is

just a metal stub onto which particles deposit. The deposited particles can then be analyzed by

various analytical techniques, for example,

microscopy. Area sampling is done to determine general

background environmental conditions. These samplers on a rooftop sample the air in

the Twin Cities and provide a measure of the background aerosol concentration in the Twin

Cities metro area. In contrast, personal sampling is done to

determine the personal exposure of workers to aerosol by monitoring their breathing zone

concentration. In this photograph, you see an aerosol sampler

which is attached to a pump, and the sampler is

drawing air from the same general region of air from which the person is breathing, and

this volume is called the breathing zone of this

person. Another technique that is commonly being used

is to videotape a worker or a person carrying out

a series of tasks, and then overlay on it the real time measurements made by an instrument. In this photograph, we see a worker on the left

hand side doing various tasks and on the right

hand side we see the time trace of the exposure measured by a real time instrument. By focusing on the peaks, we know which tasks

contribute more to the exposure, and this By focusing on the peaks, we know which tasks

contribute more to the exposure, and this helps us focus our resources on addressing

those particular tasks for added attention. Time averaging is an important concept. Samples are collected over a period of time, and

represent an average over that time interval. In this graph, we show a time trace, that is, the

concentration as a function of time over a time

interval from zero to capital T. If we obtain a sample over a very short time

interval, as from t1 to t2, we refer to that as a

grab sample, and the grab sample is an average over that time interval t1 to t2. It is defined as

the integral of that concentration C as a function

of time over the time interval t1 to t2, over that time interval t1 to t2. It is defined as

the integral of that concentration C as a function

of time over the time interval t1 to t2. Then, we can divide this integral by the length of

the time interval, t2 minus t1.

0:51:23.066,0:51:23.033

Thus, it represents an average concentration

over a very small time interval, for example, just

a few minutes Thus, it represents an average concentration

over a very small time interval, for example, just

a few minutes In integrated sampling, the contaminant is

collected over a much longer period of time,

zero to capital T, for example. It could be an entire work shift or an entire day.

It represents an average concentration over this

entire time period. The integral in both these equations is the area

under the curve over the specific time interval: t1 to t2 in the case of grab sampling, and zero

to capital T in the case of integrated sampling. The concentration can also be expressed as the

mass of contaminant collected divided by the

sampling flow rate times the sampling time T. In the case of grab sampling, the amount of

mass collected is small and, hence, grab

sampling can only be used for identifying the contaminants rather than detailed quantification

of the mass. In integrated sampling, however, we can

typically collect a sufficient amount of

contaminant for quantitative assessment.

Very Precise and Understandable. Great lecture. Thanks for Sharing

Nice video đź‘Ś

Thank you for this video. I've been scouring for information and this is the first great source that isn't a peer reviewed paper.

Great Video.

can you please send a ppt of that lecture @METPHAST Program

can you gives this slides lecture ?

All right, is it possible to subtitle it in Spanish? Thank you