Characterization of Nano-Aerosol Sampling
Instrumentation and Development of Data
Inversion Algorithms
Sayuri D. Yapa1, Suresh Dhaniyala2
Mechanical and Aeronautical Engineering, Clarkson University
Since the advent of the Industrial Revolution, the contribution of human activities to air
pollution has significantly increased. Present day air pollution is illustrated by the haze and smog
present in cities and national parks and by reports of acid rain. One particular component of air
pollution is aerosols, i.e., particles suspended in air. It has been acknowledged that aerosols play
an increasingly important role in controlling the earth’s climate, in the urban environment, and in
human health (Seinfeld and Pandis, 1998; IPCC 2007). Adverse human health effects such as
asthma exacerbations and cardiovascular and respiratory stress have been attributed to aerosol
exposure, especially aerosols produced from combustion. The relationship between air quality
and human health was dramatically illustrated during the London Smog episode of 1952 when it
is estimated that more than 4000 people died over a four-day period due to the combination of
pollution caused from coal burning and fog.
Our continued increase in the use of fossil fuels and the development of new combustion
technologies has resulted in new challenges in monitoring and controlling air pollution.
Subsequently, national and state agencies require monitoring and control of the net mass of these
particles, and such policies have helped to significantly improve air quality in cities since the
London Smog episode. Ambient aerosol particles range in size from ~ 2 nm to ~ 100 m. The
vast size range is due to the presence of varied sources and to the atmospheric processing of
particles. Several studies in the past decade have established that particles below 2.5 m (PM2.5)
pose the most hazards to human health.
In recent years, the development of new combustion technologies and tightened
restrictions for acceptable emissions from power plants and mobile sources have resulted in
significantly reduced mass of particles (e.g., less visible “black” soot emissions from diesel
engines), but possibly a greater number of nanoparticles (Kittleson, 1998). As small particles
have very small mass, current regulations can be satisfied by reducing total particle mass emitted
while increasing the number of nanoparticles emitted. It is now understood that nanoparticles,
due to their greater specific surface area and effective penetration to the human lung region, pose
a greater risk to human health than larger particles of the same mass (Oberdorster et al., 2005). In
addition, the emerging field of nanotechnology has resulted in the development of engineered
nanoparticles which pose significant health concerns. These developments suggest an immediate
need to develop an accurate method for characterization of nanoparticle populations in the
ambient and to determine our exposure to these particles. Conventional techniques are sensitive
to particle mass and cannot be easily extended to make size measurements of nanoparticles.
In Prof. Dhaniyala’s research group, several novel approaches are being studied for
nanoparticle size and composition characterization. One particular technique, on which we are
working, is derived from Millikan’s oil drop experiment. This technique is called Differential
Mobility Analysis (DMA) and uses the balance of electric field and particle drag to size-select
charged particles. This technique is effective for physical size characterization of particles
smaller than 500nm. As particle sizes approach 40nm and smaller, the diffusive nature of the
1
Mechanical and Aeronautical Engineering, Honors Program, Class of 2009
Mechanical and Aeronautical Engineering, Professor
2
particles (i.e., their random motion like that of molecules in air) complicates analysis of DMA
data. To solve this problem, we are researching a new type of DMA (called the Nano-Cross Flow
Differential Mobility Analyzer, NCDMA) that has been developed in our laboratory. The
NCDMA has a high resolution for particle sizes down to a few nanometers and will be easily
deployable for ambient measurements. The improved performance of the NCDMA has been
demonstrated theoretically (Song and Dhaniyala, 2007), but not yet experimentally.
We have been working to experimentally validate these theoretical predictions, which are
complicated by the need to generate airborne particles of known shape in the nanometer size
range. For nano-particle generation, we are using the commercially available Electrospray
Aerosol Generator (TSI model 3480). Along with another Honors student, Maria Lang, I’m
working on the characterization of the particle size distributions generated by the Electrospray
Generator. The particle size distributions are determined using a single-DMA experimental setup.
The data from the characterization experiments is presented as particle size distributions, which
tell us what particle sizes are being generated for a given expected particle diameter [Fig. 1].
6
4
Inversion from Stolzenburg and McMurry (dNdlnDp)
x 10
calculations from raw data
calculations from running mean
3.5
dNdlnDp (#/cc)
3
2.5
2
1.5
1
0.5
0
0
5
10
15
20
25
Particle Diameter, (nm)
(a)
30
35
40
(b)
Fig. 1. The particle size distributions for two different concentrations of sucrose solutions is
presented; Fig.1a presents the particle size distribution generated by the Electrospray Aerosol
Generator for an expected peak particle diameter of ~8 nm. Fig. 1b presents the particle size
distribution for an expected peak particle diameter of ~26 nm.
We are using this data to then characterize the transfer function of the NCDMA instrument.
The data from the characterization of the NCDMA will be analyzed to determine if this
instrument is more effective in sizing ultrafine particles than conventional DMAs.
Characterization of the NCDMA will be performed using tandem-DMA (TDMA) experiments
[Fig. 2]. In these experiments the upstream DMA (DMA1) remains at a fixed voltage, outputting
particles over a narrow size, while the downstream DMA (DMA2) is scanned over a range of
electrical mobilities or particle sizes. The TDMA results are then analyzed to determine the
transfer function of the test DMA (DMA 2) and its resolution. By locating different DMA
designs as the test DMA, their relative performances can be characterized in the ultrafine particle
size range.
Aerosol
Generator
Mixing
Chamber
85
Kr
DMA 1
CPC1
DMA
2
CPC 2
DAQ
Fig. 2. The TDMA experimental setup for characterization of the NCDMA.
Analysis of the experimental results to determine instrument sizing performance requires
advanced inversion algorithms that accurately account for particle and flow non-idealities in the
instrument. These algorithms will be able to account for both losses in the DMA as a result of the
instrument’s geometry as well as accounting for nanoparticle diffusion spreading in the
instrument.
I will discuss the design of the NCDMA instrument, the experimental methodology to
characterize the instrument performance, and the inversion algorithm developed for NCDMA
data analysis.
REFERENCES
Chen, Da-Ren, David Y.H.Pui, and Stanley L. Kaufman (1995). “Electrospraying of Conducting
Liquids for Monodisperse Aerosol Generation in the 4 nm to 1.8 μm Diameter Range.”
Journal of Aerosol Science, 26:963-977
Flagan, R. C. (1999) On Differential Mobility Analyzer Resolution. Aerosol Science and
Technology 30, 556-570.
Hinds, W.C. Aerosol Technology: Properties, Behavior, and Measurements of Airborne
Particles. USA: John Wiley and Sons, 1999.
IPCC, 2007, Climate Change 2007: The Physical Science Basis, Cambridge University Press,
Cambridge, UK.
Kittelson, D.B., 1998. Engines and nanoparticles: a review. Journal of Aerosol Science 29,
pp. 575–588.
Martinsson, Bengt G., Karlsson, Martin N.A., Frank, Goran (2001). Methodology to Estimate the
Transfer Function of Individual Differential Mobility Analyzers. Aerosol Science and
Technology, 35:4, 815-823. doi: 10.1080/027868201753227361
Oberdorster G, Oberdorster E, Oberdorster J. 2005b. Nanotoxicology: an emerging discipline
evolving from studies of ultrafine particles. Environ Health Perspect 113:823-839.
Seinfeld, J.H., and S.N. Pandis, Atmospheric Chemistry and Physics, Wiley-Interscience, 1998.
Song D.K., and Dhaniyala S., Change in distributions of particle positions by Brownian diffusion
in a non-uniform external field, Journal of Aerosol Science, doi: 10.1016/j.
jaerosci.2007.01.006, 2007.