Supplementary material to manuscript:
Concentration, Size, and Density of Total Suspended Particulates at the Air
Exhaust of Concentrated Animal Feeding Operations
Xufei Yang1, Jongmin Lee1, Yuanhui Zhang*, Xinlei Wang, Liangcheng Yang
Department of Agricultural and Biological Engineering, University of Illinois at UrbanaChampaign, Urbana, IL 61801, USA
1
*
X. Yang and J. Lee contributed equally to this work.
Corresponding author: Tel: +1-217-333-2693; Fax: +1-217-244-0323; Email:
yzhang1@illinois.edu
Manuscript submitted to
Journal of the Air & Waste Management Association
February 2015
Ambient climate conditions during the sampling campaign
Ambient climate conditions, such as daily average (mean) temperature, moisture and pressure,
were summarized in Table S1. The ambient climate data sets were obtained from the nearest
local weather stations. We also monitored the outside air temperature and moisture near the
visited animal houses. However, the measurement data sets could be biased by the ventilation
of animal houses; therefore, they were not used for calculation.
Table S1. Ambient climate conditions during the sampling campaign (average ± standard
deviation)a.
Swine
Swine
Swine
Laying
Tom
Regional
finishing
farrowing
gestation
hen
turkey
averagec
winter
-5.3 ± 8.9
0.7 ± 0.6
0.7 ± 2.3
-8.0 ± 2.6
-2.7 ± 5.5
-1.4
Daily average
spring/fall 10.0 ± 1.7
15.0 ± 2.6
12.3 ± 2.5
14.0 ± 8.9
8.3 ± 5.1
12.2
temperature (⁰C)
summer
21.0 ± 1.6
21.7 ± 2.1
24.0 ± 2.0
24.0 ± 2.0
20.3 ± 3.1
23.4
winter
79 ± 17
77 ± 7
83 ± 6
73 ± 3
76 ± 7
n/a
Daily average
spring/fall
64 ± 7
68 ± 19
67 ± 12
62 ± 19
64 ± 13
n/a
moisture (%)
summer
70 ± 2
71 ± 17
72 ± 10
70 ± 12
76 ± 10
n/a
winter
30.02 ± 0.22 30.05 ± 0.06 29.88 ±0.21 29.99 ± 0.09 30.17 ± 0.15
n/a
Daily average
pressure
spring/fall 30.06 ± 0.32 29.82 ± 0.12 29.85 ± 0.16 29.98 ± 0.13 30.00 ± 0.21
n/a
(inch Hg)
summer 30.05 ± 0.15 30.00 ± 0.05 29.81 ± 0.16 29.99 ± 0.17 29.90 ± 0.11
n/a
Seasonsb
a
The data in the table shows the average and standard deviation of daily mean temperatures,
moistures or pressures from multiple farms of the same housing facility type.
c
Seasons were classified as: winter (December, January and February), spring (March, April
and May), summer (June, July and August), and fall (September, October and November).
b
The regional average climate conditions were calculated from the historical climate data for
Springfield, the state capital of Illinois. The city is located in the central Illinois. Thus, we
consider its climate conditions to be representative of most sampling sites.
Calibration of the Horiba LA-300 particle sizer
Mono-dispersed particle size standards (4000 series; Duke Scientific Corp., Fremont, CA)
were tested to calibrate the Horiba LA-300 particle sizer. The standard was delivered as an
aqueous suspension of polystyrene spheres (particle density: 1.05 g/cm3; refractive index:
1.59 at 589 nm [25 ºC]). The size of the standard was certified by the National Institute of
Standards and Technology (NIST) using optical microscopy.
Every size standard was tested three times. The coefficient of variance (CV) was all <2%,
indicating excellent precision and reproducibility. The test results are summarized in Table
S2. It is important to note that the test results are case-specific (depending on the instrument
and its working condition) and should not be generalized to other Horiba LA-300 particle
sizers.
Table S2. Test results of mono-dispersed particle size standards.
Certified particle
sizea, µm
10.0
25.1
49.7
100.0
200.0
VMD (d50), µm
10.6
27.4
55.1
123.0
247.5
Measured particle sizeb
d16, µm
d80, µm
7.5
12.8
20.7
33.5
41.2
73.4
92.6
171.5
182.1
361.9
GSD
1.31
1.27
1.30
1.36
1.37
a
The certified particle size is expressed in volumetric mean diameter.
b
VMD: volumetric mean diameter; dx: the diameter that corresponds to x% cumulative
volume fraction; and GSD: geometric standard deviation.
The Horiba yielded a widened particle size distribution (PSD) profile for mono-dispersed
particles, as indicated by the measured GSD values. This means that some particles were
misclassified into nearby size bins. To correct for this misclassification, a lognormal kernel
function was established based on the PSD profiles of mono-dispersed particles (Figure S1):
𝐾(𝑑) =
1
√2𝜋 ln 𝐺𝑆𝐷(𝑑𝑔
exp [−
)
(ln 𝑑−ln 𝑑𝑔 )
2
2(ln 𝐺𝑆𝐷(𝑑𝑔 ))
2
]
(S1)
where, K(d) is the probability at which a particle with actual diameter of dg is sized to be d,
and GSD(dg) describes the spread of the measured size distribution of mono-dispersed
particles with diameter of dg.
Figure S1. Measured size distribution of the 200-micron particle standard.
The measured GSDs were averaged (1.32±0.04); and given the small standard deviation, we
assumed that the average GSD applied to all size bins. Thus, the kernel function was
simplified as:
2
𝐾(𝑑) = 1.429 × exp [−6.417(ln 𝑑 − ln 𝑑𝑔 ) ]
(S2)
With the equation, an inversion of the PSD data was performed (eq S3), following the
STWOM algorithm proposed by Markowski (1987). This nonlinear iterative algorithm was
revised from the Twomey algorithm (Twomey, 1975) which has been extensively used for
PSD data analysis. A FORTRAN code, provided by Dr. Xiaoliang Wang (Wang, 2002), was
employed, and it was compiled and run with the Intel FORTRAN Compiler for Windows
14.0. The code was originally developed to retrieve PSD from the pulse height distribution
measured by optical particle counters (OPCs). A few minor modifications were made to make
it suitable to process the Horiba data sets.
𝑦𝑖 = ∑𝑛𝑗=1 𝐾𝑖 (𝑑𝑗 ) 𝑓(𝑑𝑗 )∆𝑑𝑗
(S3)
where, yi is the frequency of particles counted by the ith size bin, Ki(dj) is the probability that
a particle with actual diameter of dj (i.e., in the jth size bin) is counted by the ith size bin, f(dj)
is the frequency of particles with actual diameter of dj, Δdj is the width of the jth size bin, and
n is the total number of size bins.
The measured VMDs were used to establish a calibration curve (Figure S2). Once the
inversion is done, the obtained PSD data sets were corrected for the bias in size measurement
based on the slope of the calibration curve, as shown in Figure S3.
Figure S2. Calibration curve for the Horiba LA-300 particle sizer.
Figure S3. Processing of a particle size distribution data set: (A) raw data, (B) after inversion,
and (C) after correction for size measurement.
Size classification by the Horiba LA-300 particle sizer
The Horiba LA-300 particle sizer classifies particles with a diameter between 0.1 and ~592
µm into 64 size classes, as listed in Table S3.
Table S3. Size classes measured by the Horiba LA-300 particle sizer.
Size class #
Lower size limit
(µm)
Upper size limit
(µm)
Size class #
1
0.100
0.115
33
2
0.115
0.131
3
0.131
0.150
4
0.150
5
Lower size limit
(µm)
Upper size limit
(µm)
7.697
8.816
34
8.816
10.097
35
10.097
11.565
0.172
36
11.565
13.246
0.172
0.197
37
13.246
15.172
6
0.197
0.226
38
15.172
17.377
7
0.226
0.259
39
17.377
19.904
8
0.259
0.296
40
19.904
22.797
9
0.296
0.339
41
22.797
26.111
10
0.339
0.389
42
26.111
29.907
11
0.389
0.445
43
29.907
34.255
12
0.445
0.510
44
34.255
39.234
13
0.510
0.584
45
39.234
44.938
14
0.584
0.669
46
44.938
51.471
15
0.669
0.766
47
51.471
58.953
16
0.766
0.877
48
58.953
67.523
17
0.877
1.005
49
67.523
77.339
18
1.005
1.151
50
77.339
88.583
19
1.151
1.318
51
88.583
101.460
20
1.318
1.510
52
101.460
116.210
21
1.510
1.729
53
116.210
133.103
22
1.729
1.981
54
133.103
152.453
23
1.981
2.269
55
152.453
174.616
24
2.269
2.599
56
174.616
200.000
25
2.599
2.976
57
200.000
229.075
26
2.976
3.409
58
229.075
262.376
27
3.409
3.905
59
262.376
300.518
28
3.905
4.472
60
300.518
344.206
29
4.472
5.122
61
344.206
394.244
30
5.122
5.867
62
394.244
451.556
31
5.867
6.720
63
451.556
517.200
32
6.720
7.697
64
517.200
592.387
References
Markowski, G.R. 1987. Improving Twomey’s algorithm for inversion of aerosol
measurement data. Aerosol Sci. Technol. 7: 127-141.
Twomey, S. 1975. Comparison of constrained linear inversion and an iterative nonlinear
algorithm applied to the indirect estimate of particle size distribution. J. Comput. Phys.
18:188-200.
Wang, X. Optical particle counter (OPC) measurements and pulse height analysis (PHA)
data inversion. Master thesis, University of Minnesota, 2002.