4>EPA
EPA/600/R-14/191 | August 2014 | www.epa.gov/research
United States
Environmental Protection
Agency
Atmospheric LiDar Coupled
with Point Measurement Air
Quality Samplers to Measure
Fine Particle Matter (PM)
Emissions from Agricultural
Operations. Part 2 of the California
2007 - 2008 Tillage Campaigns:
Spring 2008 Data Analysis
RESEARCH AND DEVELOPMENT
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Atmospheric LiDar Coupled with Point
Measurement Air Quality Samplers to
Measure Fine Particle Matter (PM)
Emissions from Agricultural Operations,
Part 2 of the California 2007 - 2008
Tillage Campaigns:
Spring 2008 Data Analysis
David J. Williams
Office of Research and Development
National Exposure Research Laboratory
Environmental Sciences Division
Landscape Characterization Branch
Research Triangle Park, North Carolina
Jerry Hatfield
National Soil Tilth Laboratory
Agricultural Research Service
United States Department of Agriculture
Ames, Iowa
Although this work was reviewed by EPA and approved for publication, it may not necessarily reflect
official Agency policy. Mention of trade names and commercial products does not constitute
endorsement or recommendation for use.
U.S. Environmental Protection Agency
Office of Research and Development
Washington, DC 20460
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Atmospheric LiDAR coupled with point measurement air quality samplers to measure fine
particulate matter (PM) emissions from agricultural operations. Part 2 of the California 2007-
2008 Tillage Campaigns: Spring 2008 Data Analysis
Authors:
Jerry Hatfield
National Soil Tilth Laboratory
Agricultural Research Service
United States Department of Agriculture
Ames, Iowa
David Williams
Environmental Sciences Division
National Exposure Research Laboratory
U.S. Environmental Protection Agency
Research Triangle Park, NC
James Sweet
Planning Division
Central Region Office
San Joaquin Valley Air Pollution Control District
Fresno, CA
Sona Chilingaryan
Air Division
Region 9
U.S. Environmental Protection Agency
San Francisco, CA
* Corresponding author. (919) 541-2573; williams.davidj@epa.gov
Disclaimer Notice: The United States Environmental Protection Agency through its Office of
Research and Development partially funded and collaborated in the research described here
under Interagency Agreement: DW 12922568 to Space Dynamics Laboratory. It has been
subjected to Agency review and approved for publication. Mention of trade names or
commercial products does not constitute endorsement or recommendation for use.
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Report Prepared By:
Dr. Gail Bingham
SDL Chief Scientist & Civil Space and Environment Division Leader
Space Dynamics Laboratory
1695 North Research Park Way
North Logan, UT 84341
Jennifer Bowman
Senior Manager, Internal Communications
ATK Aerospace Systems
P.O. Box 98
5000 S. 8400 W.
Magna, UT 84044
Christian Mar chant
Energy Dynamics Laboratory
1695 North Research Park Way
North Logan, UT 84341
Dr. Randal Martin
Research Associate Professor
Department of Civil and Environmental Engineering
Utah State University
41 10 Old Main Hill
Logan, UT 84322
Kori Moore
Environmental Engineer
Energy Dynamics Laboratory
1695 North Research Park Way
North Logan, UT 84341
Dr. Philip Silva
Environmental Chemist
Animal Waste Management Research Unit
USDA Agricultural Research Service
230 Bennett Lane
Bowling Green, KY 42 104
Dr. Michael Wojcik
Branch Chief for Environmental Measurement
Energy Dynamics Laboratory
1695 North Research Park Way
North Logan, UT 84341
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Space Dynamics
LABORATORY
Utah State University Research Foundation
California Spring 2008 Tillage Campaign: Data
Analysis
A project performed for the San Joaquin Valleywide Air Pollution Study
Agency, Contract 07-1 AG
Submitted To:
James Sweet
Planning Division
Central Region Office
San Joaquin Valley Air Pollution Control District
Fresno, CA
David Williams
Environmental Sciences Division
National Exposure Research Laboratory
U.S. Environmental Protection Agency
RTF, NC
Sona Chilingaryan
Air Division
Region 9
U.S. Environmental Protection Agency
San Francisco, CA
Dr. Jerry Hatfield
National Laboratory for Agriculture and the Environment
Agricultural Research Service
United States Department of Agriculture
Ames, IA
Submitted By:
Space Dynamics Laboratory/ Utah State University Research Foundation
1695 North Research Park Way
North Logan, Utah 84341
DOCUMENT NUMBER: SDL/08-556
REVISION:
DATE: JUNE 21,2013
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TABLE OF CONTENTS
TABLE OF ABBREVIATIONS/VARIABLES ix
EXECUTIVE SUMMARY 1
1. Background 3
1.1 Literature Review 4
2. Experiment Design 12
2.1 Site Description 12
2.2 Operation Description 12
2.3 Tillage Operation Data 18
3. Measurements and Methods 20
3.1 Measurement Overview 20
3.1.1 Meteorological Measurements 21
3.1.2 Wind Profile Calculations 25
3.1.3 Soil Characterization 26
3.1.4 Air Quality Point Samplers 27
3.1.5 Lidar Aerosol Measurement and Tracking System 32
3.2 Dispersion Modeling Software 37
3.3 Statistical Analysis of Data 40
4. Results and Decisions 41
4.1 General Observations 41
4.1.1 Soil Characteristics 41
4.1.2 Meteorological Measurements 43
4.2 Aerosol Characterization Data 46
4.2.1 Minivol Filter Sampler Data 46
4.2.2 PM Chemical Analysis 48
4.2.3 Aerosol Mass Spectrometer 54
4.3 Optical Characterization Data 56
4.3.1 Met One Optical Particle Counter 56
4.3.2 Optical To PM Mass Concentration Conversion 61
4.3.3 Lidar Aerosol Concentration Measurements 66
4.4 Fluxes and Emissions Rates 67
4.4.1 Lidar Based Fluxes and Emission Rates 69
4.4.2 Inverse Modeling Calculations 73
4.5 Derived Emission Rate Comparison 81
SDL/08-556 California 2008 Tillage Campaign i
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5. Summary and Conclusions 85
6. Lessons Learned 90
7. Acknowledgments 92
8. Publications 93
9. References 94
10. Appendices 99
10.1 Appendix A: Data and Settings Tables 99
10.2 Appendix B: Investigations into and conclusions from filter-based data 108
10.3 Appendix C: Responses to comments received re: California Spring 2008 Tillage
Campaign: Data Analysis Report 120
10.3.1 DraftDate: 16 March 2011 (Organization: U.S. EPA) 120
10.4 Draft Date: 15 April 2013 (Organization: San Joaquin Valley Air Pollution Control
District) 126
10.4.2 Presentation of Results and Discussion with the San Joaquin Valley Air Pollution
Control District's Ag Technical Group: 22 April 2013 131
SDL/08-556 California 2008 Tillage Campaign
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TABLE OF FIGURES
Figure 1. Histogram of disc operation emissions factors and both the empirical and the Weibull
cumulative distribution functions (CDFs) 11
Figure 2. Shaded relief map of the State of California, USA, with the location of the selected
sample site shown by the white star. Image from geology.com [33] 13
Figure 3. Satellite image of the study location with soil types shown in white. The study fields
are outlined within the two rectangles. Soil type 130 represents Kimberlina fine sandy loam,
saline-alkali [34] 14
Figure 4. Photo taken standing near the southern edge of Field 5 looking north across Field 5
towards Field 4. Note the relative flatness of the terrain 15
Figure 5. Orthman 1-tRIPr in operation during this field experiment 16
Figure 6. Wind rose for May and June of 2005 - 2007 as recorded by the CEVIIS Station # 15
(Stratford). No calm periods were recorded 20
Figure 7. Sample layout used for Field 4 22
Figure 8. Sample layout used for Field 5 24
Figure 9. Two Airmetrics MiniVol Portable Air Samplers and a Met One Instruments Optical
Particle Counter (OPC) deployed for field sampling during Spring 2008 tillage study 28
Figure 10. The three wavelength Aglite lidar at dusk, scanning a harvested wheat field 32
Figure 11. The Aglite lidar retrieval algorithm flow chart, showing the input locations for the in
situ data 33
Figure 12. (A) Conceptual illustration of the method for using lidar to generate time resolved
local area particulate fluxes. (B) An example of a "staple" lidar scan over the facility showing
aerosol concentration on the three sides of the box 34
Figure 13. Example of a lidar scan profile used to monitor PM concentrations around and
emissions from conventional tillage operations in Field 4. Each data point represents a 0.5
second averaging time, therefore data point 1000 was taken at time = 500 seconds 36
Figure 14. Example of a lidar scan profile used to monitor PM concentrations around and
emissions from conservation tillage operations in Field 5. Each data point represents a 0.5 second
averaging time, therefore data point 1000 was taken at time = 500 seconds 36
Figure 15. Soil sample collection locations in fields under study 43
Figure 16. Wind rose of wind speed and direction measured during the May-June 2008 campaign
44
Figure 17. Cup anemometer measurements shown with the wind speed profiled calculated using
the measured wind speed 46
Figure 18. Contour plot of measured PMio concentration for the June 25 sample period 47
Figure 19. PM2.s OC/EC time series concentrations as collected at the downwind AQT location.
The shaded sections indicate the observed agricultural practices. It should also be noted that the
SDL/08-556 California 2008 Tillage Campaign iii
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raw instrument OC concentrations have been multiplied by 1.7 to account for potential non-
carbon functional groups 49
Figure 20. PM2.5 organic matter and EC concentrations during specific sampling periods (parallel
to filter-based sampling) 50
Figure 21. PM2.5 organic matter and elemental carbon concentrations during specific sampling
periods (parallel to filter-based sampling) 51
Figure 22. Soluble ionic mass concentrations of AQT downwind PM2.5 filters 52
Figure 23. Average soluble ionic mass percentage composition of the AQT downwind PM2.5
filters. The error bars represent the 95% confidence interval 53
Figure 24. Average compositional mass percentage of the AQT downwind PM2.5 filters 53
Figure 25. Average chemical composition of particles detected by the AMS from May 14-15... 54
Figure 26. Representative AMS mass spectrum of particles detected during the study. Mass-to-
charge (m/z) ion assignments include nitrate (m/z 30 NO+ and m/z 46 NC>2+), sulfate (m/z 48 SO+,
64 SO2+, 81 HSO3+, and 98 H2SO4+), and carbon (e.g. m/z 55 C4H7+, 57 C4H9+, 77 C6H5+, 91
C7H7+) 54
Figure 27. (a) Image plot of nitrate particles on May 14, 2008 using m/z 30 (NO+). This shows
the formation of a mode of nitrate particles in the early morning hours of 5/14/2008, with peak
mass concentration at -3:00 AM Pacific Standard Time, (b) Integrated size distribution of nitrate
particles from 2:00-4:00 AM on May 14, 2008 showing the peak in the mass distribution at
-0.65 |im 55
Figure 28. Comparison of AMS PMi and OPC PMi (assuming a MCF of 1.0 g/cm3) data for the
morning of May 15,2008 56
Figure 29. Particle volume size distributions measured from upwind (background) and
downwind (background plus emissions) locations, with the difference being the aerosol emitted
by the tillage activity: (a) strip-till operation, conservation tillage method; (b) disc 2 operation,
conventional tillage method, (c) plant operation, conservation tillage method; and (d) plant
operation, conventional tillage method 58
Figure 30. OPC PM time series, created by multiplying the volume concentrations (Vk) by the
daily MCF, as measured at elevated locations in a) an upwind and b) a downwind positions
during the May 19 R2 sample period 59
Figure 31. Contour plots of average OPC a) number concentration (#/L) for particles larger than
1 jam and b) PMio concentration (|ag/m3) across the field for the third cultivator pass on June 25.
60
Figure 32. Average daily measured MCF values with error bars representing the 95% confidence
intervals 61
Figure 33. Sample period MCF values compared with the length of each sample period 63
Figure 34. PM2.5, PMio, and TSP mass concentrations retrieved from collocated lidar and OPC
during the 'stare' time series for 6/18. Data acquisition time of the lidar data is 0.5 s while OPCs
were set to 20 s accumulation time. Measurements were done on the upwind side of facility
(location Tl) 64
SDL/08-556 California 2008 Tillage Campaign iv
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Figure 35. PM2.5, PMio, and TSP mass concentrations retrieved from collocated lidar and OPC
during 'stares' at downwind locations. Data acquisition time of the lidar data point is 0.5 sec
while OPCs were set to 20 sec accumulation time. Measurements were done on the downwind
side of field (location T2) on 6/18/2008 65
Figure 36. Wind speed, wind direction, upwind and downwind plume area average particulate
volume concentrations, for the May 17, 2008 strip-till pass of the conservation tillage method. 68
Figure 37. Wind speed, wind direction, upwind and downwind plume area averaged particulate
volume concentrations for the May 19, 2008 first disc pass of the conventional tillage operation.
68
Figure 38. Lidar-derived fluxes (|ig/m2/s) of PM2.5, PMio, and TSP for the May 17, 2008 strip-till
pass of the conservation tillage method 69
Figure 39. Lidar derived fluxes (|ig/m2/s) of PM2.5, PMio, and TSP for the May 19, 2008 first
disc pass of the conventional tillage operation 70
Figure 40. ISCST3-modeled results for the third cultivator pass of the conventional tillage
operations on June 25, 2008 with northwest winds. The area of operations and sampler locations
are denoted in black and contour line numerical values are in |ig/m3 75
Figure 41. AERMOD modeled results for third cultivator pass of the conventional tillage
operations on June 25, 2008 with northwest winds. The area of operations and sampler locations
are denoted in black and contour line numerical values are in |ig/m3 76
Figure 42. A comparison of PM2.5, PMio, and TSP emission rates ± 95% confidence intervals
derived through lidar scanning techniques and inverse modeling using ISCST3 and AEJAMOD
dispersion models and measured PM concentrations 83
Figure 43. Four adjacent microscopic images taken with the lOx lens that have been combined
for determining the particle size distribution and count at this location on a PMio sample filter.
The largest particle with dpA 31.94 jam is shown by the blue circle Ill
Figure 44. Number distributions based on projected area diameter as measured via microscopy
for two downwind and one upwind PM2.5 filters and two downwind PMio filters used during the
Lister pass on May 20, 2008 112
Figure 45. Line graphs showing the number of filters in each ring particle density category for
each sample period 117
Figure 46. Categorization of filter samples to determine suitability for use in emission rate
calculations in inverse modeling 119
SDL/08-556 California 2008 Tillage Campaign
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LIST OF TABLES
Table 1. Emission rates ± 95% confidence intervals found by SDL [7] using lidar measurements
for a conventional fall tillage sequence and a Combined Operations CMP sequence 4
Table 2. Emission factors and uncertainties for land preparation as reported by Flocchini et al.
(2001) [12] 6
Table 3. Emission factors used by the California Air Resources Board in estimating agricultural
tilling PMio emissions [13] 6
Table 4. Conventional and conservation tillage emission rates reported by Madden et al. (2008)
for tillage in a dairy forage crop rotation [16]. ST= standard tillage method, CT = conservation
tillage method 7
Table 5. Estimated distribution parameters and goodness-of-fit, as root mean square error
(RMSE), for the lognormal and Weibull distributions fit to tillage operation datasets with > 8
data points. Values in bold represent model with a better fit 11
Table 6. Emissions factor values corresponding to statistical measures of interest for the
distributions fitted to the various tillage operation datasets 11
Table 7. Tillage operations and dates performed for the comparison study 16
Table 8. Agricultural equipment used to perform the tillage operations 17
Table 9. Operation data for both the conventional and conservation tillage studies as recorded by
field personnel 18
Table 10. Sample period, total tractor operation time, and the sample period-to-tractor operation
time ratio for all sample periods 19
Table 11. Summary of instruments located at each position for the conventional tillage study of
Field 4. All heights given as above ground level (agl) 22
Table 12. Summary of instruments located at each position for the Conservation tillage study of
Field 5. All heights given as above ground level (agl) 24
Table 13. Statistics of soil characteristics measured for both fields 41
Table 14. Stable aggregate analysis results for both fields 42
Table 15. Average ± la temperature, relative humidity, wind speed, and wind direction for each
sample period as measured at the WM tower. Temperature, relative humidity, and wind speed
were measured at 9.7 m agl and wind direction was measured at 11.3 m agl 45
Table 16. PM2.5 filter ion concentrations averaged over seven downwind samples collected at
AQT (uncertainty represents the 95% confidence interval) 52
Table 17. Mass conversion factors (g/cm3) used to convert optical particle measurements to mass
concentrations for each day and averaged for the whole campaign. Error values represent the
95% confidence interval for ri>3 62
Table 18. Comparison of PM mass concentrations (|ig/m3) as reported by MiniVol samplers and
mean values measured by collocated OPCs and lidar at Tl (upwind) and T2 (downwind) for
6/18/2008 64
SDL/08-556 California 2008 Tillage Campaign vi
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Table 19. Total number of upwind and downwind vertical lidar scans and the number of those
scans determined to be valid for emission rate calculations. No lidar data exists for the 6/25 run
because of instrument problems after the 6/18 run 66
Table 20. Mean fluxes (|ig/m2/s) ± 95% confidence interval from quality controlled samples for
each tillage operation 71
Table 21. Aerosol mass transfer (± 95% confidence interval) from each field (flux normalized by
operation duration and area tilled) as calculated from lidar data for all tillage operations 72
Table 22. Tillage operations, number of passes, area tilled per pass during monitoring period, and
the seed emission rate used for modeling particle dispersion using ISCST3 and AERMOD for
each sample period 74
Table 23. Mean emission rates per unit area per unit time (± 95% CI for n > 3) for each operation
as determined by inverse modeling using ISCST3 78
Table 24. Mean emission rates per unit area (± 95% CI for n > 3) for each operation as
determined by inverse modeling using ISCST3 79
Table 25. Mean emission rates per unit area per unit time (± 95% CI for n > 3) for each operation
as calculated by inverse modeling using AERMOD 80
Table 26. Mean emission rates per unit area (± 95% CI for n > 3) for each operation as calculated
by inverse modeling using AERMOD 81
Table 27. Calculated PMio emission rates (± 95% confidence interval) from the lidar and inverse
modeling using two dispersion models 84
Table 28. A comparison of PMio emission rates herein derived and found in literature 87
Table 29. Particulate emissions, from Lidar data and inverse modeling with OPC data, and tillage
rate comparison between conventional and conservation tillage 88
Table 30. Settings for the ISCST3 and AERMOD dispersion models for the tillage study in the
ISC-AERMOD View software by Lakes Environmental, Inc. All settings were held constant
across the sample periods except the source area size and shape, which changed each day, and
the downwind receptor locations, which were specific to each field studies. (— = not applicable)
99
Table 31. Calculated PM concentrations (ng/m3) measured during May 2008 at all sample
locations 101
Table 32. Calculated PM concentrations (|ag/m3) measured during June 2008 at all sample
locations 102
Table 33. PM concentrations (ng/m3) used in emission rate calculations from sample periods in
May 2008 103
Table 34. PM concentrations (ng/m3) used in emission rate calculations from sample periods in
June 2008 104
Table 35. PM concentrations (ng/m3) as measured by OPCs from sample periods in May 2008.
105
SDL/08-556 California 2008 Tillage Campaign vii
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Table 36. PM concentrations (ng/m3) as measured by OPCs from sample periods in June 2008.
106
Table 37. PM concentrations (ng/m3) as measured by OPCs used in emission rate calculations
from sample periods in May 2008. (OPC PM,t = V* x MCF,t) [[[ 107
Table 38. PM concentrations (ng/m3) as measured by OPCs used in emission rate calculations
from sample periods in June 2008. (OPC PM,t = V* x MCF,t) [[[ 108
Table 39. Size fractionated results of the visual inspection of annular filter rings ..................... 115
Table 40. Results of the visual inspection of filter rings by sample date ................................... 116
Table 41. Sample period filter datasets used in calculating emissions, with reasons for why some
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TABLE OF ABBREVIATIONS/VARIABLES
Abbr./Var.
PE
Pv
n
ng
HL
pirn
Pv
o
Ow
°v
Oz
2D
3D
,4
ACS
Activity
AERMOD
agl
AMS
AQT
ARS
Avg
AvgHp
bhp
CIQ
CD
n
^ measured
n
^ modeled
Co
c
CE
CFR
/^ +2
Ca
CARB
CCF
CCV
CF
CHrs
CI
CIMIS
cr
CMP
CO2
CT
d
Definition
backscatter values measured by lidar in vector form (1/m-steradians)
particle normalized backscatter from OPC data in vector form ( 1/m-steradians)
control efficiency
micrograms (IxlCT6 gram)
microliter (IxlCT6 liter)
micrometers (IxlCT6 meter)
density of water vapor
standard deviation
standard deviation of the vertical wind (m/s)
horizontal plume spread parameter (meter)
vertical plume spread parameter (meter)
two-dimensional
three-dimensional
acres of land tilled
American Chemical Society
amount of time the engine is active during the year (hr/yr)
American Meteorological Society/Environmental Protection Agency Regulatory Model
above ground level
aerosol mass spectrometer
air quality trailer
Agricultural Research Service
average
maximum rated average horsepower (bhp)
brake horsepower
10 minute average pollutant concentration (ng/m3)
downwind PMK concentration (ng/m3)
measured PM concentration (ng/m3)
modeled concentration (ng/m3)
scan area average upwind PMK concentration (ng/m3)
Carbon dioxide concentration
constant, 4.8 Ib/acre-pass
Code of Federal Regulations
Calcium ion
State of California Air Resources Board
counting correction factor (unitless)
continuing calibration verification
conversion factor from (g/yr) to (tons/day)
cumulative engine operation hours (hr)
confidence interval
California Irrigation Management Information System
Chloride ion
conservation management practice
Carbon dioxide
conservation tillage
particle diameter (\m\)
SDL/08-556
California 2008 Tillage Campaign
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Abbr./Var.
daero
QZ, upper
QZ, /ower
4
dpA
DDI
DF
dr
E
E
ECT
f
^estimated
EPM
Tf
l-'seed
ESTT
EC
EC
EC/OC
EF
EFacjj(pM)
FF
L-'i ss
Emissions
EPA
F
F
FRM
GMD
GPS
H
H
H2SO4
HC1
hp
lirtractor
1C
IRGA
ISCST3
k
r
LE
Load
Lpm
M
m
MCF
MDL
Mg+2
Definition
aerodynamic diameter (nm)
upper edge of bin / (nm)
lower edge of bin / (nm)
particle of diameter k (\m\)
projected area diameter (\m\)
double-distilled, de-ionized water
engine deterioration factor (unitless)
engine deterioration rate (g/bhp-hr2)
east
flux of water vapor from the soil surface
emission rate for the conventional tillage method
derived emission rate (ng/s-m2)
PM emission (Ibs)
seed emission rate (ng/s-m2)
emission rate for the strip-till tillage method
elemental Carbon particulate matter
eddy covariance
elemental Carbon/organic Carbon particulate sampler
emission factor (g/bhp-hr)
adjusted PM emission factor (g/hp-hr)
steady-state measured emission factor (g/hp-hr)
amount of pollutant released (tons/day)
United States Environmental Protection Agency
particulate flux in lidar scans (ng/s)
Fluoride ion
federal reference method
geometric mean diameter (nm)
global positioning system
sensible heat
effective stack height (meter)
Hydrogen sulfate
Hydrogen chloride
horsepower
tractor operation hour
ion chromatography
infrared gas analyzers
Industrial Source Complex, Short Term Ver. 3
dimensionless particle size multiplier
Potassium ion
latent heat
load factor
liters per minute
molar
meter
mass conversion factor (g/cm3)
minimum detection level
Magnesium ion
SDL/08-556
California 2008 Tillage Campaign
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Abbr./Var.
N
N,,
Nt
n
n(r)
Na+
NAAQS
NaOH
NC
NEVES
ng
NH4+
NO2"
NO3
NOX
NONROAD
NRCS
OC
OFFROAD
OPC
P
Pii
PM
PM,0
PM25
PM10
PM*
PMK(r)
ppm
PSL
Q
1
to
RARE
RD
S
S
SDL
SO42
SpMadj
ST
t,
tp
TAP
TD
TSP
Definition
north
average number concentration per bin /' for OPC j (number/cm3)
mean of the averages across all OPCs for bin / (number/cm3)
number of samples
relative amplitude of the aerosol component of total atmospheric backscatter at range r
(unitless)
Sodium ion
National Ambient Air Quality Standard
Sodium hydroxide
not calculated
Nonroad Engine and Vehicle Emission Study
nanogram (IxlCT9 gram)
Ammonium ion
Nitrite ion
Nitrate ion
Oxides of nitrogen
U.S. EPA Nonroad Emissions Model
Natural Resources Conservation Service
Organic Carbon
CARB Offroad Emissions Model
optical particle counter
power law wind speed coefficient (unitless)
raw OPC particle counts in bin / (number)
particulate matter
particulate matter with an aerodynamic diameter less than or equal to 1.0 micrometers
particulate matter with an aerodynamic diameter less than or equal to 2.5 micrometers
particulate matter with an aerodynamic diameter less than or equal to 10 micrometers
particulate matter with an aerodynamic diameter less than or equal to k micrometers
particulate matter with an aerodynamic diameter less than or equal to K micrometers,
calculated from lidar data, at range r
parts per million
polystyrene latex sphere
emission rate (ng/s)
water vapor concentration
average measured sample flow for OPC j (cm3/min)
Regional Applied Research Effort
respirable dust
south
silt content of surface soil (%)
Space Dynamics Laboratory
Sulfate ion
diesel sulfur content adjustment factor (g/hp-hr)
standard tillage
sample time for OPC j (min)
number of passes or tillings per year
transient adjustment factor (unitless)
total dust
total suspended particulate
SDL/08-556
California 2008 Tillage Campaign
XI
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Abbr./Var.
M
U*
Ui
U2
UGM
u w
UC
USDA
UWRL
v£
v,
VK(r)
V
^(r,h)
vw
W
W
W
w
wss
y
z
Zl
Z2
ZH
Definition
wind speed in the streamwise direction (m/s)
friction velocity (m/s)
Measured reference wind speed (m/s)
Calculated wind speed based on the reference wind speed (m/s)
mean wind speed at the release height H (m/s)
momentum term of the streamwise and vertical wind directions (m2/s2)
University of California
United States Department of Agriculture
Utah Water Research Laboratory
particle normalized volume concentration vector (nm3/cm3)
OPC cumulative volume concentration up to a diameter of k (nmVcm3)
cumulative volume concentration from lidar data at range r (nm3/cm3)
wind speed in the lateral direction (m/s)
average wind speed component at range r and height h that is parallel to the long axis of
the lidar staple box
momentum term of the lateral and vertical wind directions (m2/s2)
west
watt
diagonal weighting matrix used in lidar data analysis (unitless)
wind speed in the vertical direction (m/s)
web soil survey
horizontal distance of the modeled receptor from the centerline of the plume (meter)
height of modeled receptor above ground level (meter)
height at which the reference wind speed was measured (m)
height of the calculated wind speed (m)
zero-hour engine emission rate (g/bhp-hr)
SDL/08-556
California 2008 Tillage Campaign
xn
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The statements and conclusions in this report are those of SDL and not necessarily those of the
California Air Resources Board, the San Joaquin Valleywide Air Pollution Study Agency, or its
Policy Committee, their employees or their members. The mention of commercial products, their
source, or their use in connection with material reported herein is not to be construed as actual or
implied endorsement of such products.
SDL/08-556 California 2008 Tillage Campaign xiii
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EXECUTIVE SUMMARY
Airborne particles, especially particulate matter 10 micrometers (um) or smaller in aerodynamic
diameter (PMio) and fine participate matter 2.5 jim or smaller in aerodynamic diameter (PM^.s),
are microscopic solids or liquid droplets in the air that can cause serious health problems (e.g.,
coughing, difficulty breathing, decreased lung function, asthma, heart attacks, and premature
death), especially in people with heart and lung disease. Concern with health effects resulting
from PMio exposure is drawing increased regulatory scrutiny and research toward local
agricultural tillage operations. To investigate the control effectiveness of one of the current
Conservation Management Practices (CMPs) written for agricultural land preparation on the
generation of particulate matter (PM) levels, the San Joaquin Valleywide Air Pollution Study
Agency funded a project to study:
1) The magnitude, flux, and transport of PM emissions produced by agricultural
practices for row crops where tillage CMPs are implemented vs. the magnitude,
flux, and transport of PM emissions produced by agricultural practices where
CMPs are not implemented.
2) The control efficiencies of equipment used to implement the "conservation
tillage" CMP. If resources allow assessing additional CMPs, what are the
control efficiencies of the "equipment change/technological improvements"
CMP?
3) If these CMPs for a specific crop can be quantitatively compared, controlling
for soil type, soil moisture, and meteorological conditions.
This study used advanced measurement technologies, which link lidar systems with conventional
point-measurement air quality samplers, to map PM emissions at high spatial and temporal
resolution in order to accurately compare CMPs with conventional tillage systems. The purpose
of this field study was to determine if and how much particulate emissions differ between the
conventional method of agricultural fall tillage and a conservation tillage CMP. It is a
companion study to an earlier study performed in October 2007 near Los Banos, CA
investigating the control efficiency of combined operations CMP versus conventional tillage in
the fall tillage sequence. Findings from that study are detailed in a previous report [7].
The test location and CMP to be evaluated were chosen in discussion with stakeholders,
regulatory agencies, and researchers. The current study was performed in the San Joaquin Valley
of California in May and June of 2008 during the spring tillage sequence between a winter wheat
crop and corn. The conventional 13-pass spring tillage sequence for a field going from winter
wheat into corn was: two passes for in-field irrigation border breakdown, chisel, two disc passes,
lister, two cultivator passes, roll, plant, fertilizer injection, and two more cultivator passes. The
spring conservation tillage CMP sequence consisted of only three tractor passes: strip-till,
plant/fertilize, and herbicide spray; specifically, the CMP implement investigated was the strip-
till implement.
An extensive network of measurement systems were used during this study, including a scanning
lidar, a full meteorology suite, four sonic anemometers (for turbulence information), and filter
and optical aerosol point samplers. Two additional aerosol chemical analysis systems were
employed from a sampling trailer located on the downwind side of the field under test.
Tillage particulate emission rates were determined using two methods: 1) inverse modeling
coupled with observed facility-derived concentrations from filter- and optical-based instruments,
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and 2) a mass balance approach applied to upwind and downwind PM concentrations measured
by the lidar. The tillage emissions were modeled using two different air dispersion models: the
Industrial Source Complex Short-Term Model, version 3 (ISCST3) and the American
Meteorological Society/Environmental Protection Agency Regulatory Model (AERMOD).
Emission data calculated for each measurement method for the conventional and conservation
tillage operations are presented herein. The study showed that the conservation practice required
< 1/4 of the number of tractor passes when compared to conventional tillage; similar reductions
in fuel use and tractor exhaust associated PMio emissions were expected to have occurred.
Lidar-derived and inverse modeling emission rates for PM2.s, PMio, and total suspended
particulate (TSP) by operation, as well as the average tillage rate in hours per hectare are
summarized herein. Based on lidar data, the conservation tillage method reduced PM2.5 emission
by 91%, PMio by 94%, and TSP by 91%, which were all statistically significant differences.
Reduced emissions as calculated using inverse modeling and optical particle counter data are
very close to lidar-derived reductions at 85%, 87%, and 90% for PM2.5, PMio, and TSP,
respectively. The time per hectare required to perform the conservation tillage was about 14% of
the conventional method. The control efficiency of the Conservation Management Practice for
particulate emissions was 0.905, 0.937, and 0.909 for PM2.s, PMio, and TSP, respectively, based
on lidar data and 0.853, 0.872, and 0.903 for PM2.5, PMio, and TSP, respectively, based on
inverse modeling with optical particle counter data.
Some of the derived PMio emission rates from this experiment agree with those reported in the
literature, as well as the emission factors used by the State of California Air Resources Board
(CARB) to calculate area source PMio contributions from agricultural tilling [14]; others do not
agree and are significantly higher, such as the emission rates for the chisel, disc 1, disc 2, and
lister passes. Emissions factor values estimated through inverse modeling for these operations
are below the 95% level predicted by statistical distributions fitted to published data; emissions
estimates from lidar for the same operations are above the 95% level. While values from
published studies are generally not in close agreement, they are within the range of the variability
expected from measurements made under different meteorological and soil conditions, as
demonstrated by the wide range of values in the literature [12][16]. In general, the emission rates
calculated from this study are higher than others reported but are within the variability of other
reported emission rates.
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1. BACKGROUND
Airborne particles, especially parti culate matter 10 micrometers (um) or smaller in aerodynamic
diameter (PMio) and fine participate matter 2.5 jim or smaller in aerodynamic diameter (PM^.s),
are microscopic solids or liquid droplets in the air that can cause serious health problems (e.g.,
coughing or difficulty breathing, decreased lung function, aggravated asthma, development of
chronic bronchitis, irregular heartbeat, heart attacks, and premature death), especially in people
with heart or lung disease [3]. Particles larger than 1.0 um tend to be prevented from entering the
lungs by the nose and throat [4]. The U.S. Environmental Protection Agency (U.S. EPA) has
established limits for PIVb.s and PMio levels in order to protect public health as part of the
National Ambient Air Quality Standards (NAAQS) [5][6]. The U.S. EPA requires state air
quality management agencies to monitor ambient PM2.5 and PMio concentrations in order to
identify possibly hazardous conditions for the population; to report areas that exceed the
NAAQS beyond the allowed number of times; and to establish procedures to reduce particulate
concentrations to meet the standards.
To address the problems associated with exposure to high particulate matter (PM) levels, the
U.S. EPA has been working with the San Joaquin Valley Air Pollution Control District to
support PM research. In 2007 the Environmental Sciences Division, National Exposure Research
Laboratory was awarded a Regional Applied Research Effort (RARE) grant to determine the
control effectiveness of a Conservation Management Practice (CMP) for agricultural tillage,
targeted toward reducing PMio emissions, using advanced measurement technologies such as
atmospheric light detection and ranging (lidar) systems. The field study was carried out in
October 2007 in the San Joaquin Valley [7]. This report describes the 2008 tillage comparison
project that was supported by the San Joaquin Valley wide Air Pollution Study Agency to follow-
up the 2007 RARE project. The lidar system, when coupled with point-measurement air quality
samplers, can map PM emissions at high spatial and temporal resolutions, allowing for accurate
comparisons of various CMPs for a variety of agricultural practices [8]. The purpose of this
project was to deploy an elastic lidar system, together with a network of air samplers, to measure
PM emissions from agricultural operations in order to answer the following research questions:
1. What are the magnitude, flux, and transport of PM emissions produced by agricultural
practices for row crops where tillage CMPs are implemented vs. the magnitude, flux, and
transport of PM emissions produced by agricultural practices where CMPs are not
implemented?
2. What are the control efficiencies of implementing the "conservation tillage" CMP?
3. Can this CMP for a specific crop be quantitatively compared, controlling for soil type,
soil moisture, and meteorological conditions?
In November 2008, EPA redesignated the San Joaquin Valley to attainment for the
NAAQS, but sources in the Valley must continue to implement measures that helped the District
attain the PMio standard, including CMPs. The Valley continues to violate the PM2.s NAAQS.
The CMP chosen for comparison against the conventional tillage method was conservation
tillage, which is defined as a method in which the soil is being tilled or cultivated to a lesser
extent compared to a conventional system [9]. There are several different conservation tillage
systems that vary in the amount of the soil tilled; the system chosen for examination in this study
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was the strip-till system. Data collected May 17 - June 25, 2008 at a site in the San Joaquin
Valley of California are included in this report.
1.1 LITERATURE REVIEW
A handful of published articles pertain to PM emissions from agricultural tillage, with the
majority of the studies performed in the State of California. There are also several papers that
collectively examine impacts of a variety of conservation tillage practices with respect to soil
characteristics, fuel consumption, cost of production, and air emissions.
The companion study to this experiment was conducted in October 2007 near Los Banos, CA
[7]. It examined differences between conventional fall tillage practices and a Combined
Operations CMP. This involved using an implement designed to do the work of multiple
implements in one pass called the Optimizer1 on two fields that were adjacent and similar to one
another. The conventional method consisted of four different passes over the field in the
following order: 1) disc pass, 2) chisel pass, 3) another disc pass, and 4) land plane pass. The
CMP was applied in two steps in the following order: 1) chisel pass, and 2) the Optimizer pass.
This study found that the CMP used 51% less fuel per unit area and took 62% less time per unit
area. Using a lidar mass balance approach, it was determined that the CMP produced 71%, 40%,
and 76% as much PM2.s, PMio, and total suspended paniculate (TSP) as the conventional
method. Table 1 presents the lidar-based PM2.5, PMio, and TSP emissions rates for each tillage
step. Comparisons using inverse modeling coupled with PM measurements were not complete
due to insufficient differences between upwind and downwind PM2.5 concentrations during some
measurement periods; however, operation-specific emission rates calculated using inverse
modeling were similar to those reported by the calibrated lidar measurements.
Table 1. Emission rates ± 95% confidence intervals found by SDL [7] using lidar measurements for a
conventional fall tillage sequence and a Combined Operations CMP sequence.
Operation
Combined: Chisel
Combined: Optimizer
Sum for Combined
Operation Method
PM25
(mg/m2)
45. 3 ± 13.1
32.5 ±5.1
77.8 ± 14.0
PM10
(mg/m2)
69.0 ± 19.9
42.7 ±6.6
111.6 ±20.9
TSP
(mg/m2)
265.9 ±76.6
169.9 ±26.2
435.8 ± 80.9
Conventional: Disc 1
Conventional: Chisel
Conventional: Disc 2
Conventional: Land plane
Sum for Conventional
Method
20.4 ±2.6
35.8 ±5.9
39.5 ±11.2
13.8 ±3.9
109.5 ± 13.5
99.7 ± 12.5
79.5 ± 13.1
80.7 ± 20.5
21. 9 ±6.2
281.9 ± 28.0
159.8 ±20.0
235.1 ±38.8
149.3 ±40.3
33.4 ±9.4
577.6 ± 60.1
The use of an elastic lidar system by the University of California at Davis (UC Davis) to
examine dust plumes resulting from tillage activities was presented by Holmen et al.[8].
Qualitatively, the constructed system was able to track the plume emitted from the moving
source and provide a 2D vertical, downwind map of the plume. It was observed that the plume
1 Mention of a specific tradename or manufacturer does not imply endorsement or preferential treatment by the
USD A-ARS or Space Dynamics Laboratory or Utah State University
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heights were often above the point samplers located at 10 m along the downwind plane. The
authors suggested that the best fugitive dust sampling procedures would include a combination
of elastic lidar and strategically placed point samplers.
Two papers by Holmen et al., both published in 2001, further discuss tillage PMio emission rate
investigations by UC Davis using filter-based mass concentration samplers and qualitative
measurements from the previously mentioned elastic lidar system [10][11]. The 24 samples
labeled as valid were collected from Fall 1996 to Winter 1998 in the San Joaquin Valley during a
wide range of environmental (temperature 7-35 °C, relative humidity 20-90%, and from
prior to the season's first precipitation to periods between winter storms) and soil moisture
conditions (1.5-20%). Tillage operations examined were discing, listing, root cutting, and
ripping. Calculated PMio emission rates ranged from 0 to 800 mg/m (0 to 6.9 Ib/acre), the mean
± one standard deviation was 152 ± 240 mg/m (1.4 ±2.1 Ib/acre), and the median was 43 mg/m
(0.4 Ib/acre). One point made by Holmen et al. [11] is that several environmental conditions
(e.g., temperature profile, relative humidity, soil moisture) can significantly affect PM emissions
and should be monitored and accounted for in emission rate measurement and reporting. As a
result, the reliability of direct comparisons of emission rates measured under different
environmental conditions must be carefully examined.
The studies published by researchers at UC Davis and herein previously discussed were part of a
much larger investigation of agricultural PMio emission rates in the San Joaquin Valley as
funded by the U.S. Department of Agriculture Special Research Grant Program. Findings of this
broad study are published in Flocchini et al. (2001) [12]. Table 2 presents emission factors
published in this study for different types of agricultural tillage along with the crop and time of
year. As seen in results measured by Holmen et al., the emission rates reported by Flocchini et al.
for agricultural tillage were influenced more by environmental conditions, such as the near-
ground temperature profile, relative humidity, and soil moisture, than by the type of crop or
equipment used for tilling [11][12].
The California Air Resources Board (CARB) developed area source emission inventory
calculation methodologies for agricultural tillage and harvesting operations based on the report
by Flocchini et al. [12][13][14]. A summary of the resulting emission factors appears in Table 3.
The given unit for the emission factor can be explained as the mass of PMio particles released
per acre for each operation, or pass. Specific tillage operations were assigned to one of these five
categories and the displayed emission factor was used for all operations in each category.
The U.S. EPA (2001) uses the empirically derived equation shown below to estimate the quantity
of particulate matter emitted from all agricultural tilling processes [15].
E cExkxs°'6 xpxa (1)
where E PM emission in Ibs, CE constant 4.8 Ib/acre-pass, k dimensionless particle size
multiplier (TSP = 1.0, PMio 0.21, PM2.5 0.042), s silt content of surface soil (%),/? =
number of passes or tillage operations in a year, and a acres of land tilled. The above equation
was developed to estimate TSP emissions (k 1.0) and has since been scaled to estimate PMio
and PM2.5 emissions by using the respective k value. Average values of s are tabulated in Table
4.8-6 in U.S. EPA (2001) as a function of soil type on the soil texture classification triangle [15].
Based on the k values, PM released by tillage operations is dominated by large particles (> 10
jam).
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Table 2. Emission factors and uncertainties for land preparation as reported by Flocchini et al. (2001) [12].
Date
Emission Factor
(mg/m2)
Uncertainty
Date
Emission Factor
(mg/m2)
Uncertainty
Stubble Disc
10/27/1995
11/3/1995
11/3/1995
11/3/1995
11/3/1995
11/3/1995
11/3/1995
11/15/1995
11/15/1995
6/24/1997
257.7
49.3
27.4
231.0
136.7
140.8
286.1
537.9
542.2
430.0
NC
9%
470%
4%
7%
6%
5%
9%
125%
17%
11/6/1998
11/6/1998
11/6/1998
11/6/1998
11/6/1998
11/6/1998
11/6/1998
11/6/1998
11/6/1998
50.0
28.4
35.0
28.0
117.0
32.4
58.9
93.5
74.2
146%
145%
NC
10%
18%
9%
8%
9%
8%
Finish disc
11/26/1996
11/26/1996
11/26/1996
12/2/1996
124.3
142.4
97.5
91.0
3%
4%
5%
9%
12/4/1996
12/4/1996
12/4/1996
12/5/1996
9.2
0.6
3.5
-0.5
NC
NC
NC
NC
Ripping/chisel
6/24/1997
6/26/1997
6/26/1997
765.0
112.0
776.0
5%
5%
3%
6/25/1997
6/25/1997
331.0
577.0
5%
6%
Root cutting
11/16/1996
30.0
12%
11/16/1996
36.0
8%
Table 3. Emission factors used by the California Air Resources Board in estimating agricultural tilling PM10
emissions [13].
Agricultural Tilling Operation
Root cutting
Discing, Tilling, Chiseling
Ripping, Subsoiling
Land Planing Floating
Weeding
Emission Factor
(Ib PM10/acre-pass)
0.3
1.2
4.6
12.5
0.8
(mg PM10/m2-pass)
33.6
134.5
515.6
1401.0
89.7
A comparison between standard tilling practices and conservation tilling (strip-till) in dairy
forage production on two farms in the San Joaquin Valley is given by Madden et al. (2008) [16].
Both strip-till and standard till operations were monitored for PMio emissions over two tillage
cycles at both farms. Results show that conservation tillage practices reduce PMio emissions
from one farm by 86% and 52% for 2004 and 2005, respectively. At the second farm,
conservation tillage emissions were reduced by 85% and 93% for 2004 and 2005, respectively.
Derived emission rates are presented in Table 4. Madden et al. attribute these reductions, in part,
to a reduced total number of passes (from 3-6 passes in standard tillage to 1 pass in conservation
tillage) and the ability for conservation tillage operations to be done under a higher soil moisture
content than standard operations.
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Table 4. Conventional and conservation tillage emission rates reported by Madden et al. (2008) for tillage in a
dairy forage crop rotation [16]. ST= standard tillage method, CT = conservation tillage method.
Season/Year
Spring 2004
Sweet Haven Dairy
Operation
ST: 1st discing
ST: 2nd discing (w/ roller)
ST: 3rd discing (w/roller)
ST: Planting
CT: Strip-tilling
CT: Planting
Avg Emission
Factor (mg/m2)
198
1035
114
103
181
26
Barcellos Farms
Operation
ST: 1st discing
ST: 2nd discing
ST: Listing
ST: Bed discing
ST: Bed mulching
ST: Ring roller
ST: Planting
ST: Ring roller
CT: Planting
Avg Emission
Factor (mg/m2)
259
917
615
25
89
566
96
104
394
Spring 2005
ST: 1st discing
ST: 2nd discing (w/ roller)
ST: 3rd discing (w/roller)
ST: Planting
CT: Strip-tilling
CT: Planting
139
375
404
263
180
385
ST: 1st discing
ST: 2nd discing
ST: Circle narrow
ST: Listing
ST: Bed discing
ST: Bed mulching
ST: Planting
CT: Planting
51
123
337
466
109
384
481
130
Conventional tillage emissions factors were reported by Wang et al. (2010) and Kasumba et al.
(2011) from different field experiments conducted in cotton fields on the same farm in New
Mexico [17][18]. Kasumba et al. 2011 reported emission rates from six different discing
operation tests, resulting in averages of 154.6 ± 6.9 mg/m2, 78.3 ± 2.9 mg/m2, 238.7 ± 8.8
mg/m2, 89.2 ± 3.7 mg/m2, 8.4 ± 2.5 mg/m2, and 68.4 ± 2.3 mg/m2 where the uncertainty
represents the standard deviation (o) about the average. Wang et al. reported operation average
emissions ± la of 12.1 ± 9.7 mg/m2 for a rolling operation, 210.7 ± 115.3 mg/m2 for listing,
176.7 ± 77.0 mg/m2 for planting, 10.4 ± 6.5 mg/m2 for harvesting, and 44.8 ± 29.3 mg/m2 and
202.8 ± 79.8 mg/m for discing operations during different years. Wang et al. also developed an
empirical relationship relating emissions to soil moisture, silt fraction, crop type, and operation.
Dust concentrations produced by agricultural implements used at a UC Davis research farm were
reported by Clausnitzer and Singer (1996) [19]. Personal exposure samplers measuring respirable
dust (RD) concentrations—particles that may reach the alveolar region of the lungs when
breathed in (with a 50% cut-point diameter of 4 (im)--were mounted on implements in 22
different operations over a seven month period in 1994; replicate samples were collected during
18 operations. Average RD concentrations measured on the implement ranged from 0.33 mg/m3
for discing corn stubble to 10.3 mg/m3 for both land planing and ripping operations. While RD
concentration was heavily influenced by operation, other factors determined to be significant in
dust production were relative humidity, air temperature, soil moisture, wind speed, and tractor
speed.
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Further investigation of the data set presented by Clausnitzer and Singer (1996) [19] and of
another data set collected on a different UC Davis research farm was reported by Clausnitzer and
Singer (2000) [20]. Both sets of data focus on RD concentrations as measured on the agricultural
implement. The analysis examined environmental influences on the measured concentration.
Again, soil moisture and air temperature were found to be significant factors in RD production.
The RD production with respect to soil moisture was well fit by a power function, with the curve
predicting RD concentrations becoming significantly steeper below 5%. Air temperature was
hypothesized to be significant in that it was a surrogate measurement of atmospheric instability:
as temperature increases near the surface, the atmosphere becomes less stable and may carry
greater quantities of dust upwards.
Baker et al. (2005) examined differences between dust concentrations resulting from standard
and conservation tillage practices in the San Joaquin Valley over a two year cotton/tomato crop
rotation, each under two different cover crop scenarios: 1) no cover crop and 2) a cover crop
forage mixture [21]. Total dust (TD), particles < 100 jim in aerodynamic diameter, and RD
samplers were stationed on the implements to collect samples in the plume. For both TD and RD,
the presence or lack of a cover crop in the standard till treatment did not seem to affect
concentrations. Summed concentrations for conservation tillage without a cover crop were about
one-third of standard tillage; for conservation tillage with a cover crop, they were about two-
thirds for both dust fractions measured. Reductions in summed concentrations with conservation
tillage were attributed to fewer operations, including the elimination of the dustiest (discing and
power incorporation). When comparing operations common to all four treatments, tomato
planting and harvesting in conservation till produced higher concentrations than standard till
(thought to be due to increased organic matter on the surface) and concentrations during cotton
harvesting, which does not disturb the soil, were equivalent for all treatments. This study was
part of a larger effort to quantify the effects of conservation tillage in California on crop
production, soil quality, and time and resources dedicated to production as outlined by Mitchell
et al. (2008) and Veenstra et al. (2006) [22][23].
Upadhyaya et al. (2001) compared the Incorpramaster, a one-pass tillage instrument against a
conventional combination of discing twice and land planing twice based on fuel consumption,
timeliness, and effect on soil [24]. Studies on four experimental fields at UC Davis showed no
statistical difference between resulting soil conditions (bulk density changes, soil moisture
changes, and aggregate size), but the Incorpramaster used between 19% and 81% less fuel with a
mean of 50%. The time savings ranged from 67% to 83% with a mean of 72%. In most cases,
two passes with the Incorpramaster were required to achieve the same soil conditions as the four
passes in the conventional till.
Three conservation tillage methods were compared in Mitchell et al. (2006) against the standard
tillage method in cotton production in terms of yield, yield quality, tractor passes, fuel, and
production [25]. A single field near Fresno, CA was divided in area among seven tillage
treatments: 1) standard, 2) no till/chop, 3) no till, 4) ridge till/chop, 5) ridge till, 6) strip-till/chop,
and 7) strip-till. Prior to both cotton growing seasons examined, a small grain wheat was planted
in the field to enhance soil properties; this crop was sprayed with herbicide, and in treatments 2,
4, and 6, it was chopped with a mower prior to tillage activities. In the other treatments (1, 3, 5,
and 7), the dead wheat was either incorporated by the tilling or left standing. Yield and yield
quality were statistically the same for both years for all treatments, though the standard treatment
was numerically higher in both yield and yield quality the 2nd year. Conservation tillage
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treatments reduced tractor passes by 41% to 53% over the standard method and estimated fuel
reduction was 48% to 62% for the conservation practices. The estimated overall production costs
of the conservation tillage systems were 14% to 18% lower than the conventional system.
Mitchell et al. estimated, by extrapolation from other work, that whole-tillage process paniculate
matter emissions would also be decreased.
Particulate matter is released during agricultural tillage activities from both the operational
activity of the tillage implement, as well as the tractor in use. Emissions from the tractor mainly
originate from the tires kicking up dust from the soil and the combustion engine. Attempts to
quantify the PM emitted in agricultural tractor exhaust have been made by the U.S. EPA and the
CARB in software designed to estimate off-road engine emissions on a county or regional scale.
The U.S. EPA developed the NONROAD software program, with the latest version distributed in
2005 [26]. Emission factors from compression ignition (diesel) engines used in the NONROAD
model are calculated by adjusting a zero-hour (ZH), steady-state measured emission factor (EFSS)
for engine deterioration with operation time (DF) and a transient adjustment factor (TAP) that
accounts for variations from steady-state engine loading and speed, as shown in the following
equation [27].
EFadj(PM) EFssxTAFxDF-SPMadj (2)
where EFadj(pM) is the adjusted PM emission factor and SpMadj is the emission factor adjustment
accounting for the use of a diesel fuel with a sulfur content different than the default
concentrations; fuel sulfur level is known to affect PM emissions. The units for EFadj(pM) , EFSS,
and SpMadj are g/hp-hr, where hp stands for horsepower, and TAP and DF are both unitless. All
four variables on the right side of Eq. 2 vary with model year and engine size, expressed in
horsepower (hp), according to measured values and/or the emission standards each model year
and engine size was designed to meet. The selection of values for steady-state emission factors
and all the adjustment variables given in U.S. EPA (2004) [27] was performed using a variety of
tests and resources, including the Nonroad Engine and Vehicle Emission Study (NEVES) Report
[28], or by setting the values such that the adjusted PM emissions were equal to model year-
specific emission standards.
The CARB has also developed a model to forecast and backcast daily exhaust emissions from
off-road engines, including agricultural tractors, herein called OFFROAD. Similarly to the U.S.
EPA's NONROAD model, emission factors (EF) for each engine size and model year are
calculated based on a ZH emission rate with a deterioration factor (dr) applied to account for
engine wear with use, as in Eq. 3. The derived emission factor is then multiplied by the load
factor (Load), the maximum rated average horsepower (AvgHp), and the amount of time the
engine is active through the year (Activity) in hr/yr.
EF ZH+dr*CHrs (3)
Emissions EF* AvgHp* Load* Activity *CF (4)
where CHrs is the cumulative engine operation hours, Emissions is the amount of pollutant
released in tons/day, and CF is the conversion factor from units of grams per year to tons per day
and has a value of 3.02 x 10"9. The values for the EF and DR are derived from measured values
or they are calculated based on requirements to meet the proposed emissions limits for future
years [29].
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Kean et al. (2000) estimated off-road diesel engine, locomotive, and marine vehicle emissions of
NOX and PMio for 1996 based on fuel sales [30]. Diesel engine exhaust emission factors were
developed based on information provided in the development of the U.S. EPA NONROAD off-
road vehicle emissions model with supplemental information in order to calculate emissions
based on fuel consumption. A fleet-wide average PMio emission factor was determined for farm
diesel equipment to be 3.8 g/kg of fuel used, at an average mass per volume of 0.85 kg/L of
diesel fuel. Fuel sales surveys from 1996 were used to calculate regional and national emissions.
In the off-road category, which includes farm equipment, the U.S. EPA NONROAD model
calculated on average 2.3 times higher emissions, which was attributed to higher engine activity
assumed in the EPA model than represented in the reported fuel sales data.
An uncertainty analysis was conducted to determine the statistics of the preceding PMio
emissions factors reported from measurements. This analysis was performed following the
emissions factor dataset analysis technique used by RTI International in "Emission Factor
Uncertainty Assessment, Review Draft" [31]. Data points were categorized according to the
following tillage operations, with the number of values given in parenthesis: chisel (2), disc (67),
land planing (1), listing (8), ripping (5), root cutting (3), standard tillage planting (11), strip-till
planting (9), strip-tilling (6), and weeding (15). In cases where a report/paper only provided an
average emissions factor, the average value was used only once. RTI International found that two
parametric models, the lognormal and Weibull distributions, best fit the analyzed datasets. These
same two parametric models were fitted to the tillage operation datasets with eight or more data
points. The estimated parameters for these models and the corresponding goodness-of-fit to the
available data points, expressed as the root mean square error (RMSE), are given in Table 5 with
the better fit model values in bold. Note that a smaller RMSE represents a better fit to the data.
The Weibull distribution proved to be a better fit for four out of the five examined datasets. The
fits to the disc and weeding datasets were better than that for the remaining three, both visually
and based on the RMSE values. Figure 1 presents the histogram for the disc operation dataset
and cumulative density functions developed from the data and the Weibull distribution fit to the
data. The mean and median values of the fitted Weibull distribution are shown on the cumulative
density function line, along with the emissions factor value given by ARE for a disc operation.
The ARB emissions factor of 134.5 mg/m2 is very close to the Weibull distribution median of
136.3 mg/m . The emissions factor values corresponding to the 5%, 25%, median, 75%, and 95%
levels along the cumulative distribution curve, as well as the average emissions factor, were
calculated for the five operations and are presented in Table 6. The 95% level emissions factors
for the three operations with poorer fits (i.e., higher RMSE) seem very high; this is likely an
artifact of fitting the distributions to a limited number of data points. In this analysis, the better
fits were obtained for datasets with n > 15.
SDL/08-556 California 2008 Tillage Campaign 10
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Table 5. Estimated distribution parameters and goodness-of-fit, as root mean square error (RMSE), for the
lognormal and Weibull distributions fit to tillage operation datasets with > 8 data points. Values in bold
represent model with a better fit.
Operation
Disc
Weeding
Standard-till Planting
Strip-till Planting
Listing
n
67
15
11
9
8
Lognormal
Mean
4.61
3.90
3.92
3.21
4.21
G
2.18
1.22
4.45
4.99
5.44
RMSE
0.10
0.06
0.19
0.17
0.23
Weibull
Scale
214.1
89.3
197.0
143.3
366.0
Shape
0.82
0.91
0.58
0.43
0.50
RMSE
0.04
0.07
0.13
0.15
0.22
Empirical CDF
Weibull CDF
500
1000
1500
EF (mg rrT2)
EF (mg rrT2)
Figure 1. Histogram of disc operation emissions factors and both the empirical and the Weibull cumulative
distribution functions (CDFs).
Table 6. Emissions factor values corresponding to statistical measures of interest for the distributions fitted to
the various tillage operation datasets.
Operation
Disc
Weeding
Standard-till Planting
Strip-till Planting
Listing
Emissions Factor (mg/m )
5%
5.5
6.5
1.2
0.2
1.0
25%
46.1
21.6
23.4
7.9
30.7
Median
136.3
49.2
105.2
61.0
176.6
Mean
240.6
104.1
306.8
396.0
724.8
75%
320.1
112.4
344.4
306.5
701.0
95%
827.1
368.5
1,286.6
1,843.4
3,246.4
SDL/08-556
California 2008 Tillage Campaign
11
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2. EXPERIMENT DESIGN
2.1 SITE DESCRIPTION
After discussion among stakeholders, regulatory agencies, and researchers, it was collectively
determined that the appropriate type of tillage for this experiment would be the tillage sequence
following the harvest of a winter wheat crop in preparation for planting of corn. An appropriate
site was identified in the San Joaquin Valley of California (see Figure 2) consisting of two
adjacent fields. Both fields were cultivated in winter wheat in late 2007 and were to be planted in
corn for the 2008 summer growing season. The focus of this study was comparing emissions
resulting from the tillage operations in each field. Therefore, no data were collected to make
comparisons of other potentially varying characteristics, such as crop yield, soil organic matter,
etc., between the two management practices. Tillage management practices are often crop
specific and are not appropriate for use in all crop production activities. The effectiveness of
CMPs used in other crop systems at reducing PM emissions should be investigated.
An aerial photograph and soil classification map of the experimental site is shown in Figure 3.
These data were obtained from the Web Soil Survey (WSS), a website maintained by the USDA
Natural Resources Conservation Service (NRCS) [32]. The photo was slightly modified from its
original format to reflect current land use, to delineate and label study fields, and to make soil
type boundaries and labels more visible. The study fields are outlined and labeled in yellow. The
soil type boundaries and labels are in white. The two fields that were used for this experiment are
labeled as Field 4 and Field 5. The fields are referred to by these names throughout this
experiment and reflect the designation given to them by the landowner. Both Field 4 and Field 5
are rectangular in shape, contain an area of approximately 25 acres and are completely
dominated by soil type 130 (Kimberlina fine sandy loam, saline-alkali).
The area immediately surrounding the experiment fields is dominated by agricultural practices.
Field 4 has adjacent cultivated cropland on three sides and the fourth side is bounded by an
active dairy. Field 5 has adjacent cultivated cropland on two and a half sides and the other side
and a half are adjacent to active dairies. Both fields are surrounded on all sides by roads. These
roads, with the exception of one, are dirt roads used for field access by farm machinery. The
paved road is a heavily travelled, two-lane asphalt road that was downwind of the fields during
all measurements. Additionally, railroad tracks are located to the north of this site and two to
three trains pass by per day with varying numbers of cars. The crops observed in the area at the
time of the experiment were grain, corn, almond orchards and grape vineyards.
The terrain surrounding the fields was relatively flat in all directions. Figure 4 is a photo taken on
the southern edge of Field 5 looking across Field 5 and Field 4. The main form of topographical
terrain relief was provided by the drainage and irrigation ditches and canals.
2.2 OPERATION DESCRIPTION
The purpose of this field study was to measure and compare the quantity of particulate emissions
between the conventional method (of spring tillage between a winter wheat crop and a summer
crop of feed corn) and a Conservation Tillage CMP. As described in the Conservation
Management Practices Program Report (2006), the conservation tillage CMP "involves using a
system in which the soil is being tilled or cultivated to a lesser extent compared to a conventional
system" and it is "intended to reduce primary soil disturbance operation such as plowing,
discing, ripping, and chiseling".
SDL/08-556 California 2008 Tillage Campaign 12
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t KM 100 Ml«
Figure 2. Shaded relief map of the State of California, USA, with the location of the selected sample site
shown by the white star. Image from geology.com [33].
The Conservation Tillage CMP under study is a strip-till method which combines multiple
operations to reduce the number of passes required and disturbs the soil only in 8"-wide strips
centered every 30" instead of disturbing the entire surface like the plowing, discing, and listing
operations of a conventional method. This strip-till method therefore reduces the tilled surface by
up to 75% while leaving a lot of the wheat stubble as ground cover in addition to reducing the
number of passes. The implement used in this study was the Orthman 1-tRIPr, shown in Figure
5. The level of precision and repeatability required by the strip-till method makes the use of
accurate GPS systems a necessity. The cooperating farm has been using the Orthman 1-tRTPr for
seedbed preparation on all of its fields for several years with the exception of Field 4 which has
continued to be prepared by conventional tillage methods.
SDL/08-556
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13
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Figure 3. Satellite image of the study location with soil types shown in white. The study fields are outlined
within the two rectangles. Soil type 130 represents Kimberlina fine sandy loam, saline-alkali [34].
The conservation tillage CMP applied in this study consisted of three operations totaling three
passes across the field, with all three being measured in separate sample periods. In comparison,
the conventional tillage method as applied here had nine different operations totaling 13 passes,
excluding the building and removal of a ditch and in-field borders, with 12 of them being
measured in nine sample periods.
The conventional tillage method was employed in Field 4, and the conservation tillage CMP was
used in Field 5. The operations that were performed in each method are shown in order in Table
7, with their corresponding dates. In both the CMP and conventional tillage sequences, ditches
and field-edge borders were built and then broken down between 5/20/2008 and 6/5/2008 to
allow for flood irrigation of the field prior to planting. Drainage ditches on the east side of both
fields returned excess water to the wastewater lagoon of the adjacent dairy. As the ditch and
field-edge border construction and removal were not measured in Field 5, the corresponding step
for the conventional tillage method was not considered in the total emissions per method. Also,
the in-field borders in Field 5 were not broken down and smoothed out, but instead were used for
the summer corn crop. The term in-field borders as used here applies to low ridges of soil that
separate the field into smaller areas for flood irrigation. Prior to any spring tillage activities, both
Fields 4 and 5 had in-field borders running in roughly an east/west direction, with the irrigation
water sources located on the west side of the fields. In Field 5 the borders were not broken down,
but in Field 4 they were removed and the irrigation water moved from west to east between the
ridges in which the corn was planted.
SDL/08-556
California 2008 Tillage Campaign
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Figure 4. Photo taken standing near the southern edge of Field 5 looking north across Field 5 towards Field 4.
Note the relative flatness of the terrain.
The cultivator passes in the conventional tillage sequence function as mechanical weed control,
whereas a chemical weed control (herbicide) is used in the CMP sequence. Additionally, the
cultivator pass 4 was carried out the day after the cultivator pass 3, but unmovable schedules
prevented its measurement. It is assumed, in calculating the total PM emissions, that the
emission rates of both passes 3 and 4 were equal. In general, two cultivator passes are done right
after the other in opposite directions down the rows to ensure adequate weed control.
The tractors and implements used during all the tillage operations are listed in Table 8 by date
and operation. In cases where multiple tractors and implements were used, they are listed in the
order of use. A single, handheld GPS unit was used to actively log the tractors' positions over
time for each run. During the first part of the lister operation, unharvested plant material along
the border lines caused clogging of the lister blades, decreasing its effectiveness. A second
tractor with the disc set was brought in to go over the border lines again to further reduce the size
of residual plant material. Note that the cultivator passes 1 and 2 and the roller pass were not
finished when the planter arrived to begin planting Field 4. Therefore, the first sample period on
6/5/2008 was stopped and the second sample period was started shortly thereafter with the
cultivator and roller still operating in the north end of the field - the implement locations in the
north end of the field and meteorological conditions likely prevented significant impacts from
the cultivator and roller operations on downwind samples.
Field personnel observed operations continually and recorded notes on tractor operation times,
potential contamination issues due to traffic on surrounding dirt roads and wind-blown dust,
general meteorological observations, etc.
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California 2008 Tillage Campaign
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Figure 5. Orthman 1-tRJPr in operation during this field experiment.
Table 7. Tillage operations and dates performed for the comparison study.
Sequence
Operation
Date
Conservation Tillage (Field 5)
1
2
o
J
Strip-Till
Plant and Fertilize
Herbicide Application
5/17/2008
6/7/2008
6/11/2008
Conventional Tillage (Field 4)
1
2
o
J
4
5
6
7
8
9
10
Break down in-field irrigation borders
Chisel
Disc 1
Disc 2
Lister
Build up ditch and field-edge borders
Break down ditch and field-edge borders,
Cultivator passes 1 and 2, and Roller
Plant
Fertilize
Cultivator pass 3
5/17/2008
5/18/2008
5/19/2008
5/19/2008
5/20/2008
5/20/2008
6/5/2008
6/5/2008
6/18/2008
6/25/2008
SDL/08-556
California 2008 Tillage Campaign
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Table 8. Agricultural equipment used to perform the tillage operations.
Operation
Strip-till
Break down in-field
borders
Chisel
Disc 1
Disc 2
Lister
Building borders and
ditches
Break borders and
ditches, cultivate,
and roll
Planting
Planting
Herbicide
Application
Fertilizer
Application
Cultivator
Field
#*
5
4
4
4
4
4
4
4
4
5
5
4
4
Date
5/17/08
5/17/08
5/18/08
5/19/08
5/19/08
5/20/08
5/20/08
6/5/08
6/5/08
6/7/08
6/11/08
6/18/08
6/25/08
Tractor
Case MX255
Case Puma 195
Case MX255
Case Puma 195
Case Puma 195
1) Case MX255
2) Case Puma 195
1) Kubota
M8030DT
2) Case 870
1) Kubota
M8030DT
2) Case 870
3) Case Puma 195
4) Case 2290
1) Case Puma 195
2) Case 2290
3) John Deere 4055
Case MX255
Kubota B-Series
Case 2290
Case 1370
Implement (1 per tractor)
Orthman 1-tRIPr, 6 row, 30 in. spacing
Custom border buster (2 sets of 3 discs that move
soil from center to edges)
Custom chisel, 13 ft. wide, 22 in. depth, w/ edged
roller
International Offset Disc, 19 ft. wide, pulling a
single axle (2 smooth road tires), pulling a 19 ft.
wide spiked roller
International Offset Disc, 19 ft. wide, pulling a
single axle (2 smooth road tires), pulling a 19 ft.
wide spiked roller
Custom lister, 6 row, 38 in. spacing
International Offset Disc, 19 ft. wide
Custom 1-way ditch digger (2 sets of 3 discs that
move soil toward center)
Custom border ridger (2 sets of 3 discs that move
soil from center to edges)
Custom 1-way disc (1 set of 3 discs that move soil
from one side to the other)
Custom border buster (2 sets of 3 discs that move
soil from center to edges)
Lilliston Rolling Cultivator, 6 rows wide, 38 in.
spacing
Flat roller, 6 rows wide
Lilliston Rolling Cultivator, 6 rows wide, 38 in.
spacing
Flat roller, 6 rows wide
John Deere MaxEmerge 2 Row Planter, single
row, 6 rows wide, 38 in. spacing
Monosem Twin-Row Planter Model 6x2, 6 rows,
30 in. spacing
Hardi ATV Sprayer, 40 ft. boom
Custom side-dress fertilizer, 6 rows wide, 38 in.
spacing, pulling a fertilizer tank (1 axle, 2 small
smooth tractor tires)
Lilliston Rolling Cultivator, 6 rows wide, 38 in.
spacing
* Note: Field 4 = conventional tillage practice, Field 5 = conservation tillage CMP
SDL/08-556
California 2008 Tillage Campaign
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2.3 TILLAGE OPERATION DATA
Based on field notes and GPS data points, the total tractor run time in tractor hours (hrtractor), or
the sum of individual tractor operation times, and the total area tilled per day were calculated.
This tractor run time only includes times when the tractor was moving and performing the
specified operation in the field. The tractor hours we report therefore do not include time the
tractor spent motionless in the field with or without an idling engine. Operation delays observed
were refueling, implement adjustments, changing equipment, and corrections to the on-board
GPS system. The tillage rate (hectares/hrfractor) and the operation rate of the tractors
(hrtractor/hectares) were calculated from the tractor run time and total area tilled. While both fields
were 25 acres (10.1 hectares) in area, measurement equipment had to be placed inside of the field
edge in Field 5 used for conservation tillage, reducing the monitored area to about 22.4 acres
(9.05 hectares). The tractor operation rates were summed to provide the total amount of time per
hectare spent preparing the ground for the next crop. All of these data are presented in Table 9.
In this study, the tillage rate of the conservation tillage operation was 0.86 hrtractor/hectare and the
conventional tillage rate was about five times that amount at 4.90
Table 9. Operation data for both the conventional and conservation tillage studies as recorded by field
personnel.
Operation
Date
Total
Tractor Time
(hrtractor)
Total Area
worked
(hectares)
Tillage Rate
(hectares/hrtractor)
Tractor Rate
(hrtractor/hectare)
Conservation Tillage (study area = 9. 05 hectares)
Strip till
Plant
Herbicide application
5/17
6/7
6/11
3.05
3.82
0.93
9.05
9.05
9.05
2.97
2.37
9.73
Sum
0.337
0.422
0.103
0.862
Conventional Tillage (study area = 10.05 hectares)
Break down borders
Chisel
Disc 1
Disc 2
Lister
Build ditches
Break down ditches, Cultivator
passes 1 and 2, & Roller
Plant
Fertilizer application
Cultivator pass 3
5/17
5/18
5/19
5/19
5/20
5/20
6/5
6/5
6/18
6/25
0.92
6.18
4.83
4.73
5.07
0.83
7.43
3.82
1.08
4.02
2.02
8.51
10.05
10.05
12.48
0.77
23.86
13.21
3.81
10.05
2.20
1.38
2.08
2.12
2.46
0.93
3.21
3.46
3.53
2.50
Sum
0.455
0.726
0.481
0.471
0.406
1.078
0.311
0.289
0.283
0.400
4.90
Due to the breaks in tractor run time and variation in the presence of other tractors, the ratio of
the sample period length and total tractor operation time was slightly different for each run, as
shown in Table 10 below. The difference between total tractor operation time and sample period
time is important because the source strength also varies based on how many tractors, if any, are
operating at a given time. All calculations of emission rates herein have accounted for these
differences in source strength with time, with final emission rates based on time reported as the
emission rate of a single tractor.
SDL/08-556
California 2008 Tillage Campaign
18
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Table 10. Sample period, total tractor operation time, and the sample period-to-tractor operation time ratio
for all sample periods.
Sample
5/17 Run 1
5/17 Run 2
5/18
5/19 Run 1
5/19 Run 2
5/20 Run 1
5/20 Run 2
6/5 Run 1
6/5 Run 2
6/7
6/11
6/18
6/25
Sample Time (hr)
3.92
0.92
6.58
4.92
5.25
3.83
1.07
7.25
2.00
5.33
1.58
2.17
4.25
Total Tractor Time (hr)
3.05
0.92
6.18
4.83
4.73
5.07
0.83
7.43
3.82
3.82
0.93
1.08
4.02
Sample time/Tractor time
1.29
1.00
1.06
1.02
1.11
0.76
1.29
0.98
0.52
1.40
1.70
2.00
1.06
SDL/08-556
California 2008 Tillage Campaign
19
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3. MEASUREMENTS AND METHODS
3.1 MEASUREMENT OVERVIEW
A wide variety of air quality and meteorological sampling equipment was employed during this
field study. These instruments are described in the following sections along with their functions,
data analysis procedures, and calibration verification procedures which were performed before,
during, and after the campaign to ensure the accuracy of data collected.
As wind direction and wind speed are important factors in obtaining accurate and representative
data from the tillage operations, it was necessary to determine the dominant wind direction for
this period of year in the area. Meteorological data from the California Irrigation Management
Information System (CEVIIS) database were downloaded for May and June of 2005 through 2007
for the Stratford station (#15) [35]. Based on these data, the dominant wind direction for the
months of May and June in the area was determined to be from the north to the northwest, as
shown in Figure 6.
,.<• X
sv/
>..
-•-.
"•-, .'
i
''10.0%
/I 5.0%
.'^\ /
E
'25.0%
/30.0%
/
,-'
, SF
, oc:
Figure 6. Wind rose for May and June of 2005 - 2007 as recorded by the CIMIS Station # 15 (Stratford). No
calm periods were recorded.
SDL/08-556
California 2008 Tillage Campaign
20
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Due to the layout of the fields and adjacent dairy, the preferred wind direction for sampling was
from the north to northwest. Assuming a northwest wind, instrument deployment was such that
samplers meant to measure background aerosol parameters were to the west and northwest of the
fields of interest. Samplers meant to measure background plus plume parameters were to the
south and east. Similar sample layouts were arranged around each field using the same
instruments. Some equipment, such as the lidar and air quality trailers, remained in the same
place for samples in both fields while more portable samplers were moved between fields.
The first layout, shown in Figure 7, presents the sample layout for monitoring conventional
tillage practices in Field 4. Table 11 summarizes the types of instruments that were located at
each site and the dates certain instruments were used if they were not used during the entire
study. The greatest deviations in the daily layout occurred during the runs on June 18* and 25*,
which are due to the following situations: 1) a separate study of particulate and gaseous
emissions from the adjacent dairy was carried out from June 13th to the 20th during the break in
tillage operations that required the relocation of most samplers, including both the Aglite lidar
and Air Quality trailers; and 2) a failure in the Aglite lidar system on June 19 prevented the
lidar from being used in subsequent sample periods. The lidar system was located at position L2
throughout the dairy investigation and for the June 18th sample of the fertilizer application in
Field 4.
The sample array arranged to monitor the conservation tillage activities in Field 5 is shown in
Figure 8. Table 12 summarizes the type of instruments that were located at each site. In contrast
to the sampling changes to measure operations in Field 4, there were not significant deviations
from the original sample layout used around this field as all sampling occurred prior to moving
equipment for the dairy emissions study.
3.1.1 Meteorological Measurements
A Vantage Pro2 Plus weather station from Davis Instruments, Inc. (Hayward, CA) was used to
monitor wind speed, wind direction, temperature, humidity, precipitation, barometric pressure,
and solar radiation. It was located on top of the Air Quality sampling trailer, at an approximate
height of 5 m above ground level. It was wired to a datalogger and a display panel inside the
trailer, which was connected to a computer for data storage.
Two 15.2 m towers were instrumented with 3-cup anemometers (model 12102) at five heights
(2.5, 3.9, 6.2, 9.7 and 15.3 m) to measure the vertical wind speed profile. Relative
humidity/temperature sensors (Vaisala HMP45C) from Campbell Scientific, Inc. (Logan, UT)
were also stationed at five heights (1.5, 2.5, 3.9, 6.2, and 9.7 m) to provide profiles of
temperature and relative humidity. A Met One Instruments, Inc. (model 024A, Grants Pass, OR)
Wind Vane was stationed at a height of 15.3 m on each tower. Data from each tower were stored
as one minute averages on the Campbell Scientific CR23X dataloggers and were downloaded
daily.
The eddy covariance (EC) instrumentation was comprised of four Campbell Scientific Inc.
(Logan, UT) 3D sonic anemometers (CSAT) and four LiCOR 7500 infrared gas analyzers
(IRGA). The sensor separation for all four EC systems was 10 cm. All data were stored on to a
Campbell Scientific data logger (CR5000). Together the CSAT and LiCOR measured water
vapor (q) and carbon dioxide (c) concentrations, and velocity components of the wind flow in
three spatial dimensions: x, y, and z. These measurements were made at a scan rate of 20 Hz, 20
measurements per second for w, v, w, q and c. Each EC instrumentation group was visited daily
SDL/08-556 California 2008 Tillage Campaign 21
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between 06:00-07:00 hours for maintenance. Maintenance performed included exchanging
compact flash cards for data storage on the CR5000, sensor interrogation at the data logger
screen to evaluate measurement status, cleaning dust from the surface of the IRGA lens with de-
ionized water, and removing spider webs from the transducer arms.
I
Legend
Met Tower
A Tower
• Tripod
AQ Trailer
°:
11.0
I
I I I
Held 4
onventional Tillage
25 Acres
r
Fields
Conservation Tillage
25 Acres
Meters
400
Figure 7. Sample layout used for Field 4.
Table 11. Summary of instruments located at each position for the conventional tillage study of Field 4. All
heights given as above ground level (agl).
Instrument
Location
Description
Tl
1-10 m tower
1 - OPC: 1 @ 9 m
3 - MiniVols: 3 @ 9 m (TSP, PM10 andPM25)
T2
1-10 m tower
1 - OPC: 1 @ 9 m
3 - MiniVols: 3 @ 9 m (TSP, PM10 and PM25)
WM
1-15 m tower
5 - cup anemometers: 1 each @ 2.5, 3.9, 6.2, 9.7 and 15.3 m
1 - wind vane: 1 @ 15.3 m
5 - temp/RH sensors: 1 each @ 1.5, 2.5, 3.9, 6.2, and 9.7 m
2 - Campbell Scientific dataloggers
1 - sonic anemometers: 1 @ 11.3 m
1 - energy balance systems: 1 @ 2 m
SDL/08-556
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Instrument
Location
EM
LI
L2
EC1
EC2
ECS
2.4
6.4
7.4
8.4
9.4
10.0
11.0
12.0
LARS
AQT
Description
1-15 m tower
5 - cup anemometers: 1 each @ 2.5, 3.9, 6.2, 9.7 and 15.3 m
1 - wind vane: 1 @ 15.3 m
5 - temp/RH sensors: 1 each @ 1.5, 2.5, 3.9, 6.2, and 9.7 m
2 - Campbell Scientific dataloggers
1 - sonic anemometers: 1 @ 11.3 m
1 - energy balance systems: 1 @ 2 m
2 - OPCs: 1 @ 9 m, 1 @ 14.5 m
1 - Lidar data collection system (5/14 - 6/11)
1 - Davis met station for lidar operator's reference
1 - Lidar data collection system (6/18)
1 - Davis met station for lidar operator's reference
1 - Campbell Scientific CSAT @ 1.6 m
l-LiCORIRGA@1.6m
1 - Campbell Scientific CSAT @ 2.9 m
1 - LiCOR IRGA @ 2.9 m
1 - Campbell Scientific CSAT @ 1.6 m
1 - LiCOR IRGA @ 1.6m
1 - 2 m tripod
1 - OPC: 1 @ 2 m
3 - MiniVols: 3 @ 2 m (TSP, PM10 and PM25)
1 - 2 m tripod
2 - MiniVols: 2 @ 2 m (PM10 and PM25)
1 - 2 m tripod
1 - OPC: 1 @ 2 m
2 - MiniVols: 2 @ 2 m (PM10 and PM25)
1 - 2 m tripod
2 - MiniVols: 2 @ 2 m (PM10 and PM25)
1 - 2 m tripod
2 - MiniVols: 2 @ 2 m (PM10 and PM25)
l-2mtripod(5/18-6/ll)
1 - OPC: 1 @ 2 m
3 - MiniVols: 3 @ 2 m (TSP, PM10 and PM25)
l-2mtripod(6/18, 6/25)
1 - OPC: 1 @ 2 m
3 - MiniVols: 3 @ 2 m (TSP, PM10 and PM25)
l-2mtripod(6/25)
1 - OPC: 1 @ 2 m
3 - MiniVols: 3 @ 2 m (TSP, PM10 and PM25)
1 - OPC: 1 @ 2.8 m
1 - OPC: 1 @ 5 m
3 - MiniVols: 3 @ 5 m (TSP, PM10 and PM25)
1 - Davis met station: 1 @ 5 m
1 - OC/EC Analyzer: 1 inlet @ 4.5 m
1 - AMS: 1 inlet @ 4 m
1 - radio and laptop for OPC Data collection
Note: The Air Quality Trailer was in the position indicated on the map for the tillage
practices that occurred from 5/1 7/2008 through 6/11/2008. For the two remaining runs on
6/18/2008 and 6/25/2008 the Air Quality Trailer was in a different location and the
analysis equipment were either not used or located in a different downwind position.
SDL/08-556
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Legend
_ Met Tower
A Tower
• Tripod
• AQ Trailer
• Lidar
Field Outline
Figure 8. Sample layout used for Field 5.
Table 12. Summary of instruments located at each position for the Conservation tillage study of Field 5. All
heights given as above ground level (agl).
Instrument
Location
Tl
T2
T3
WM
Description
1
1
3
1
1
1
1
3
1
5
1
5
2
1
1
-10 m tower
- OPC: 1 @ 9 m
- MiniVols: 3 @ 9 m (TSP, PM10 and PM25)
-10 m tower
- OPC: 1 @ 9 m
-10 m tower
- OPC: 1 @ 9 m
- MiniVols: 3 @ 9 m (TSP, PM10 and PM25)
-15 m tower
- cup anemometers: 1 each @ 2.5, 3.9, 6.2, 9.7 and 15.3 m
- wind vanes: 1 @ 15.3 m
- temp/RH sensors: 1 each @ 1.5, 2.5, 3.9, 6.2, and 9.7 m
- Campbell Scientific dataloggers
- sonic anemometers: 1 @ 11.3 m
- energy balance systems: 1 @ 2 m
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EM
LI
EC1
EC2
ECS
2.5
6.5
7.5
8.5
9.5
10.0
LARS
AQT
1-15 m tower
5 - cup anemometers: 1 each @ 2.5, 3.9, 6.2, 9.7 and 15.3 m
1 - wind vanes: 1 @ 15.3 m
5 - temp/RH sensors: 1 each @ 1.5, 2.5, 3.9, 6.2, and 9.7 m
2 - Campbell Scientific dataloggers
1 - sonic anemometers: 1 @ 11.3 m
1 - energy balance systems: 1 @ 2 m
2 - OPCs: 1 @ 9 m, 1 @ 14.5 m
1 - Lidar data collection system
1 - Davis met station for lidar operator's reference
1 - Campbell Scientific CSAT @ 1.6 m
l-LiCORIRGA@1.6m
1 - Campbell Scientific CSAT @ 2.9 m
1 - LiCOR IRGA @ 2.9 m
1 - Campbell Scientific CSAT @ 1.6 m
1 - LiCOR IRGA @ 1.6m
1 - 2 m tripod
1 - OPC: 1 @ 2 m
3 - MiniVols: 3 @ 2 m (TSP, PM10 and PM25)
1 - 2 m tripod
2 - MiniVols: 2 @ 2 m (PM10 and PM25)
1 - 2 m tripod
1 - OPC: 1 @ 2 m
2 - MiniVols: 2 @ 2 m (PM10 and PM25)
1 - 2 m tripod
2 - MiniVols: 2 @ 2 m (PM10 and PM25)
1 - 2 m tripod
2 - MiniVols: 2 @ 2 m (PM10 and PM25)
l-2mtripod(6/7-6/ll)
1 - OPC: 1 @ 2 m
3 - MiniVols: 3 @ 2 m (TSP, PM10 and PM25)
1 - OPC: 1 @ 2.8 m
2 - OPC: 2 @ 5 m
3 - MiniVols: 3 @ 5 m (TSP, PM10 and PM25)
1 - Davis met station: 1 @ 5 m
1 - OC/EC Analyzer: 1 inlet @ 4.5 m
1 - AMS: 1 inlet @ 4 m
1 - radio and laptop for OPC Data collection
3.1.2 Wind Profile Calculations
Wind profiles near the ground surface were calculated based on one-minute averaged wind speed
data from the logarithmically spaced cup anemometers on the 15m meteorological tower at the
WM location, which was the meteorological tower located upwind/crosswind of both tillage
sites. A power law wind speed profile fit to the tower data was extrapolated up to 250 m to
estimate the wind speed at higher elevations. The power law for calculating wind speed (ui) at
height 22 based on a measured wind speed (iij) at height zj is shown in Eq. 5 from Cooper and
Alley (2002) [36].
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(5)
where wind speeds are in m/s, height is in meters, andp is a unitless coefficient that varies based
on atmospheric stability and surface roughness. The approximate value ofp is 0.5 for very stable
conditions and 0.15 for very unstable conditions.
An error in the code used to calculate the one-minute average wind directions from the wind
vane at the top of the meteorological towers (15.3 m) was discovered in post-processing of the
data taken during the 2007 fall tillage CMP study [7]. For both the 2007 fall tillage and the 2008
spring tillage datasets, one-minute averaged horizontal wind directions calculated from the sonic
anemometer data were used in subsequent analyses. The wind direction measured by the sonic
anemometer was applied to the entire wind speed profile. For most sample periods, wind profiles
above 150 m were not required for the lidar emission rate calculations. Averaged horizontal wind
speed data from the sonic anemometer (11.3 m agl) on N Met were compared with the nearest
cup anemometer (9.7 m agl) wind speed measurements as a quality check. Both data sets showed
the same patterns and recorded wind speeds were close (< 0.25 m/s difference), with the
observed difference likely due to a combination of vertically separated sample heights and
instrument error.
3.1.3 Soil Characterization
Soil characterization involved collecting soil samples for analysis of bulk density, soil moisture,
and sand/silt/clay content. Bulk density samples were collected in each field prior to tillage
operations. A manual device consisting of a 7.6 cm-diameter and 7.6 cm-deep cylinder was hand
driven into the soil until the top of the cylinder was level with the soil surface. Samples were
removed and placed into pre-weighed cans. Post-weights were performed in the field for
determination of wet weight. All weights were determined using a Mettler balance (Columbus,
OH), Model PM2000.
Samples for soil moisture were taken for each day of operation at random locations in the field
and collected in pre-weighed cans 7 cm in diameter and 4 cm deep. Samples were collected
immediately prior to the tillage period or shortly after commencement in areas that had not been
tilled. The can was pushed into the soil approximately 3cm then removed. The can was closed
and weighed in the field for determination of wet weight.
All soil samples were dried at the Agricultural Research Service (ARS) National Laboratory for
Agriculture and the Environment (NLAE) in Ames, IA at a temperature of 105 °C until a
constant weight was achieved (-60 hours). Samples were then weighed to determine dry weight.
Calculations for soil moisture and bulk density were performed according to the following
equations as found in Doran and Jones 1996 [37].
SDL/08-556 California 2008 Tillage Campaign 26
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field water content (%) = weight of moist soil - weight of oven dried soil x 100 (6)
weight of oven dried soil
% moisture = weight of moist soil - weight of oven dried soil x 100 (7)
weight of moist soil
bulk density = weight of moist soil x (1 - field water content) (8)
volume of soil collected
where "volume of soil collected" = TI x radius2 x length of cylinder = 71 x 3.812 x 7.62 = 347.3
cm3 and "field water content" is the value given by Eq. 6 expressed as a fraction.
A composite was made of all the samples collected. It was analyzed for the percent of sand, silt,
and clay according to the Hygrometer Procedure, as given in Soil Sampling and Methods of
Analysis (1993) [38]. The percent of stable aggregates was also determined from the composite
sample according to the Dry-Sieve Method, as given in the Soil Sampling and Methods of
Analysis (1993) [39].
3.1.4 Air Quality Point Samplers
The suite of air quality point samplers deployed around the tillage plots to quantify both the
ambient and ambient plus operations emissions values were summarized in Table 11 and Table
12. Details of each of these sensors and their data processing methods are presented below.
3.1.4.1 MiniVol Portable Air Sampler
Twenty-four MiniVol Portable Air Samplers (Airmetrics, Eugene, OR) were distributed to
gravimetrically measure the time-averaged mass concentrations of aerosols at multiple locations
surrounding the fields of interest. The MiniVol is a battery operated, ambient air sampler that
gives results that closely approximate air quality data collected by a Federal Reference Method
(FRM) PM sampler [40][41]. The MiniVol is neither designated as an FRM nor a Federal
Equivalency Method (FEM) by the EPA, and results should be considered as approximate
measures of PM. The sampler draws air through a particle size separator, or impactor head, and
then through a filter medium [42]. The photo in Figure 9 shows MiniVols mounted on a
rechargeable battery pack with attached impactor sample heads deployed during the reported
study.
Particulate concentrations in the PMio and PM2.5 size fractions were measured using impactor
heads for size separation based on aerodynamic diameter, while Total Suspended Particulate
(TSP) was measured with the impactor head removed. Each PM sampler was assigned to sample
a specific size fraction at a specific location throughout the study, with location changes made as
deemed necessary. Clusters of three PM samplers were assigned to four locations, two upwind
and two downwind, in order to provide size fractionated, mass-based particle loading
distributions.
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Figure 9. Two Airmetrics MiniVol Portable Air Samplers and a Met One Instruments Optical Particle
Counter (OPC) deployed for field sampling during Spring 2008 tillage study.
Filters used in the PM samplers were pre-conditioned according to the protocols outlined in 40
CFR 50 Appendix J before obtaining pre- and post-sample filter weights. Final average weights
were found using a Mettler Toledo Microbalance, Type MT5 located at the Utah Water Research
Laboratory (UWRL) in Logan, UT to determine three stable weights within ± 5 jig, measured on
different days. Filters were transported to and from the site and stored on-site in dessicators to
maintain filter conditioning. Flow calibrations on each MiniVol were performed using a slant
manometer prior to the study and the actual sample flow was adjusted daily on each instrument
in order to maintain the required 5.0 L/min for accurate particle size separation.
During this study it was found that the PM2.5 and PMio impactor heads were being overloaded
with particulates due to high wind conditions and extremely high ambient particle loading.
Therefore, for June sample periods, we installed a PMio impactor in series with a PM2.5 impactor
to serve as an additional filter. These efforts are described more fully in Appendix B.
3.1.4.2 Optical Particle Counter
Ten Optical Particle Counters (OPCs), Model 9722 from Met One Instruments, Inc. (Grants
Pass, OR), were collocated with MiniVols in order to describe the particle count and size
distribution at locations measuring background aerosols and those locations measuring
background plus plume aerosols. Figure 9 shows a Met One OPC collocated with MiniVols for
sampling in Field 5. The 9722 particle counter uses scattered light to size and count airborne
particles. Particle counts are reported in eight, user-defined channels over a user-defined sample
time. For this study, the OPCs collected samples continuously at a sample time of 20 seconds
with the following channel sizes, in units of |im: (1) 0.3-0.5, (2) 0.5-0.6, (3) 0.6-1.0, (4) 1.0-2.0,
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(5) 2.0-2.5, (6) 2.5-5.0, (7) 5.0-10.0, and (8) >10.0. The data from each OPC were relayed to a
single computer over a custom radio network for storage. Inlet flows for individual OPCs were
measured on-site before and after the experiment using a Gilian Gilibrator2 Calibration System,
a volumetric flow meter. The average of the averages from each flow measurement period was
used as the sample flow throughout the field study.
Calibration of OPC particle counts was performed for each sample day in post-campaign
analysis. For this calibration, careful examination of the number concentration (number of
particles/m3) time series yielded a time period before or after the tillage operation during which
no plumes were detected. Number concentration was chosen as the calibration point, as opposed
to the raw particle count data, because it normalizes the raw particle counts by sample flow (see
Eq. 10); sample flow varies up to 20% between OPCs. The average number concentration (Ny)
per bin (/') for the designated calibration time for each OPC (/) was calculated, and the mean of
the averages (Nt~) was used as the calibration concentration. A scalar correction, Counting
Correction Factor (CCFy), was applied to each bin (/') of each OPC (/) and was calculated for
each collocated run based on the calibration concentration for that bin according to the following
equation:
(9)
Number concentration (Ny) is a function of raw particle counts (py), the measured average flow
rate (qj), the sample time (tj), and the CCFy, as shown in Eq. 10.
pa x CCFn
N,, ^ - L (10)
where the units for each variable are N number/cm3, pt number, qj = cm3/min, t minutes,
and CCF is unitless. As in Eq. 9, the subscript y represents a specified OPC.
The volume concentration of aerosols based on a number concentration TV is calculated based on
the following assumptions: 1) the particles are spheres, 2) the maximum particle diameter
measured is 20 jim, and 3) the geometric mean particle diameter per bin (GMD,) is
representative of the particles in a given bin / with the assumption of a log-normal distribution of
particle numbers. The GMD, is calculated by Eq. 11.
GMD, ^upperxd,Jower (11)
where di:Upper and di:iower are the diameters of the upper and lower ranges for bin /'. The assumption
of a maximum measured particle diameter must be made in order to calculate the GMD for
channel 8, which counts particles > 10 jim.
The cumulative volume concentration of aerosols (Vk) up to a particle diameter k may be
calculated using the following equation:
SDL/08-556 California 2008 Tillage Campaign 29
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Vk ~N(d)dedd (12)
where N(d) is the number concentration at diameter d. For application to the OPC data, Eq. 12 is
expressed in the following terms that have been previously defined:
Vk - fiME?tf, (13)'
o i i
where GMD, is expressed in |im, N, is in number/cm3, and Vk is in units of |im3/cm3. In this case,
the Vk definition is similar to PM& concentrations: the fraction of the total volume of particles
whose diameter, in jim, is < k = 1, 2.5, 10, and oo for TSP.
By collocating PM samplers and OPCs, the data provide information about the relationship
between optical and aerodynamic measurements and allow direct calibration of optical
instruments (both OPC and lidar) to mass concentration instruments by estimation of the mass
conversion factor (MCF) for each PMk fraction. The theoretical conversion from particulate
volume concentration to mass concentration is complex, and several simplifying assumptions
must be made. These include a spherical particle shape approximation, a priori assumption of
the refractive index, and neglecting multiple scattering effects. The time-resolved V^ data from
each OPC as calculated in Eq. 13 are then averaged over the corresponding PM sampler sample
time. The MCFs, in units of density (g/cm3), for each PM size fraction k are calculated by
dividing the mass concentrations measured by the PM samplers (PMk) by the time-averaged V^.
These data are averaged over several locations or instrument clusters j, where Ey = «, and both a
daily mean value and an overall mean value of the MCF,t is calculated for each PM,t fraction
separately.
1 ' " PM
The aerosol number size distribution (dNld(ln(&))) is calculated as outlined in Hinds [43] and
expressed mathematically in Eq. 15.
dN/d(\n(d» — - i- - - (15)'
where di:Upper and drawer are the diameters of the upper and lower ranges of bin /'.
3. 1 .4.3 Organic Carbon/Elemental Carbon Analyzer
An Organic Carbon/Elemental Carbon Analyzer (OC/EC), Model 5400 from Rupprecht and
Patashnick Co., Inc. (Albany, NY), was located in the Air Quality Trailer (AQT) on the
downwind borders of Field 5. This instrument provides sample-averaged organic carbon and
elemental carbon mass concentrations over a user-defined sample time, which was set at one
hour for this study. In operation, the system alternately collects particulate matter onto one of
two ceramic filters which, after the desired collection period, are heated within a closed-loop
system to determine carbon content via direct thermal desorption and pyrolysis techniques
developed and validated by Rupprecht and Patashnick [44]. As recommended by the
manufacturer, during the analysis phases, an initial temperature plateau of 250° C for 600
seconds was used for determination of the organic carbon (OC) fraction and a final temperature
SDL/08-556 California 2008 Tillage Campaign 30
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plateau of 750° C was used for quantification of the elemental carbon (EC) fraction. To account
for non-carbon components of the organic compounds' mass, the OC concentrations reported by
the 5400 analyzer were increased by the recommended multiplier of 1.7 [45]. A sharp-cut PM2.5
cyclonic separator was placed at the inlet, which was located directly above the instrument on
top of the trailer. A nominal flow rate of 16.7 Lpm was maintained through the system by on-
board mass flow controllers and integral temperature and pressure sensors.
The 5400 instrument successfully passed the flow checks, leak checks, and CC>2 audits as
prescribed by its manual the week prior to departure for the field campaign [44]. The instrument
passed these same checks upon setup in the field on May 13, 2008. It passed CC>2 audits
administered in the field on June 3 and June 21, 2008.
3.1.4.4 Ion Chromatographic Analysis
To more fully chemically characterize the nature of the upwind and downwind particulate matter,
ion chromatographic (1C) analysis was performed on filters collected via the MiniVol systems
that were collocated with the EC/OC inlet on top of the Air Quality Trailer (AQT). After final
post-test weights were determined from the filters at the UWRL, the chosen filters were
sonicated with triplicate rinses in 10 mL double-distilled, de-ionized water (DDI) and split into
two aliquots of approximately 15 mL each for separate anion and cation analysis. The anion
analysis occurred within 48 hours of sonication and the cation aliquots were stabilized with 10
uL of 0.5 M HC1 acid and analyzed within 28 days of sonication. The base 1C system (Dionex
Corporation) utilized an AS 40 (Automated Sampler), a CD 20 (Conductivity Detector), a GP 40
(Gradient Pump), and a membrane suppressor. Cation quantification was accomplished using an
lonPac® CS12A cation column, a CG12A cation guard column, and a 500 jiL sample loop. The
system eluent was 0.15 M H^SC^ with a 1.0 mL/min flow. Anion concentrations were
determined using an lonPac® AS11HC anion column, a AG11HC anion guard column, and a 188
|iL sample loop. For anions, the system eluent was 30 mM NaOH with a 1.0 mL/min flow.
American Chemical Society (ACS) reagent grade materials were used to prepare stock standard
solutions for each of the target ions, from which concentrations of 0.5, 1.0, and 5.0 mg/L (ppm)
were mixed to make 1C calibration curves. The ions quantified were fluoride (F~), chloride (Cl~),
nitrite (NCV), sulfate (SO/f2), nitrate (NCV), sodium (Na+), ammonium (NH4+), potassium (K+),
magnesium (Mg+2), and calcium (Ca+2). Verifications of the system calibrations were performed
prior to each analysis run. Continuing calibration verification standards (CCVs) and blank
samples (DDI water) were analyzed roughly every 10 samples. Peak identification and data
processing were executed using Dionex PeakNet Data Chromatography software (Version 2.0).
3.1.4.5 Aerosol Mass Spectrometer
An Aerosol Mass Spectrometer (AMS) from Aerodyne Research, Inc. (Billerica, MA) was
located in the sampling trailer, with a sample port on the upwind side of the trailer just below the
roof level (~3 m). The AMS provides chemical composition and particle size information for
volatile and semi-volatile particle components in the 0.1 - 1.0 jim size ranges in vacuum
aerodynamic diameter. Hence, it is nominally considered a PMi instrument for the fraction of
chemicals detected. An AMS size calibration was conducted on-site on May 14 using
polystyrene latex spheres (PSLs). Mass calibrations were also conducted on-site on May 14 and
June 4, 2008 using ammonium nitrate. The AMS vaporizer was operated at higher than normal
temperature (-800 °C vs. -600 °C) to detect some of the inorganic components in dust particles.
SDL/08-556 California 2008 Tillage Campaign 31
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During sampling, the AMS integrated and saved particle composition and size data every ten
minutes.
3.1.5 Lidar Aerosol Measurement and Tracking System
The Aglite lidar system is a monostatic laser transmitter and 28-cm receiver telescope (Figure
10). The laser is a three-wavelength, 6W, Nd:YAG laser, emitting at 1.064 (3W), 0.532 (2W)
and 0.355 (1W) um with a 10 kHz repetition rate. The lidar utilizes a turning mirror turret
mounted on the top of a small trailer to direct the beam -10° to + 45° vertically and + 140°
horizontally. Lidar scan rates from 0.5° - l°/s are used to develop the 3D map of the source(s),
dependent on range and concentration of the aerosol.
The process used to retrieve aerosol mass concentration from lidar data is illustrated in Figure
11. The details of Aglite lidar calibration and the aerosol retrieval process are discussed by
Marchant [46] and Zavyalov [47]. The retrieval is as follows: first, the lidar data is preprocessed.
Then, the relationships between backscatter, extinction, volume concentration, and mass
concentration of the aerosol components are established using in-situ data measured by OPCs
and clusters of PM samplers with different separation heads (P1VU). After that, the inversion of
the lidar data is performed using a form of Klett's solution [48] for two scatterers where
extinction is proportional to backscatter. Finally, a least-squares method is used to convert
backscatter values to aerosol mass concentration using the previously established relationships.
From the OPC channel counts, the particle size distribution at a single point as a function of time
may be calculated according to Eq. 15. The backscatter and extinction coefficients necessary for
solving the lidar equation are then calculated at the OPC reference point as a function of time.
Klett's inversion is used to convert the lidar signal to the optical parameters (backscatter values
in particular) of particulate emitted by the operation [48]. Having recovered backscatter values as
a function of range and wavelength using the Klett inversion, these must be converted to the
Figure 10. The three wavelength Aglite lidar at dusk, scanning a harvested wheat field.
SDL/08-556
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f Lidar Signal 1 foPC Data] fpM Samplers]
1
Preprocessing
i
Klett Inversion
{
Aerosol
Concentration
Retrieval
<
r Volume ^
Iponcentration;
4
^Particle Size^j
^Distribution^'
\
/^Boi
*~ Con
\Lida
(,
mdary^
dition +
• Ratiosy
i
Ma
onve
Fac
ss ^
rsion .
tor j
Mass
^ConcentrationJ
Figure 11. The Aglite lidar retrieval algorithm flow chart, showing the input locations for the in situ data.
aerosol cumulative volume concentration. Expressing the particle normalized backscatter values
from the OPC (pv) and the lidar measured backscatter values (PE) in a vector form, and applying
the Moore-Penrose weighted minimum least-squares solution results in the value for n(r), the
relative amplitude of the aerosol component to total atmospheric backscatter, at range r
n(r]
(16)
which can be multiplied by the particle normalized volume concentration vector, resulting in the
VK(r) VEn(r)
(17)
The term W is a diagonal weighting matrix, whose diagonal elements are the expected variance
of the emission backscatter for the corresponding channel.
The retrieved aerosol volume concentration from the lidar return signal is multiplied by the
MCFfe which was previously calculated using in-situ data (Eq. 14). At this point, the Mi fraction
of the aerosol mass concentration of the emission component is known as a function of distance.
PMK(r)
MCFK
•VK(r)
(18)
The concept behind our flux measurement approach is shown in Figure 12A, where the facility
is treated as one would calculate the source strength in a bioreactor. In this simplified
mass-balance approach, the source strength is determined using the mean flow rate through the
reactor and the difference in reactive species concentration entering and leaving the vessel. The
scanning lidar samples the mass concentration fields entering and leaving the facility and
standard cup anemometers provide the mean wind speed profile. If we define our box large enough
so that none of the emitted material escapes through the top or crosswind sides of the box, and
the downwind side is far enough from the facility to minimize high frequency fluctuations,
the same simple relationship found in a bioreactor applies.
SDL/08-556
California 2008 Tillage Campaign
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Wind Direction
Flux In
900
800
range 700
(meters) BOO
Figure 12. (A) Conceptual illustration of the method for using lidar to generate time resolved local area
particulate fluxes. (B) An example of a "staple" lidar scan over the facility showing aerosol concentration on
the three sides of the box.
An example of our lidar-derived concentration data is shown in Figure 12B. The concentration
plot pattern from scanning up one side, across the top, and down the other looks like the common
office staple (the paper fastener), and will be referred to as a'staple' scan. The data from
the top of the box is regularly examined to be sure that no significant particulate transport
is passing through the top. The data for the left side panel of the 'staple' provides the background
concentration entering the box, while that on the right provides the background plus facility
concentrations leaving the box. The integrated mass concentration difference multiplied by the
wind speed during the scan completes the flux emission calculation by yielding mass per unit
time emission from the facility.
The flux calculation in the integral form for calculating facility/operation emissions (F) can be
expressed as following:
F
(19)
r,h
where Vj/', is the average wind speed component perpendicular to the lidar scanning plane,
CrKr, h) is the downwind PM& concentration at position range r and height h, and Co is the
average PMg concentration in the upwind scanning plane. CD - Cu form the mass concentration
difference upwind and downwind, integrated over the range (width) and height of the sides of the
staple. In our routine, Eq. 19 is discretized as:
R H
(20)
where RO and R are the near and far along beam edges of the box and H0 and H form the top and
bottom of the box. (In many cases, HO is set above eye level and concentration is extrapolated to
the ground to avoid illuminating personnel and animals.) The Ar-A/z term is the individual area
element for which each flux component as calculated by each step in the double summation.
SDL/08-556
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Figure 12B shows an example of single-scan Cu and CD mass concentration values for
measured along the vertical sides of the staple shape between the distances of 600 and 900 m
from the lidar. Single-scan differences, of course, do not account for accumulation or depletion in
the box due to wind speed variation during a scan, or from high or low concentration pulses that
may still exist in the downwind sample. For a meaningful estimate of the facility emission, many
scans are combined to achieve a time-averaged emission rate.
To perform the lidar calibration using the in-situ instruments, collocated samples are needed for
the PM sampler, OPC, and lidar. In other words, the lidar beam must directly sample the same
volume as an OPC/PM sampler location for at least the duration of one OPC sample collection
time. When the lidar beam is held constant, usually pointing next to a sample tower, we refer to
this as the 'stare' mode. The 'stare' sample mode provides not only calibration, but quality
assurance of the data set as well. After data processing and PM concentrations along the beam
have been calculated, successive 'stare' data are compared against data from the reference OPC
and PM samplers to verify that the concentrations calculated from both instruments are similar
within measurement errors.
For most of this study, the Aglite lidar was located at position LI and used the tower Tl, located
at 31° as the upwind reference point. Downwind scans for Field 4 were made along the line
between LI and T2 at about 73°; downwind scans for Field 5 were made along the line between
LI and AQT at about 91°. Scanning profiles were programmed for each field and included both
'staple' scans and 'stares', with some variations as necessary according to conditions at the
sample time. An example of a lidar scan profile used for each field is provided as a reference,
with Figure 13 and Figure 14 presenting graphical representation of the azimuth and elevation of
the beam during each sequential data averaging and archival period (averaged and archived
every 0.5-1.0 seconds).
Field 4 - The scanning pattern used during the breaking borders operation on May 17
commenced with a stare at 31° for 73 seconds followed by three upwind vertical scans reaching
45° in elevation and lasting 90 seconds each. The downwind portion of the profile consisted of
three vertical scans at an azimuth of 73.5°, each reaching 45° in elevation and lasting 90 seconds.
These vertical scans were followed by a stare at the downwind tower for 67 seconds. The beam
was then returned to 31° and began again. The total time per profile including intermittent
delays to position azimuth was 758 sec. (-12.6 min.).
Field 5 - The scanning pattern used during the herbicide application operation on June 11
commenced with a stare at 31° for 33 seconds followed by two upwind vertical scans reaching
15° in elevation and lasting 29 seconds each. The downwind portion of the profile consisted of a
stare for 63 seconds at azimuth angle 91° followed by four vertical scans each reaching 15° in
elevation and lasting 29 seconds. The profile then moved the beam to an azimuth of 73° for a
stare at T2 lasting 18 seconds. The beam was then returned to 31° and the profile was started
over. The total time per profile including intermittent delays to position azimuth was 395 sec.
(-6.6 min.).
SDL/08-556 California 2008 Tillage Campaign 35
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500
1000
Data Point
1500
2000
Figure 13. Example of a lidar scan profile used to monitor PM concentrations around and emissions from
conventional tillage operations in Field 4. Each data point represents a 0.5 second averaging time, therefore
data point 1000 was taken at time = 500 seconds.
100
90
80
70
60
" 50
ff
Q 40
30
20
10
0
I
500
1000
Data Point
1500
2000
Figure 14. Example of a lidar scan profile used to monitor PM concentrations around and emissions from
conservation tillage operations in Field 5. Each data point represents a 0.5 second averaging time, therefore
data point 1000 was taken at time = 500 seconds.
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3.2 DISPERSION MODELING SOFTWARE
The U. S. EPA has approved a number of air dispersion models for use in regulatory
applications. These are listed in Appendix W of 40 CFR Part 51 [49]; included are the Industrial
Source Complex Short-Term Model, version 3 (ISCST3) and the American Meteorological
Society/Environmental Protection Agency Regulatory Model (AERMOD), which as of
November 2005 is recommended for all regulatory applications [1][2]. Both models assume
steady-state conditions, continuous emissions, and conservation of mass. The default, and
minimum, time-step available for both models is one hour. Therefore, all meteorological input
data represent one-hour averages.
ISCST3 assumes a Gaussian distribution of vertical and crosswind pollutant concentrations [50].
The Gaussian plume equation uses the Pasquill-Gifford horizontal and vertical plume spread
parameters, oy and erz, respectively, shown in Eq. 21.
10 _ _
Cio is the ten-minute average concentration (ug/m3), Q is the emission rate (ug/s), UGM is the
average wind speed at release height (m/s), y is the horizontal distance of the chosen receptor
from the centerline of the plume (m), z is the height of the receptor above ground level (m) and H
is the effective stack height (m), which includes estimates of plume rise due to buoyancy and/or
momentum [50].
ISCST3 assumes a Gaussian distribution of pollutants based on time-averaged meteorological
data. It also uses stability classes to address pollution dispersion due to atmospheric mixing.
Stability classes are typically determined by a combination of vertical temperature lapse rates
and incoming solar radiation or methods using vertical or horizontal wind variance [51].
AERMOD requires more detailed meteorological and surface characteristic information. Because
of the additional input requirements for AERMOD and the lack of an established database for
these inputs, many regulatory agencies continue to use ISCST3.
The suite of meteorological instruments employed during this field study allowed us to use both
models in this study. AERMOD uses continuous functions for atmospheric stability
determinations, and based on stability determines the appropriate distribution: a Gaussian
distribution for stable atmospheric conditions, and a bi-Gaussian distribution for unstable, or
turbulent conditions. AERMOD is also better at accounting for terrain features and building
downwash phenomena than ISCST3 [52]. The commercial interface used to run the models was
ISC-AERMOD View packaged by Lakes Environmental, Inc. (Waterloo, Ontario, Canada).
Final emission rates were determined using inverse modeling coupled with observed facility-
derived pollutant concentrations. In inverse modeling, the downwind impact on pollutant
concentrations by a source is known while the emission rate is unknown. To solve for the source
emission rate, a model such as ISCST3 or AERMOD is run with the following inputs: on-site
collected meteorological data, the facility layout, the locations of pollutant sources and receptors
(samplers), and an estimated or "seed" emission rate, which can be obtained from literature.
Observed facility-derived concentrations are calculated by subtracting measured background
levels from concentrations measured at locations impacted by the source plume. Modeled
concentrations are then compared to the facility-derived concentrations from both MiniVols and
OPCs at each sampler location. The location-specific ratio of the measured concentration
SDL/08-556 California 2008 Tillage Campaign 37
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to the modeled concentration (Cmodekd) is multiplied by the seed emission rate (Eseed)
and an average across all valid locations is calculated to yield the source emission rate
corresponding to the measured facility-derived concentrations (Eestimated) as shown in Eq. 22.
(c \
' F F \ measured (22)
^estimated *-*' seed\ ^ \^^>
\ modeled j
A seed emission rate of 8.6 ug/s-m2 was used in the dispersion models. This value was an
average of preliminary emission rates found through inverse modeling in the 2007 fall tillage
CMP study that is a companion to the current study [7].
It should be noted that evaluations of air dispersion model accuracy have shown that models are
better at predicting concentrations over longer averaging times than shorter time periods at a
specific location; such models were developed and optimized to predict longer-term averages
and do not incorporate all of the many temporally and spatially variable factors that may affect
dispersion in the atmosphere. Models can predict the magnitude of the highest concentration
reasonably well, with a typical range of error of ±10% to 40%, but do not reliably predict the
exact location and time of the highest value. Measured and modeled concentrations at the same
location are usually poorly correlated, which is likely due to a combination of uncertainties in the
input data and unquantifiable uncertainties within the model. The uncertainty of the input data
(e.g., meteorological data, emission rates, source and receptor locations, topography) potentially
may be reduced through careful collection and screening. In addition, the deposition and particle
removal mechanisms are limited in the ISCST3 and AERMOD models herein employed.
Insufficient correction within a given model for these and other processes that decrease
downwind pollutant concentrations will lead to an underestimation of emission rates based on
measured downwind impacts.
An example of errors due to uncertainty in the input parameters is that concentration errors
between 20% and 70% can result from an uncertainty of 5° to 10° in the wind direction that
directly affects plume location, depending on atmospheric stability and the sampler/receptor
location. Uncertainty within air dispersion models, called "inherent uncertainty," is mostly due to
the simplification of complex and highly variable processes affecting dispersion in the
atmosphere. If atmospheric conditions that are used as inputs into the model (e.g., wind speed,
wind direction, mixing height) are consistent across multiple sample periods, the model would
predict the same concentration, while measured concentrations could vary significantly due to
variability in the complex processes that are not directly accounted for in the model. These
inherent uncertainties can produce predicted ground level concentration errors of up to 50%. A
more detailed discussion of air dispersion model uncertainty and accuracy is presented in
Appendix W, 40 CFR 51 [49]. The report "Air Emissions from Animal Feeding Operations:
Current Knowledge, Future Needs" by the National Research Council of the National Academies
states that due to the assumptions required with Gaussian dispersion models, the uncertainty
associated with predicted concentrations are not smaller than ±50%. Additional uncertainty is
introduced by stability classification and sample instruments [53]. However, the placement of
near ground-level receptors along the downwind side of large, ground-level area sources, such as
the setup in this study, potentially may reduce the inherent uncertainty of the predicted
concentrations due to both the vertical and horizontal proximity of the receptor to the source.
Unfortunately, no discussion of uncertainty in dispersion models under such conditions is
available.
SDL/08-556 California 2008 Tillage Campaign 38
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Based on the issue of potentially reducible uncertainty, all of the input data for both models were
very carefully screened to reduce uncertainty in the output to the maximum extent possible, as is
subsequently described. Even with such efforts, the error in the model predicted concentrations
for this study is expected to be ±50% according to the above sources, which, when combined
with a ±20% sampling error of the difference between upwind and downwind MiniVol PM
samplers (±10% each) in Eq. 22, yields a range of error about the calculated emission rate at a
single location between -46% and ±140%. Averaging valid emission rates per sample period may
reduce the potential error range to -33% to ±100% by removing instrument sampling error.
The meteorological data were carefully screened and corrected for errors prior to preprocessing
for the dispersion models. It was this same screening process that uncovered the erroneous wind
direction averaging code for the meteorological towers in the analysis of the 2007 fall tillage
CMP study [7]. As previously described, we have instead employed the horizontal, one-minute
averaged wind directions from sonic anemometers. The meteorological inputs for the models
generated by the preprocessing programs were also screened for consistency with input measured
values; adjustments were made as necessary. The effects of the uncertainty associated with the
meteorological input, however small, combined with the inherent uncertainty within the model
most greatly impacts model-predicted concentrations at receptor locations that are near the edges
of the predicted plume, which in turn may greatly impact the calculated emission rates. Arya
(1998) suggests that the plume edge be defined as 10% of the maximum modeled concentration
to minimize these effects [54]. Therefore, emission rates calculated at locations with predicted
concentrations less than 10% of the maximum predicted concentration were not used in
calculating the average emission rate.
AERMET, the preprocessor of meteorological data for AERMOD, requires that the surrounding
land use and land cover be categorized to quantify the Bowen ratio, surface roughness, and
midday albedo. For this study, the land use on all sides of the site was classified as cultivated
land and the default spring time values of midday albedo (0.14), Bowen ratio (0.30), and surface
roughness (0.03 m) were used because the summer values for cultivated land assume vegetation
cover, which was not the situation for the majority of measurement periods. During each of the
sample runs, the sky was clear of clouds, so the amount of cloud cover was set to 0.0 for all
hours. The mixing height for input into RAMMET and AERMET was set at 1000 m because all
samples were started at least two hours after sunrise and ended before sunset. In addition, all the
receptor locations of interest were on the southern edge of the field so the exact depth of the
mixing layer during daylight hours over such a short distance at ground level was not considered
to be a significant factor. Based on the measured incoming solar radiation, vertical temperature
lapse rates, and surface wind speed, stability classes for ISC during all sample periods were
determined to be slightly unstable to very unstable. The Upper Air Estimator in AERMET View
was used to calculate required upper air parameters based on observed surface conditions.
Digital elevation model files based on 7.5 minute topographical maps were used to calculate
receptor and source elevations. The terrain was not considered to be a significant factor in the
modeled concentrations as the change in elevation over the entire modeled domain (~2 km x 2
km) was gradual and no greater than 2 meters. The areas tilled during the sample periods were
represented in the dispersion models by ground level area sources that varied in size and shape.
The readings of the handheld GPS tracking device located on Tractor 1 for each tillage operation,
mentioned in Section 2.2 were used to develop the source areas and shapes. The GPS readings of
sample locations were used to specify discrete receptors for comparison between modeled and
SDL/08-556 California 2008 Tillage Campaign 39
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measured concentrations at specific locations. In addition, a receptor grid with 15 m x 15 m
spacing and a flagpole height of 2.0 m above ground level was set up slightly upwind of, over,
and downwind of the area source to provide a visualization of the modeled particulate matter
concentrations resulting from the area source emissions. Elevations for receptors and source
areas were calculated and assigned by AERMAP. Table 30 in Appendix A provides more details
about the settings used in each model. All concentrations used for emission rate calculations and
presented in this report are averages over the modeled periods.
3.3 STATISTICAL ANALYSIS OF DATA
The goal of this investigation is to determine if there are statistical differences between the
conventional and conservation tillage practices. Many different instruments and techniques were
used during this experiment; some techniques exhibit Gaussian distributions and some
instruments exhibit Poisson distributions. Instruments that count events (photons or particles)
exhibit Poisson statistics. The specific "Poisson" instruments are the OPC and Aglite. All other
instruments exhibit Gaussian statistics. Regardless of the kind of statistics a particular instrument
might use, the same general principle is employed, which is based on comparison of the
maximum error (as computed through confidence intervals and T-values) for conservation and
non-conservation tillage processes. The respective maximum errors for two tillage processes are
compared using a simple T-test for the two time intervals. The null hypothesis is "there are no
differences in the particulate emissions between conventional and conservation management
practices."
Soil samples were collected over the entire field of study and analyzed using Gaussian statistics.
The respective averages and confidence intervals were calculated for each region of the field.
Since no statistical difference was found among regions of the field, global statistics for the
whole field were calculated and used to describe the entire field. In the case of the OPC
instruments, the OPCs generate a particle size distribution every 20 seconds for a tillage
operation lasting several hours. These data were first manually examined to remove data artifacts
such as unrealistic high particle counts or partial distributions. Then, all of the particle size
distributions for the entire tillage process were averaged and the variance and confidence
intervals calculated according to their corresponding Poisson definitions.
The process of extracting particle volume and mass concentrations from the Aglite lidar return
signal is an iterative process that involves several linear least squares regressions. Our process
for retrieval is to average the returns for an entire tillage process, then run the concentration
extraction regression. Typically, several million laser shots are averaged prior to calculation of
concentrations.
A challenge in dealing with highly variable discrete events, such as agricultural tillage operations
is that PM emissions from each tractor/implement pass can - and does - vary considerably from
the previous tractor pass across the field. This leads our raw data to appear "noisy," though in
fact the error bars on any given measurement are typically much smaller, usually by a factor of
five or more, than the natural variability in the instantaneous emissions from the tillage process.
The aggregation of the particulate data, lidar and meteorology data provide an estimate of the
particulate flux within a field. This data will be expressed as cumulative particulate emission for
a specific portion of the tillage operation. Then, differences between systems will be compared
using Gaussian mean comparison methods.
SDL/08-556 California 2008 Tillage Campaign 40
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4. RESULTS AND DECISIONS
4.1 GENERAL OBSERVATIONS
4.1.1 Soil Characteristics
The average bulk density and soil moisture values, with la and the number of samples collected
(«), are presented in Table 13. Figure 15 provides a map of soil sample locations in both fields.
The average bulk densities (± la) for Fields 4 and 5 were 1.57 ± 0.05 g/cm3 and 1.57 ± 0.08
g/cm3, respectively. Similarly, the soil moisture did not differ significantly between the two
fields. Soil moisture generally did not change with increasing number of tillage operations. The
largest in-field differences expectedly were recorded after the irrigation event at the end of May
2008 (see Table 13).
Analysis performed on samples from Field 4 yielded an average soil composition of 62% sand,
29% silt, and 9% clay. For Field 5, the composition was 67% sand, 23% silt, and 10% clay.
Stable aggregate results are presented in Table 14 and show that the mass of stable aggregates
decreased after the first tillage operation with the greatest decrease in the largest sieve. Since the
bulk density and the soil moisture data from the two fields are essentially the same within the
error of the measurement, we expect these fields to have similar characteristics for aerosol/dust
generation. Similarly, as has been shown in the literature [10][11][12][16], we expect the soil
moisture content to strongly influence the amount of aerosol/dust production from any given
tillage operation.
Table 13. Statistics of soil characteristics measured for both fields.
Bulk Density Data Summary
Field 4 (Conventional Tillage)
Field 5 (Conservation Tillage)
Soil Moisture Data Summary (Date)
Field 5 (5/17/08)
Field 4 (5/17/08)
Field 4 (5/19/08)
Field 4 (5/20/08)
Field 4 (6/5/08)
Field 5 (6/7/08)
Mean (g/cm3)
1.57
1.57
Mean (%)
1.3
1.0
1.7
3.3
6.1
8.1
o (g/cm3)
0.05
0.08
o (%)
0.5
0.6
0.7
1.6
2.2
1.6
n
10
10
n
10
10
10
10
12
10
95% CI
0.03
0.05
95% CI
0.3
0.4
0.4
1.0
1.2
1.0
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Table 14. Stable aggregate analysis results for both fields.
Sample collection
Sieve Size (mm)
Mass per
sieve pan (g)
Total Stable Aggregate Mass
out of 100 g analyzed (g)
Conventional Tillage, Field 4
Prior to tillage
After Disc 1
After Disc 2
After flood irrigation
4
2
1
0.5
0.25
4
2
1
0.5
0.25
4
2
1
0.5
0.25
4
2
1
0.5
0.25
31.41
11.38
9.67
10.48
12.16
4.1
6.25
5.2
15.74
19.51
12.92
8.75
15.64
11.62
9.27
12.88
11.55
6.16
14.28
12.02
75.1
50.8
58.2
56.89
Conservation Tillage, Field 5
Prior to tillage
After flood irrigation
4
2
1
0.5
0.25
4
2
1
0.5
0.25
24.84
10.63
6.57
12.58
11.68
7.23
10.87
8.64
13.87
16.72
66.3
57.33
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Figure 15. Soil sample collection locations in fields under study.
4.1.2 Meteorological Measurements
A summary of the meteorological characteristics recorded during the study period are presented
in this section with some examples.
.th
4.1.2.1 Precipitation Event
One precipitation event occurred during the break in measurement periods from May 20m to June
5* to allow for flood irrigation of both fields. The proximity in time of the precipitation event to
flood irrigation of the fields suggested that the effects of flood irrigation on soil conditions and
PM emissions would mask any effect of the precipitation event. A minimum of ten days passed
between the completion of irrigation on the study fields and any tillage activity.
4.1.2.2 Hourly Wind Data
One-minute averaged wind data collected at the meteorological tower at the WM location from
May and June 2008 were averaged again to hourly values in order to create a wind rose of actual
conditions during the field experiment (see Figure 16) for comparison with the wind rose created
for May and June 2005-2007 (see Figure 6) from the CJJVIIS station located near Stratford, CA.
The wind speed measured at 2.5 m agl on the tower was used because the wind measurement for
the CJJVIIS station appeared to be at the 2-3 m agl level. The wind rose in Figure 16 closely
resembles the data collected by the CIMIS station for the previous three years. Wind conditions
were very favorable for the designed sample layout throughout the duration of the field study.
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NE
SW
, .
\ /10.0% :
,20.0%;
30.0% /'
•'400%
SE
Figure 16. Wind rose of wind speed and direction measured during the May-June 2008 campaign
4.1.2.3 Period-Average Meteorological Conditions
Meteorological characteristics were monitored on-site throughout the field study. Sample period
average conditions were calculated and are presented in Table 15 based on measurements taken
at the WM and EM locations. As can be seen from these data, all measurements were made
during warm and dry conditions. Winds were consistently out of the northwest with average
speeds between 2 and 6 m/s.
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Table 15. Average ± lo temperature, relative humidity, wind speed, and wind direction for each sample
period as measured at the WM tower. Temperature, relative humidity, and wind speed were measured at 9.7
m agl and wind direction was measured at 11.3 m agl.
Sample
5/17 Run 1
5/17 Run 2
5/18
5/19 Run 1
5/19 Run 2
5/20 Run 1
5/20 Run 2
6/5 Run 1
6/5 Run 2
6/7
6/11*
6/18
6/25*
Ambient Temperature (C)
32.3 ±2.1
36.8 ±0.2
33. 8 ±2.8
31.4 ±2.5
35.3 ±1.5
29.1 ±2.2
32.5 ±0.4
24.7 ±2.6
27.6 ±0.5
22.5 ±2.7
29.1 ±0.1
34.1 ±0.3*
30.2 ±2.5
Relative Humidity (%)
33 ±4
24 ±0.3
29 ±4
27 ±3
21±3
30 ±10
23 ±1
34 ±7
26 ±2
40 ±9
19 ±1
16 ±1*
29 ±5
Wind Speed (m/s)
3.6 ±0.6
4.3 ±0.6
4.3 ±1.2
2.9 ±0.8
3. 3 ±0.5
5.1±1.1
5. 9 ±0.8
3. 3 ±1.3
4.0 ±0.9
4.0 ±1.0
3. 8 ±0.6
5.6 ±0.7
1.9 ±0.8
Wind Direction (°)
321 ±15
321 ±8
325 ± 16
318 ±22
319 ±16
320 ± 10
317±8
320 ± 30
315±7
335 ±20
326 ± 17
326 ±4
328 ± 29
* Data taken from EM tower due to missing data at WM tower
4.1.2.4 Wind Profile Calculations
The wind profiles used in calculating the PM mass fluxes through the vertical lidar beam planes
were determined by fitting the power law wind speed equation (Eq. 5) to cup anemometer data
from the meteorological tower located at location WM. In fitting the measured data, calculated/?
values ranged from the minimum to the maximum designated limits of 0.1 and 0.6, respectively,
and followed a diurnal cycle similar to that of atmospheric stability class; the mean value of p
throughout the field study ± la was 0.25 ± 0.13.An example of a calculated wind speed profile
based on tower-mounted cup anemometer data and a power-law fitting model is presented in
Figure 17.
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250
200
150
100
50
Generated Wind Profile
Cup Anemometer Wind Speed
»--«*
2.5
3.5
Wind Speed (m/s)
4.5
Figure 17. Cup anemometer measurements shown with the wind speed profiled calculated using the
measured wind speed.
4.2 AEROSOL CHARACTERIZATION DATA
The following section contains the aerosol data collected using both the filter and optical sampler
methods.
4.2.1 Minivol Filter Sampler Data
As mentioned in Section 3.1.4.1, an array of Airmetrics MiniVol samplers was positioned to
characterize the upwind/background and downwind PM concentrations. A total of 296 samples
were collected: 116 (39%) PM2.5, 116(39%) PMio, and 64 (22%) TSP. Calculated PM2.5
concentrations based on filter catch ranged from 23.2 to 3244.9 ug/m3; PMio concentrations
ranged from 38.1 to 1458.4 ug/m3; TSP concentrations ranged from 73.6 to 2276.9 ug/m3. The
MDL for each sample period was calculated based on the average run time of the MiniVols, the
targeted flow of 5.0 L/min, and the minimum filter catch that could be measured in the difference
between the pre- and post-test filter weights of 5 ug. The MDL for each run varied based on
different sample times; the average MDL ± 1 o (n = 13) was 6.6 ± 4.9 ug/m3 and the median was
4.3 |-ig/m3, with a range of 2.3 for a run length of 7.3 hours to 17.3 ug/m3 for a run length of 1.0
hour. The calculated PM concentrations measured at all sample locations during May 2008 may
be found in Table 31 and during June 2008 may be found in Table 32 in Appendix A. Figure 18
presents a contour plot of the measured PMio concentrations at 2 m agl for the June 25 sample
period across the field of study. Sample locations are shown by (+) markers and the field edges
are approximated by the black lines. Note the background PMio concentrations were around 100
Hg/m3.
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1200
(Mg/mJ)
180
160
140
120
100
700
900 950 1000 1050 1100 '1150 1200 1250 1300
E(m)
Figure 18. Contour plot of measured PM10 concentration for the June 25 sample period.
Of the 296 filter samples collected, 165 did not pass quality analysis/quality control (QA/QC)
steps applied to the dataset, leaving 131 for use in calculating emission rates using inverse
modeling. An investigation into this high rate of failure was conducted and a detailed description
is provided in Appendix B. In summary, filters that did not pass QA/QC were suspected to have
been contaminated either during sampling or during storage and handling. Evidence of "particle
bounce" was found on many PIVb.s and PMio samples collected during May sample periods.
Particle bounce occurs when particles that collide with the impactor plate, the mechanism used
by the MiniVols to exclude particles larger than the design size, are re-entrained in the airstream
and collected on the filter downstream and result in higher measured levels than actually existed.
This issue is most likely due to exposing the MiniVol samplers to dust plumes exceeding the
maximum recommended exposure level and improper instrument maintenance and cleaning
through the May sample periods. Corrective action was taken during the June sample periods and
no issues associated with particle bounce were observed in the second portion of the study.
Additionally, some particles were observed on top of and imbedded into the plastic annular ring
around the Teflon filter material - the plastic ring is covered by the filter holder assembly during
deployment. This was likely due to contamination during on-site filter storage or handling.
Efforts were made to minimize this issue throughout, especially during the June sample periods.
However, windblown dust did impact the handling and storage area during the last sample
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periods in May. The size fraction distribution of approved filters was nearly identical to the total
sample set: 51 (39%) were PM2.5, 50 (38%) were PMio, and 30 (23%) were TSP. These finalized
concentrations are given in Table 33 and Table 34 in Appendix A. See Appendix B for a detailed
discussion of the QA/QC steps, filter inspection failure, possible causes of the failures, and
preventative solutions for future sampling.
4.2.2 PM Chemical Analysis
4.2.2.1 Organic Carbon/Elemental Carbon Analyzer
As mentioned previously, the organic carbon/elemental carbon analyzer passed the
manufacturer's suggested in-field audits and after completion of the field project the data were
manually screened for completeness and potential outliers. During the two distinct periods of
sampling, May 13-20 and June 3-21, 2008, the EC/OC instrument operated continually except
for brief periods for QA/QC checks, servicing of the system generator, or significant breaks in
the producer operations. An unanticipated consequence of the one hour sample times, coupled
with the dual channel operation of the R P 5400 EC/OC Analyzer, was that the
sampling/analysis/cleaning cycles extended beyond the planned two hours. The net result was a
sampling profile wherein every third hour of data was missing (66% sample collection efficiency
over the entire deployment). However, because the actual farm practice periods varied from one
to eight hours, the actual, observed data periods ranged from one to eight hours, the data
coverage was from 50-100%, averaging 78.2% ± 10.3%.
The PM2.s OC/EC time series data collected at the downwind Air Quality Trailer (AQT) are
shown in Figure 19, with the shaded sections indicating the discrete sampling periods for the
observed tillage practices. As can be seen, the PM2.s-associated EC was typically quite low,
ranging from 0.3 ng/m3 to 0.7 |-ig/m3 and averaging 0.4 ng/m3 ±0.1 ng/m3 (at the 95%
confidence interval) during the concurrent tillage sampling periods and demonstrated relatively
little variability. During these same sample periods, the total PM2.5 at the AQT, measured by the
filter-based MiniVol systems, ranged from 30.8 ng/m3 to 141.6 ng/m3 and averaging 64.1 |ag/m3
± 26.3 |-ig/m3 (at the 95% confidence interval). For further comparisons, during the non-test
periods the EC PM2.5 concentrations averaged 0.8 ng/m3 ±0.1 ng/m3 (at the 95% confidence
interval). This would suggest that the EC fraction of the PM2.5 at the site was not greatly
impacted by the examined tillage practices or by typical EC sources such as biomass or diesel
combustion and is more likely of a regional phenomenon.
The observed OC PM2.5 concentrations, derived by multiplication of the raw OC concentrations
by 1.7 (refer back to Section 3.1.4.3), were approximately an order of magnitude greater than the
EC concentrations, ranging from 2.8 ng/m3 to 9.2 ng/m3 and averaging 5.6 ng/m3 ±1.3 ng/m3
(see Figure 20). Although the PM2.5 organic component seemed to vary more and occasionally
show greater concentration spikes than the EC, these episodes generally occurred during non-test
events, when the OC PM2.5 averaged 6.0 ng/m3 ±1.3 ng/rn3, statistically indistinguishable from
the tillage test periods.
SDL/08-556 California 2008 Tillage Campaign 48
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-Elemental Carbon
-Organic Carbon
-Total Carbon
o
5/13/08 5/14/08 5/15/08 5/16/08 5/17/08 5/18/08 5/19/08 5/20/08
5/21/08 5/22/08
5/23/08
18
-Elemental Carbon
-OrganicCarbon
-Total Carbon
o
6/3/2008 6/4/2008 6/5/2008 6/6/2008 6/7/2008 6/8/2008 6/9/2008 6/10/2008 6/11/2008 6/12/2008 6/13/2008
Figure 19. PM2.5 OC/EC time series concentrations as collected at the downwind AQT location. The shaded
sections indicate the observed agricultural practices. It should also be noted that the raw instrument OC
concentrations have been multiplied by 1.7 to account for potential non-carbon functional groups.
SDL/08-556
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* -*** <^ <^
fc°
-------
18
16 -
M
•H
ns
s "-
rJ
S 12
Q. C
•a o
c 8
•S3
5s g
D Organic Carbon .
C
O
re 4
u
2
• Elemental Carbon
••
-r
• •
AvgField4 A vg Fie Id 3
Figure 21. PM2.5 organic matter and elemental carbon concentrations during specific sampling periods
(parallel to filter-based sampling).
The cations observed included sodium (Na+), ammonium (NH4+), potassium (K+), magnesium
(Mg2+), and calcium (Ca2+). The resolved anions included fluoride (F), chloride (Cl~), nitrite
(NCV), and nitrate (NCV). It should be noted here that sulfate (SC>42~) is often an expected anion
present in common ambient particulate; unfortunately, chromatographic failures prevented clear
resolution of the sulfate peak so no values for SC>42~ were obtained. However, similar studies by
the project investigators found central California tillage dust contained low levels of SC>42~,
averaging less than 4% of the total observed soluble ions and around 0.5% of the total PM2.5
mass [7]. Aside from the sulfate, all of the expected ions where observed with the exception of
fluoride, which was not detected (n.d.) in any of the samples. As can be derived from Figure 22,
the observed soluble ions contributed around 10% of the total filter PIVb.s mass. On average,
chloride was the most common anion and ammonium and magnesium were the most common
cations. It should be noted that these dominances were not always significant at the 95%
confidence level. Table 16 presents the average ion concentrations across the seven analyzed
filters (uncertainty represents the 95% confidence interval).
Figure 23 shows the average mass percentages for the individual ions, along with the 95%
confidence interval. As can be seen, the average individual ionic species concentrations rarely
approached 2% of the total PM2.5. Although not explicitly shown in either Figure 22 or Figure
23, the total observed ionic percentage can be derived and averaged 10.3% ± 3.8%.
As might be expected, if the presumed sources of the combustion and secondary particles are
more regional in nature, the ionic species would show very little mass concentration differences
across the samples. When the ionic contributions observed within this study are combined with
the EC and OC contributions, the bulk of the particulate material, 81.8% ± 6.1%, was still
contributed by unknown (unanalyzed) constituents (see Figure 24). These unaccounted for
compounds are most likely insoluble crustal elements associated with the background aerosol
and expected soil disturbance emissions during the tillage processes.
SDL/08-556
California 2008 Tillage Campaign
51
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12
10
i 8
^
_3
O
in
Total filter-based PM2.5 (ng/m3)
52'3
6 - / 141.6 J"
/ R R
38.2
s
34.5
I NO3- • NO2-
1 c- D Ca+2
...i- HNH4+
ICI-
IMg+2
INa+
38.2
Figure 22. Soluble ionic mass concentrations of AQT downwind PM2.5 filters.
Table 16. PM2.S filter ion concentrations averaged over seven downwind samples collected at AQT
(uncertainty represents the 95% confidence interval).
Analyzed Ion
Mass
Concentration
F
(Hg/m3)
ad.
cr
(Hg/m3)
1.1±
0.5
NO2
(Hg/m3)
0.8 ±
0.2
NO3"
(Hg/m3)
0.2 ±
0.1
Na+
(Hg/m3)
0.7 ±
0.2
NH4+
(Hg/m3)
0.8 ±
0.1
K+
(Hg/m3)
0.4 ±
0.2
Mg+2
(Hg/m3)
0.8 ±
0.6
Ca+2
(Hg/m3)
0.5 ±
0.3
SDL/08-556
California 2008 Tillage Campaign
52
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Na+
NH4+
K+
Mg+2
Ca+2
Cl-
NO2-
NO3-
Figure 23. Average soluble ionic mass percentage composition of the AQT downwind PM2.5 filters. The error
bars represent the 95% confidence interval.
DOC
• EC
DNa
• NH4
• K
• Mg
DCa
• F
• Cl
• N02
• N03
D unkown
Figure 24. Average compositional mass percentage of the AQT downwind PM2.5 filters.
SDL/08-556
California 2008 Tillage Campaign
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4.2.3 Aerosol Mass Spectrometer
During the tillage experiment, the AMS acquired chemical composition data from May 14-May
19 with some significant gaps in the data due to mass spectrometer malfunctioning. This required
manual screening of the AMS data. Approximately 35% of data over the time period was found
to be valid. Large gaps occurred throughout the sampling period with, for example, no data
acquired on May 17. Similar to the AMS data acquired in the companion experiment in Los
Banos the previous year [7], the OC fraction dominated the detected particle chemical
composition (Figure 25). The mass spectrum shows that PMi is mostly dominated by the
presence of photochemical regional pollutants, such as ammonium nitrate (NFLiNOs) and
ammonium sulfate ((ML^SO/O salts and OC, as in Figure 26. Particle size information was
obtained (Figure 27 a and b) and is indicative of photochemical production of particles with the
size distributions showing a mode at approximately 0.6 jam (600 nm).
I Organic
I Nitrate
I Sulfate
I Ammonium
I Chloride
Figure 25. Average chemical composition of particles detected by the AMS from Mayl4-15.
N
CO
(Z
c
o
30- ^Nitrate Su,fate
5/14/2008 0:00-6:00 AM
120
140
Figure 26. Representative AMS mass spectrum of particles detected during the study. Mass-to-charge (m/z)
ion assignments include nitrate (m/z 30 NO+ and m/z 46 NO2+), sulfate (m/z 48 SO+, 64 SO2+, 81 HSO3+, and 98
H2SO4+), and carbon (e.g. m/z 55 C4H7+, 57 C4H9+, 77 C6HS+, 91 C7H7+).
SDL/08-556
California 2008 Tillage Campaign
54
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Because of the major gaps in the AMS data, there were few and limited time periods where the
AMS and Met One OPC data overlap. A brief six-hour overlap period is shown in Figure 28
where AMS mass concentration data accounts for -80% of the converted mass concentrations
for the 1 jam size bin of the OPC at the AQT sampling site. Since the AMS does not detect
refractory components of the aerosol (e.g., inorganic oxides and black/elemental carbon) and it
measures vacuum aerodynamic diameter versus the OPC that measures optical diameter, it is
expected that the AMS would detect less mass concentration than the OPC. The overall trend,
however, for the time period is consistent between the two instruments.
12:00 AM
5/14/2 DOS
3:00 AM
Date and Time
6:00 AM
(b)
5/14/2008 2:00-4:00 AM
3 456789
1000
Vacuum Aerodynamic Diameter (nm)
Figure 27. (a) Image plot of nitrate particles on May 14,2008 using m/z 30 (NO+). This shows the formation
of a mode of nitrate particles in the early morning hours of 5/14/2008, with peak mass concentration at ~3:00
AM Pacific Standard Time, (b) Integrated size distribution of nitrate particles from 2:00-4:00 AM on May 14,
2008 showing the peak in the mass distribution at ~0.65 um.
SDL/08-556
California 2008 Tillage Campaign
55
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8 -
C
•2 6
re
c
0)
4 -
o
U
(O
ro 2
T
8:00 AM
5/15/2008
T
-r
T
- 10
8 §>
o
o>
3_
6 3
o'
3
4 5
3
i- 2
10:00 AM 12:00 PM
Date and Time
1
2:00 PM
Figure 28. Comparison of AMS PMi and OPC PMt (assuming a MCF of 1.0 g/cm3) data for the morning of
May 15,2008
4.3 OPTICAL CHARACTERIZATION DATA
Several different optical instruments were used to characterize the airborne particles, both
background and those emitted by the operations under study. Samples of the results from each
instrument are presented below, with emphasis on the lidar results.
4.3.1 Met One Optical Particle Counter
The collected OPC data were analyzed for particle size distribution, particle volume
concentrations, and converted to particle mass concentration through multiplication with the
MCF, as described in Section 3.1.4.2. Table 35 and Table 36 present the PM2.5, PMio, and TSP
concentrations as reported by the OPCs. Three to four OPCs were in positions immediately
downwind of the field under study in each sample period, with between one and four OPCs in
upwind locations. Unlike the downwind MiniVol samplers, the downwind OPCs were not
overwhelmed by the dust plumes from the tillage activities - the manufacturer specified range of
the OPC of 0 to 318,000,000 particles/m3 was never exceeded - and thus provided usable data
throughout all sample periods. Upwind OPC time series data were examined for contamination
from activities upwind of the site, such as unpaved road traffic. Contamination was found in six
of the 12 sample periods, with five of those occurring at the 10.0 sample site that was
immediately downwind of an unpaved road (see Figure 7). Large spikes indicative of
contamination were removed from the upwind OPC time series data in these instances to
estimate the background aerosol concentration; the estimated background levels were in very
good agreement with those measured by an OPC at a different, uncontaminated upwind location
(see Table 37 and Table 38).
Data completeness for the OPC datasets was calculated as a ratio of the number of valid samples
per sample period over the possible number of valid samples and expressed as a percentage. Data
completeness was less than 100% due to communication errors between the OPCs and the
computer logging the data, resulting in lost packets of 20 second sample data. Communication
error frequency was variable between OPCs and across time. Data completeness per sample
period ranged from 81.8% to 100.0%, and averaged (± la) 97.4 ± 3.7%.
SDL/08-556
California 2008 Tillage Campaign
56
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The distribution of Met One optical particle counters surrounding the fields of interest at two
heights provided the ability to examine particle emissions by number and size at several
locations, as well as to monitor the PM concentration time series. Examples of particle volume
size distributions measured during tillage operations, in units of |im3/cm3/|im, are presented in
bar graph form in Figure 29 (dV/dd is the change in aerosol volume concentration normalized by
the change in particle diameter). Each graph shows the background particle volume distribution,
the volume distribution of aerosols downwind of the source, and the difference between the two
that is the volume distribution of the emitted particles. Examining these data based on volume
concentration causes the large particles to have a more visible effect than if number
concentrations were examined. However, viewing the data in this way is advantageous because it
is analogous to mass: the relative shape of and difference between the curves remains the same
while the scale changes as the transition to mass concentration is made. Therefore, the given
graphs show that the greatest volume (and mass) of emitted aerosols is in the large particle range
above a diameter of about 2.5 jim. As seen in the reported PM sampler levels, the greatest
contribution by tillage activities was in the PMio and TSP measurements.
Examples of PM time series are shown in Figure 30 from both upwind and downwind elevated
locations during the May 19 R2 period. This example was chosen because it demonstrates that
downwind samplers were exposed to extremely high PM concentrations in the tillage plumes,
supporting the conclusion that downwind filter-based samplers adjacent to the activity were
overloaded during this and other sample periods. OPC time series from other periods in which
the filter-based samplers were determined to have been overloaded also show extremely high
concentrations over very short durations of time. Applying contouring software to OPC number
and PMio concentrations measured around the field under study yields the graphs in Figure 31.
Data from the June 25 sample period were chosen for direct comparison with the filter-based
PMio concentrations (Figure 18). The number concentration contour plot presents the number of
particles larger than 1 jam per liter of air; this is the particle size range containing the greatest
volumetric contribution from agricultural tillage. The OPC PMio concentrations were calculated
by multiplying the Vw concentrations by the MCFio for that sample period. Sample locations are
shown by the (+) markers; there were fewer OPC sample locations than MiniVol locations,
leading to a coarser contour map for OPCs. Field edges are approximated by the solid black
lines.
SDL/08-556 California 2008 Tillage Campaign 57
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Conservation: Strip-till
Conventional: Disc 2
Diameter (urn)
Diameter (urn)
Conservation: Plant
2.5
10
I
Diameter (urn)
I
Conventional: Plant
10
I
15
Diameter (urn)
15
Figure 29. Particle volume size distributions measured from upwind (background) and downwind
(background plus emissions) locations, with the difference being the aerosol emitted by the tillage activity: (a)
strip-till operation, conservation tillage method; (b) disc 2 operation, conventional tillage method, (c) plant
operation, conservation tillage method; and (d) plant operation, conventional tillage method.
SDL/08-556
California 2008 Tillage Campaign
58
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500
450
400
350
soo
Upwind OPC, z = 9m
Ul
o 250
c
o
200
150
100
50
0
a)
14:24
15:36
16:48
Time
18:00
19:12
60,000
50,000
Downwind OPC, z = 9m
14:24
19:12
Figure 30. OPC PM time series, created by multiplying the volume concentrations (Vk) by the daily MCF, as
measured at elevated locations in a) an upwind and b) a downwind positions during the May 19 R2 sample
period.
SDL/08-556
California 2008 Tillage Campaign
59
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(#/L)
7600
7200
6800
6400
6000
5600
5200
4800
4400
4000
900 950 1000 1050 1100 1150 1200 1250 1300
E(m)
(H9/m3
200
190
180
170
160
150
140
130
120
110
100
90
80
900 950 1000 1050 1100 1150 1200 1250 1300
E(m)
Figure 31. Contour plots of average OPC a) number concentration (#/L) for particles larger than 1 |im and b)
PM10 concentration (|ig/m3) across the field for the third cultivator pass on June 25.
SDL/08-556
California 2008 Tillage Campaign
60
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4.3.2 Optical To PM Mass Concentration Conversion
A key factor in converting the OPC and lidar data from number density (volume) data to mass
concentration is the derivation of the MCF, which was described in Eq. 14. The daily average
MCF from all locations with valid collocated OPC and MiniVol samples are shown in Figure 32
with the corresponding 95% confidence interval (CI) about the mean. As OPC data collected at
each site and during each sample period passed QA/QC, the calculation of MCF values was
dependent solely on the presence of a valid filter-based PM measurement. While the near-source
downwind filter-based samples for sample periods May 18 through May 20 R2 were determined
to have been overloaded, and thus prevented emission rates being calculated for those operations,
the upwind and far-source downwind samples were not compromised and were used in
calculating the daily MCF values.
Day-to-day variation in the MCF is not well understood. Potential factors leading to this
variation include, but are not limited to: 1) changes in background aerosol sources, composition,
and particle shape that affect the OPC/MiniVol relationship between particle
detection/separation; 2) different treatments of particles larger than the sampled PM size k by the
two methods. If a particle larger than the cut-off size k is present on the filter at the time of the
post-sample weighing, its mass is included and, with sufficient numbers of large particles, would
lead to higher-than-actual PM,t concentrations. The collocated OPC counts particles larger than k
in their respective size bins; therefore, they are not integrated into the V^ value like in the PM,t
concentration calculation. Thus, a higher-than-actual PM,t concentration would be divided by the
actual Vk, leading to a higher-than-actual MCF value. The second potential factor would be most
influential in cases where the PM samplers had been overloaded. However, previous efforts have
removed filter-based samples known or suspected to have been overloaded from emission rate
and MCF calculations.
45
40 -
35 -
30 -
25 -
20 -
15 -
10 -
5 -
0
•PM2.5 PM10 TSP
5/17 5/18 5/19 5/20 6/5 6/7 6/11 6/18 6/25 Overall
Date
Figure 32. Average daily measured MCF values with error bars representing the 95% confidence intervals.
SDL/08-556
California 2008 Tillage Campaign
61
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Most of the PMio and TSP MCF values were within the expected range of 1.0 to 2.5 g/cm .
However, the PM2.5 MCF values were much larger than expected, with individual values ranging
from 3.2 g/cm3 to 28.2 g/cm3. The mean ± la was 14.6 ± 3.7 g/cm3 and the median was 10.1
g/cm3 with 25% and 75% quartiles of 6.1 and 17.7 g/cm3. As previously stated, calculating the
MCF is a simplified method to account for several complex and possibly interdependent
variables that affect how an aerosol mixture is measured/detected based on both optical and
aerodynamic properties. Some of the known aerosol property relationships that are incorporated
into the single MCF value are particle density (p), dynamic shape factor (%), index of refraction
(n), and porosity. In past field campaigns, PM2.5 MCF values have generally been slightly higher
than PMio and TSP values, but the PM2.5 MCF values calculated from the MiniVol and OPC data
collected during this field campaign are generally much higher than those seen before in our own
unpublished work. Due to the non-physically large MCF values for PM2.5, the calculated PM2.5
MCF values were not used to convert the lidar volume concentrations to mass concentrations.
By way of example, the density of Ni is 8.9 g/cm3 and Hg is 13.5 g/cm3, while the average
observed PM2.5 MCF value during this campaign is 14.6 g/cm3. There is no conceivable way
that this number is correct. Instead, the average soil particle density of 2.65 g/cm3 given in the
NRCS National Soil Survey Handbook was used as a constant PM2.5 MCF for all sample periods
[56]. Table 17 presents the MCF values (± 95% confidence interval where applicable) used to
convert OPC and lidar volume concentration data to mass concentration for this study.
Table 17. Mass conversion factors (g/cm3) used to convert optical particle measurements to mass
concentrations for each day and averaged for the whole campaign. Error values represent the 95%
confidence interval for n>3.
Date
May 17
May 18
May 19
May 20
June 5
June 7
June 11
June 18
June 25
All
PM
Avg
2.65
2.65
2.65
2.65
2.65
2.65
2.65
2.65
2.65
—
2.S
n
—
—
—
—
—
—
—
—
—
—
PM10
Avg ± 95% CI
2.6 ±1.3
1.6 ±0.3
1.7 ±0.3
1.6 ±0.5
1.8 ±0.3
1.5 ±0.3
4.3 ±1.2
1.8 ±0.5
2.0 ±0.3
2.1 ± 0.3
n
9
2
5
5
5
5
4
6
6
49
TSP
Avg±95%CI
4.4 ±4.0
.6±0.1
.6 ±0.3
.4 ±0.2
.5 ±0.2
.4 ±0.2
2.9 ±0.5
2.3 ±1.0
2.2 ±0.6
2.3 ±0.7
n
7
3
8
4
2
4
4
4
5
44
A possible correlation with the high PM2.s MCF values was found when sample period averages
were compared against the length of each sample period, as seen in Figure 33. The highest
average MCFs for all size fractions were found during the shortest sample periods. It is unclear
what this correlation may mean, but it is not likely a cause as sampler operation is assumed to be
independent of sample period length.
SDL/08-556
California 2008 Tillage Campaign
62
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•PM2.5 PM10 — TSP -• -SampleTime
60
-------
Upwind PM-
1J:09 14:24 13:38 14:52 15:07
200
150
100
60
Upwind PMW,6/18
14:09 14:24 14:38 14:52 15:07
Urn
up*ind TSP. em
14:09 14:24 13:38 14:52 15:07
Figure 34. PM2.S, PM10, and TSP mass concentrations retrieved from collocated lidar and OPC during the
'stare' time series for 6/18. Data acquisition time of the lidar data is 0.5 s while OPCs were set to 20 s
accumulation time. Measurements were done on the upwind side of facility (location Tl)
Table 18. Comparison of PM mass concentrations (jig/m3) as reported by MiniVol samplers and mean values
measured by collocated OPCs and lidar at Tl (upwind) and T2 (downwind) for 6/18/2008.
Upwind
PM sampler (Tl)
Upwind PM sampler
average ± lo
OPC (Tl) ± 95% CI
Lidar ± 95% CI
Downwind
PM sampler (T2)
Downwind PM sampler
average ± lo
OPC (T2) ± 95% CI
Lidar ± 95% CI
PM2.5 (jig/m3)
30.1
29.1 ± 1.3
(n = 2)
4.9 ±0.06
5.1 ±0.09
63.2
48.1 ±12.8
(n = 5)
6.4 ±0.5
6.3 ±0.1
PM10 (jig/m3)
56.5
62.6 ±8.5
(n = 2)
48.2 ±1.1
50.8 ±1.3
87.5
262.6 ±153.9
(n = 5)
93.0 ±15.1
68.1 ±2.1
TSP (jig/m3)
195.4
214.5 ±27.0
(n = 2)
185.4 ±8.5
200.0 ± 6.7
—
1696.0 ±284.1
(n = 2)
442.3 ±95.2
284.5 ±10.2
SDL/08-556
California 2008 Tillage Campaign
64
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A similar comparison of OPC and lidar time series data measured from downwind of the tillage
field is shown in Figure 35. The spikes of the concentrations (especially of PMio and TSP) are
due to the dust plume generated by tillage operations crossing this OPC. Spikes are rarely
observed on the upwind side of the field (upwind spikes are associated with road traffic). As a
quality check, comparison analysis of collocated lidar, OPC, and MiniVol data was done for all
days of the campaign. In general, the OPC and lidar data averaged for the PM sampler
acquisition time are in agreement with mass concentrations measured by the collocated PM
samplers. Discrepancies between point-source instruments (PM samplers and OPC) and lidar are
due to inherently different measurement techniques. Point instruments capture particle
concentration at a single point (a few cm3) with a small volumetric flow (1.6 x 10~5 to 8.3 x 10~5
m3/s). The lidar acquires information in a volume of ~ 3 m3 for each bin along the laser beam for
each sample (1-2 samples per second in this experiment), and thus can capture a spatially
variable plume while the plume may miss the point sensors. The best agreement is observed
when the lidar is compared with PM sampler data averaged over several locations along the up-
or down-wind side of the tillage field. For similar reasons, using MCF values averaged over the
whole campaign yields larger discrepancies between collocated lidar and OPC/PM sampler PM
concentration data than daily averaged MCF values. Based on these observations, a daily
averaged MCF is used for conversion of lidar 'staple' data used for flux and emission rate
calculations.
300
250
"g 200
B
o 150
g 100
E
O
O
50
DownwindPM25,6718
Downwind PM10,6/18
0 *++#
,U~*
14:09
14:24
14:38
Time
14:52
15:07
3500
t-
B2500
o 2000
•
| 1500
u
° 1000
500
0
• Lidar
*
•
t
I ll
1J JJLijJjfci
t
•
i-lll *
i^JlottyiLk
ft ** * P • M ^ •flffi fciVP1'' -j- — — ,. ^^- ^^^,,
14:09 14:24 14:38 14:52 15:07
Time
20000
15000
10000
5000
Downwind TSP, 6(18
•MM^f
14:09
14:24
14:38
Time
14:52
15:07
Figure 35. PM2.S, PM10, and TSP mass concentrations retrieved from collocated lidar and OPC during
'stares' at downwind locations. Data acquisition time of the lidar data point is 0.5 sec while OPCs were set to
20 sec accumulation time. Measurements were done on the downwind side of field (location T2) on 6/18/2008.
SDL/08-556
California 2008 Tillage Campaign
65
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4.3.3
Lidar Aerosol Concentration Measurements
Lidar data was collected throughout all sample periods, except for the Cultivator 3 pass
monitored on June 25 due to an equipment failure after the previous measurement. All lidar
scans collected during times when no tillage activity was occurring, based on detailed field notes,
were removed from further calculations. The remaining scans were visually checked to remove
scans with data acquisition errors and to prevent the use of data that was contaminated by other
sources; sources of observed contamination were vehicular traffic on unpaved roads, agricultural
activities immediately upwind, activities associated with the adjacent dairy, and windblown dust.
Contaminated upwind scans, as well as the corresponding downwind scans, were removed from
further emission rate calculations. In most cases two or more downwind scans use the same
upwind scan as a reference because multiple downwind scans were made for each upwind scan.
Downwind scans were not used in further calculations if the corresponding wind direction was
outside of ± 70° from perpendicular to the downwind lidar beam path; if the lidar scan contained
apparent plumes from an outside source (such as from unpaved road traffic or the dairy); if no
plumes were detected; or if the tillage plume had a potentially significant portion crossing the
lidar beam closer than 500 meters down beam. In addition, upwind scans were invalidated if
none of the associated downwind scans were usable. Light wind speeds (< 1 m/s) were recorded
during portions of some measurement periods. Light winds challenge the measurement system
and resulted in additional invalidation of some lidar scans. The total number of lidar scans made
along both upwind and downwind planes is presented in Table 19, along with the number of
valid scans, i.e. those that passed all quality checks, and the percent of valid scans compared to
the total number of scans.
Table 19. Total number of upwind and downwind vertical lidar scans and the number of those scans
determined to be valid for emission rate calculations. No lidar data exists for the 6/25 run because of
instrument problems after the 6/18 run.
Date
5/17 Run 1
5/17 Run 2
5/18
5/19 Run 1
5/19 Run 2
5/20 Run 1
5/20 Run 2
6/5 Run 1
6/5 Run 2
6/7
6/11
6/18
6/25
Operation
Strip-till
Break down borders
Chisel
Disc 1
Disc 2
Lister
Build ditches
Break down ditches,
Cultivator, Roller
Plant
Plant
Herbicide Application
Fertilizer Application
Cultivator
Field
5
4
4
4
4
4
4
4
4
5
5
4
4
Upwind Scans
Total
62
20
122
103
150
126
42
90
36
58
24
20
—
Valid
22
6
92
20
19
17
5
56
25
33
0
10
—
% Valid
35.5
30.0
75.4
19.4
12.7
13.5
11.9
62.2
69.4
56.9
0.0
50.0
—
Downwind Scans
Total
118
60
366
130
148
126
42
259
114
162
48
40
—
Valid
86
14
276
56
31
53
15
169
73
86
0
14
—
% Valid
72.9
23.3
75.4
43.1
20.9
42.1
35.7
65.3
64.0
53.1
0.0
35.0
—
In several cases, the percent of valid scans was very low. Although wind and background
conditions were good during the herbicide application on 6/11, none of the scans taken were
deemed valid because the mass difference between the upwind and downwind scans was below
the minimum detection level (MDL). This means that the operation didn't produce plumes
significant enough to be detected by the lidar; this operation was performed by a very small
SDL/08-556
California 2008 Tillage Campaign
66
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tractor pulling a small spraying apparatus - the only disturbance of the ground was due to
moving tires. Plumes of insufficient concentration differences from background levels led to
downwind scan invalidation in many other instances also. The dairy pen areas adjacent to Field 4
proved to be sufficient PM sources such that lidar scans showing dust plumes passing over the
pens prior to crossing the scanning plane were nearly always invalidated. Additionally,
windblown dust was entrained off of both Field 4 and upwind field surfaces during both the May
20 sample periods. All of these factors combined to significantly decrease the number of valid
lidar scans available for emissions estimation from most operations.
Examples of the lidar-derived PM concentration have been presented in Figure 34, Figure 35,
and Table 18. Additionally, upwind and downwind plume area average volume concentrations
used in the flux calculations are shown in Figure 36 and Figure 37. The two top panels show the
profile-averaged wind speed and direction values used in the flux calculation, with the third and
fourth panels showing the area-averaged volume concentrations measured upwind (Cu) and
downwind (Co) in |j,g3/cm3 (see Eq. 17). It was assumed that the upwind PM concentrations
would be more uniform than the downwind PM, therefore, more downwind scans were made
than those of the upwind conditions. In Figure 36 and Figure 37, the gaps in the upwind
concentration are due to this sampling plan.
4.4 FLUXES AND EMISSIONS RATES
Emission rates for the observed tillage operations were calculated using lidar measurements and
measured PM concentrations coupled with model-predicted concentrations via inverse modeling.
During the May 20 R2 sample period, strong ground-level winds were measured throughout the
entire 1-hr sample time; wind-blown dust plumes originating from the field of study were visible,
often impacting both ground-level and elevated downwind samplers, and recorded by field
personnel. Due to the size and frequency of these wind-blown dust plumes in comparison to the
strength of the monitored operation (building irrigation ditches and field edge borders), any
emission rate derived from data collected during this sample period does not accurately represent
the operation's actual impact on downwind PM concentrations. In addition, the same operations
of building and breaking down of the ditch and field-edge borders around Field 5, the
conservation tillage field, were carried out but not measured. Therefore, the emission rate for
building ditches and field-edge borders before irrigation and breaking them down after irrigation
and before planting was omitted from summations of the emission rates within each investigated
tillage method.
SDL/08-556 California 2008 Tillage Campaign 67
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ID
Wind Speed 10
(m/s)
D
Wind Dir. 50
-50
Upwind 1000
Cone.
3 , 3 1 00
10
Downwind 1000
Cone.
, 3, 3l 100
(iimj/cmj)
10
C
Figure 36. Wind speed, \
concentrations, for the IV
10
Wind Speed
(m/s) 5
0
60
Wind Dir
(cleg) 0
-60
Upwind 1000
Cone 10Q
(iimj/cmj) 1Q
Downwind 1000
c°nc- 100
(u^/cm3) 1Q
r
Figure 37. Wind speed, \
concentrations for the 1V1
TSP *
O PM10
, DM * **J
**< 2.5 *
i i
* * **%*%**** *** * *
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) 10 20 30 40 50 60 70 80 90
Sample Number
dnd direction, upwind and downwind plume area average particulate volume
lay 17, 2008 strip-till pass of the conservation tillage method.
TSP
* + PM2.5 *"**
i
* *„* +*++*+
xxx xxx
1+1 1 1 1 I-H-H-
^£
^4--f++++i-'1~r-
)-+++ ++-
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) 10 20 30 40 50 60
Sample Number
dnd direction, upwind and downwind plume area averaged particulate volume
ay 19, 2008 first disc pass of the conventional tillage operation.
SDL/08-556
California 2008 Tillage Campaign
68
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4.4.1
Lidar Based Fluxes and Emission Rates
The combination of 'staple' and 'stare' measurements from the upwind and downwind sides of
the field, as described in Section 3.1.5, were performed continuously during each tillage
operation of the field campaign except the last one. A critical component failure prevented us
from further using the lidar during the June 25 sample period. Figure 38 and Figure 39 show
calculated net fluxes for sequential lidar measurements taken during the strip-till pass in the
conservation tillage method on 5/17/2008 and the initial discing pass of the conventional tillage
operation on 5/19/2008, respectively. The net mass flux through the lidar's vertical scan is the
product of the plume area averaged volume concentration difference (Co-Cu) multiplied by the
daily average MCF and the component of the wind velocity that is perpendicular to the lidar
beam. It is then normalized by the total area tilled and the ratio of the sample time to total tractor
time to yield the emission rate per unit area tilled per unit of operation time.
Net fluxes were calculated using up- and downwind concentration measurements averaged over
each vertical scan with average wind information for the time of the individual scan. Single-scan
differences do not account for accumulation or depletion in the measurement box due to wind
speed variation during a scan, incoming background variation, or storage in/flushing of the flux
box due to the existing large scale wind eddy structure (i.e. we do not attempt to measure the
same air mass at the upwind and downwind scans). The process is assumed to be a continuous
emission source, requiring several scans to achieve a meaningful mean estimate of the facility
emission. For calculation efficiency, the net flux was calculated through the downwind surface
first and then the upwind flux, using the difference in upwind/downwind fluxes rather than
difference in concentrations.
2000
•s
20
30
40 50
Sample Number
60
70
80
Figure 38. Lidar-derived fluxes Og/m2/s) of PM2 s, PM10, and TSP for the May 17,2008 strip-till pass of the
conservation tillage method.
SDL/08-556
California 2008 Tillage Campaign
69
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4000
20
30
Sample Number
40
50
Figure 39. Lidar derived fluxes (jig/m2/s) of PM2 s, PM10, and TSP for the May 19,2008 first disc pass of the
conventional tillage operation.
The plume area was chosen manually by observing each downwind scan and highlighting the
area to be included in the calculation. Choosing an area that fully includes the source plume but
not a lot of extra area has two advantages: the primary is that portions of the upwind/downwind
scans that would clutter the plume can be removed; secondarily, it minimizes the required
computational resources used by not calculating flux values for pixels which do not contribute
significantly to the overall tillage process.
Using the series of flux measurements collected during each tillage operation, similar to those in
Figure 38 and Figure 39, the mean emissions of PM per unit area tilled per second of tillage
operation were calculated for all days and are shown in Table 20 with respective 95% confidence
intervals. Emission rates reported for multiple operations occurring in the same run are the
average amount of PM emitted per tillage pass and does not represent measured a emission rate
from individual operations. The breaking down of borders and ditches operation in the June 5 Rl
sample tilled the same 4% of the total field area each pass, so the effects of the greater number of
passes is spread over a much smaller area than the majority of other tillage operations. The
summed emission rate for the conventional tillage method includes the following thirteen
implement passes listed in order of occurrence: two break down in-field borders passes (emission
rate spread over entire field area, not just the area tilled), a chisel pass, two disc passes, a lister
pass, two cultivator passes, a roller pass, a planter pass, a fertilizer injection pass, and two
additional cultivator passes. The summed emissions do not include the emissions calculated for
the building of ditches and borders on May 20. While the last two cultivator passes were not
monitored using the lidar, they have been included in the summed emissions because they are
standard weed control operations in the conventional tillage sequence at this production site. The
measured emission rate from the June 5 Rl sample period with the plant, cultivator 1 2, and
roller operations was used for the emission rate of the cultivator 3 and 4 passes. Each of the
cultivator and roller passes were assumed to have the same emissions and variance as was
SDL/08-556
California 2008 Tillage Campaign
70
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reported for the June 5 Rl sample period. Likewise, the planter and lister were assumed to have
the same emissions as reported for the June 5 R2 and May 20 Rl periods, respectively.
Table 20. Mean fluxes (jig/m2/s) ± 95% confidence interval from quality controlled samples for each tillage
operation.
Operation (date)
Valid lidar
samples with
detectable
Amass («)
PM25
Oig/m2/s)
PM10
Oig/m2/s)
TSP
Oig/m2/s)
Conservation Tillage Method
Strip-till
Plant
Herbicide Application
86
86
0
Summed Emissions
2.5 ±0.6
3.6 ±1.1
-------
technique, conventional or CMP, was calculated as the sum of the variances of each operation.
The 95% confidence interval was then calculated, again assuming a Gaussian distribution.
The flux data presented in Table 20 were multiplied by the total tractor operation time to yield a
total mass emitted per unit area of the field for each operation to calculate emission rates
presented in Table 21. The same thirteen passes in an identical configuration were used to
calculate the summed emissions for the conventional tillage method.
Table 21. Aerosol mass transfer (± 95% confidence interval) from each field (flux normalized by operation
duration and area tilled) as calculated from lidar data for all tillage operations.
Sample
Operation
#of
passes
PM25
(mg/m2/
operation)
PM10
(mg/m2/
operation)
TSP
(mg/m2/
operation)
Conservation Tillage Method
May 17 Rl
June 7
June 11
Strip-till
Plant
Herbicide Applications
Summed Emissions
1
1
1
3
26.9 ±6.1
50.2 ±15.0
-------
4.4.2 Inverse Modeling Calculations
4.4.2.1 ISC/AERMOD dispersion models
The ground level area source dimensions used in the dispersion models were based on the GPS
readings of field perimeter measurements and from the tractor while performing the operation.
The seed emission rate of 8.6 |ig/s-m2 per operation, calculated by averaging across preliminary
emission rates for all operations derived in the 2007 tillage CMP study [7], was applied to all
sources on a per pass basis. In some measurement periods there were multiple passes over the
same area by either the same implement or different implements. The seed emission rate was
therefore adjusted by multiplying by the number of passes to account for the actual total
emissions per operation and sample period. Table 22 lists the tillage operations carried out
during each measurement period, the number of passes performed per operation, the area tilled
per pass per sample period, the percent of the field that was tilled per pass during the
measurement period, and the resulting seed emission rate used in the dispersion models. To
compare the modeled concentrations to field measurements, the measured background PM
concentration must be subtracted from the measured downwind concentration results to yield the
concentration resulting from the source activity.
Note that the area worked by some of the operations was much smaller than the entire area of the
field, with the building and breaking down of borders and ditches on May 20 and June 5 working
less than 4% of the field each. However, these operations required more passes over the same
area to accomplish the task. The total area tilled (area/pass times the number of passes) for
breaking down ditches and borders passes of the June 5 Rl period was 31,008 m2, just 13% of
the total area tilled during the sample period. Therefore, the contribution of this operation to the
average emission rate per pass is thought to be small compared to the other two operations.
In situations in which multiple passes were monitored in the same sample period, the emission
rates were calculated on a single-pass basis and all applications of the emission rates herein
derived should take into account the total number of passes performed over the same area. While
the seed emission rates for operations with multiple passes over the same area are significantly
higher than other operations in the same measurement period that involve just one pass, as found
in the first June 5 sample, the low percentage of the total area tilled combined with lower active
time for such sources, results in the period-averaged, model predicted concentration being
dominated by the longer term, more ubiquitous operation. As the individual effects of each of the
three operations in the June 5 Rl sample on measured concentrations could not be distinguished
in the period-averaged filter sample, the derived emission rates for each measurement period,
using the three seed emission rates shown, represents a conglomerate emission rate. However,
for the June 5 R2 sample period, the derived emission rates are for planting only; the cultivator
and rolling operations were performed in the northern third of the field and, coupled with
meteorological conditions, did not impact most downwind samplers. Therefore, source impacts
at all but one downwind location are due strictly to the planting operation.
SDL/08-556 California 2008 Tillage Campaign 73
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Table 22. Tillage operations, number of passes, area tilled per pass during monitoring period, and the seed
emission rate used for modeling particle dispersion using ISCST3 and AERMOD for each sample period.
Sample
Period
May 17 Rl
May 17 R2
May 18
May 19 Rl
May 19 R2
May 20 Rl
May 20 R2
June 5 Rl
June5R2
June 7
June 11
June 18
June 25
Tillage
Method
Conservation
Conventional
Conventional
Conventional
Conventional
Conventional
Conventional
Conventional
Conventional
Conservation
Conservation
Conventional
Conventional
Tillage
Operation
Strip-till
Breaking down
borders
Chisel
Disc
Disc
Lister
Disc
Build ditches and
borders
Break down
ditches/borders
Cultivate
Roll
Plant
Cultivate
Roll
Plant
Herbicide
application
Fertilizer
application
Cultivate
# of passes
1
2
1
1
1
1
3
N,W,S sides
- 2; E side -
4
W-4;
E-12
2
1
1
2
1
1
1
1
1
Area
tilled/pass
(m2)
90,484
10,112
85,116.1
100,482.5
100,482.5
100,482.5
8,121
N,W,S- 1,795;
E- 1,034
W- 1,548;
E - 2,068
69,953.5
67,644
48,175
25,741
32,404
90,484
90,484
38,126.5
100,482.5
Field portion
tilled/pass
(%)
100.0
10.0
84.7
100.0
100.0
100.0
8.1
N,W,S-1.8;
E-1.0
W-1.5;
E-2.1
69.6
6.5
47.9
25.6
32.2
100.0
100.0
37.9
100.0
Seed
emission
rate (jig/s-
m2)
8.6
17.2
8.6
8.6
8.6
8.6
25.8
N,W,S sides
-17.2;E
side - 34.4
W-34.4;
E- 103.2
17.2
8.6
8.6
17.2
8.6
8.6
8.6
8.6
8.6
ISCST3 concentrations for modeled sample periods ranged from 0.0 to 194.8 ug/m3, with the
highest concentrations typically modeled at a height of 2 m on the southern and western edges of
the tillage area, although the exact location varied slightly with differing wind directions. Figure
40 shows an example of ISCSTS-modeled concentrations for the third pass with the cultivator on
June 25, 2008 of the conventional tillage method with northwest winds. It should be noted that a
10 m x 10m grid of receptors at 2 m agl were employed throughout the near-field modeling
domain, which produces a much smoother and more detailed contour plot than the measured
PMio concentration contour plot seen in Figure 18 and the OPC PMio concentration plot in
Figure 31.
AERMOD modeled concentrations for sample periods besides the May 20 Rl and June 5 R2
samples ranged from 0.0 to 205.1 ug/m3, with the highest concentrations typically modeled at a
height of 2 m on the southern and western edges of the tillage area and at different locations
depending on wind direction. Figure 41 shows an example of AERMOD modeled concentrations
for the same cultivator third pass shown in Figure 40 for ISCST3, with the same 10 m x 10 m
grid spacing.
SDL/08-556
California 2008 Tillage Campaign
74
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ua in'-:
100
90
80
70
60
$0
30
10
Figure 40. ISCST3-modeled results for the third cultivator pass of the conventional tillage operations on June
25,2008 with northwest winds. The area of operations and sampler locations are denoted in black and
contour line numerical values are in jig/m3.
4.4.2.2 ISCSTS-Based Emission Rates
The MiniVol PM2.s, PMio, and TSP concentrations were evaluated for use in calculating
emission rates based on inverse modeling as described in Appendix B, with the verified PM
concentrations shown in Table 33 and Table 34 in Appendix A. OPC mass concentrations were
calculated by multiplying the Vk by the MCF,t for each sample period for use in emission rate
calculations and are presented in Table 35 and Table 36. The OPC data were evaluated for
impacts to upwind concentrations and whether intense plumes from the tillage operations
overwhelmed the downwind OPCs or not for the May 18 through May 20 R2 sample periods.
Manufacturer specifications state the range of the OPC is 0 to 318,000,000 particles/m3.
SDL/08-556
California 2008 Tillage Campaign
75
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100
80
70
30
20
Figure 41. AERMOD modeled results for third cultivator pass of the conventional tillage operations on June
25,2008 with northwest winds. The area of operations and sampler locations are denoted in black and
contour line numerical values are in jig/m3.
Examination of the downwind May 18 through May 20 OPC data found that there were no
significant problems with overloading, allowing these data to be used in emission rate
calculations. Potential contamination of both OPC and MiniVol upwind samples from nearby
activities was investigated using OPC time series data. Some significant events were found.
Upwind filter samples affected by these events had already been removed in the QA/QC
investigation detailed in Appendix B. Measured upwind OPC PM concentrations were adjusted
to represent solely background concentrations by removing high values identified as potential
contamination from background concentration average calculations. OPC PM concentrations
used in emission rate calculations are given in Table 37 and Table 38.
Sample period average upwind MiniVol and OPC PM concentrations in each size fraction for
were subtracted from downwind concentrations to calculate PM concentrations produced by the
tillage activity, with only downwind concentrations greater than the average upwind plus the
SDL/08-556
California 2008 Tillage Campaign
76
-------
calculated 67% confidence interval used to calculate emission rates. These operation-produced
PM concentrations were then compared with the model-predicted concentrations as shown in Eq.
22 and multiplied by the seed emission rate to calculate the observed emission rate at each
location. Emission rates were then averaged across all valid sample locations for each sample
period and are presented in Table 23 in units of mass per unit area tilled per tractor operation
time with 95% confidence intervals when three or more samples were available. Emission rates
presented in Table 24 are in units of mass per unit area tilled, representing the total emissions per
unit area per operation. The 95% confidence intervals reported for the summed emissions per
tillage method were calculated the same way as those for the lidar emission rates.
Emission rates were not calculated using filter data for the following sample periods due to the
near-source downwind samplers being overloaded: May 18, May 19 Rl, May 19 R2, May 20 Rl,
and May 20 R2. Additionally, PM2.5 and PMio filter-based emission rates for May 17 R2 and
PMio emission rates for June 5 R2 were not calculated because the upwind samples were
compromised. All of these sample periods were operations in the conventional tillage field;
therefore, a summation of emissions based on MiniVol data across all conventional tillage
operations was not possible. In addition, some emission rates based on the MiniVol data set are
based on one or two data points, preventing further statistical calculations. However, emission
rates were able to be calculated using at least three OPC PM data points for all of these sample
periods. The emission rates reported for the June 5 R2 sample in the conventional field are for
planting only: the first and second cultivator passes and the roller pass that were being performed
at the same time were shown to impact only one downwind sampler location through
examination of tractor location, average wind direction (320° ± 4), OPC data, and model
predicted concentrations. Data at this location were removed from emission rate calculations.
4.4.2.3 AERMOD-based Emission Rates
Sample period averaged emission rates based on concentrations predicted by AERMOD were
determined using the same techniques described for the calculations of ISCST3-based emission
rates. Emission rates on a mass per unit area tilled per unit time of operation are found in Table
25 and emission rates on a mass per unit area tilled are found in Table 26, with the 95%
confidence intervals calculated the same way as those for the lidar emission rates for periods
with three or more valid downwind samples of each size.
SDL/08-556 California 2008 Tillage Campaign 77
-------
Table 23. Mean emission rates per unit area per unit time (± 95% CI for n > 3) for each operation as
determined by inverse modeling using ISCST3.
Sample
Operation
#of
passes
PM25
(Hg/m2/s)
MiniVol
OPC
PM10
(Hg/m2/s)
MiniVol
OPC
TSP
(Hg/m2/s)
MiniVol
OPC
Conservation Tillage Method
May 17 Rl
June 7
June 11
Strip-till
Plant
Herbicide
Application
Summed Emissions
1
1
1
3
2.7 ±0.5
6.5
12.1
21.3
1.0 ±
0.1
0.3 ±
0.2
0.2 ±
0.1
1.5 ±
0.2
21.8±
4.9
6.7
16.5
45.0
30.7 ±
4.5
8.5 ±
1.8
10.4 ±
4.1
49.7 ±
6.3
108.3 ±
54.1
41.9±
25.7
31.6±
14.9
181.7 ±
61.7
147.9 ±
33.0
43. 3 ±
11.4
30.0 ±
16.9
221.2 ±
38.8
Conventional Tillage Method
May 17 R2
May 18
May 19 Rl
May 19 R2
May 20 Rl
May 20 R2
June 5 Rl
June 5 R2
June 18
June 25
Break down
in-field
borders
(spread over
entire field
area)
Chisel
Disc 1
Disc 2
Lister
Build up ditch
and borders
Break down
ditch, break
down borders,
Cultivator 1 &
2, Roller
Plant
Fertilizer
Injection
Cultivator 3
Summed Emissions
2
1
1
1
1
4,2
12,4,
2,1
1
1
1
13
—
—
—
—
—
—
1.5 ±0.6
6.1 ±0.6
12.6 ±
11.4
0.9 ±0.1
—
0.3 ±
0.1
0.4 ±
0.1
0.8 ±
0.3
1.1±
0.3
1.0 ±
0.1
—
0.2 ±
0.1
0.4 ±
0.2
3.9±
2.9
0.3 ±
0.1
9.4 ±
2.9
—
—
—
—
—
—
3.1 ±1.6
—
102.9 ±
63.1
6.6±3.1
—
24.0 ±
5.3
12.7 ±
6.3
30.7 ±
12.4
53. 1±
18.1
42.5 ±
4.8
—
3.7 ±
1.6
10.0 ±
3.9
116.8
±61.0
9.9 ±
3.8
344.8
±66.1
3.2
—
—
—
—
—
—
33.1
682.7
55.1 ±
8.1
—
288.8 ±
51.6
79.5 ±
27.2
203.2 ±
86.1
310.7 ±
75.8
245.2 ±
42.0
—
18.7 ±
11.3
52.7 ±
19.3
841.5 ±
387.2
65. 9 ±
22.1
2498.2
± 415.5
SDL/08-556
California 2008 Tillage Campaign
78
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Table 24. Mean emission rates per unit area (± 95% CI for n > 3) for each operation as determined by inverse
modeling using ISCST3.
Sample
Operation
#of
passes
PM25
(mg/m2/
operation)
MiniVol
OPC
PM10
(mg/m2/
operation)
MiniVol
OPC
TSP
(mg/m2/ operation)
MiniVol
OPC
Conservation Tillage Method
May 17 Rl
June 7
June 11
Strip-till
Plant
Herbicide
Application
Summed Emissions
1
1
1
3
29.9 ±
5.9
89.6
40.5
159.9
10.7
± 1.6
4.5 ±
2.1
0.6 ±
0.3
15.9
±2.7
239.2 ±
54.1
92.5
55.3
386.9
337.6
±49.0
117.2
±24.8
34.8 ±
13.7
489.6
±56.6
1189.1 ±
593.9
575.6 ±
353.3
105. 8 ±
50.0
1870.4 ±
692.8
1623.7
± 362.0
595.9 ±
157.4
100.4 ±
56.7
2320.0
± 398.7
Conventional Tillage Method
May 17 R2
May 18
May 19 Rl
May 19 R2
May 20 Rl
May 20 R2
June 5 Rl
June 5 R2
June 18
June 25
Break down in-
field borders
(spread over
entire field
area)
Chisel
Disc 1
Disc 2
Lister
Build up ditch
and borders
Break down
ditch, break
down borders,
Cultivator 1 &
2, Roller
Plant
Fertilizer
Injection
Cultivator 3
Summed Emissions
2
1
1
1
1
4,2
12,4,
2, 1
1
1
1
13
—
—
—
—
—
—
40.2 ±
16.4
84.0 ±
8.6
48.8 ±
44.3
12.7 ±
2.0
—
1.1±
0.3
9.5 ±
2.3
13.7
±5.6
18.3
±5.5
17.7
±1.2
—
5.6 ±
3.3
5.1±
2.8
15.1
±
11.2
4.4 ±
1.7
107.1
±
15.5
—
—
—
—
—
—
84. 1±
42.8
—
400. 1±
245.2
94.8 ±
45.5
—
79.5 ±
17.7
282.9
±
140.4
533.3
±
215.9
904.5
±
308.3
775.1
±87.1
—
100.2
±42.2
137.6
±53.5
454.3
±
237.0
142.9
±54.8
3833.0
±
489.9
10.5
—
—
—
—
—
—
454.6
2654.3
796.7 ±
117.6
—
956.6 ±
170.8
1767.8
± 605.0
3533.4
±
1497.6
5290.4
±
1291.0
4475.3
± 766.8
—
498.9 ±
301.7
724.6 ±
265.0
3271.6
±
1505.5
954.4 ±
320.3
24381.5
±
2781.5
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Table 25. Mean emission rates per unit area per unit time (± 95% CI for n > 3) for each operation as
calculated by inverse modeling using AERMOD.
Sample
Operation
#of
passes
PM25
(Hg/m2/s)
MiniVol
OPC
PM10
(Hg/m2/s)
MiniVol
OPC
TSP
(Hg/m2/s)
MiniVol
OPC
Conservation Tillage Method
May 17 Rl
June 7
June 11
Strip-till
Plant
Herbicide Application
Summed Emissions
1
1
1
3
2.7 ±0.5
6.5
12.1
21.3
1.2 ±
0.2
0.4 ±
0.2
0.2 ±
0.1
1.7 ±
0.3
25.1 ±
7.0
6.8
15.6
47.5
36.2 ±
7.3
9.2 ±
1.8
10.2 ±
3.1
55.6 ±
8.1
131.9±
74.0
46.3 ±
27.1
33. 8±
19.8
212.1 ±
81.3
174.6 ±
49.8
47.0 ±
12.1
28.3 ±
14.2
249.9 ±
53.2
Conventional Tillage Method
May 17 R2
May 18
May 19 Rl
May 19 R2
May 20 Rl
May 20 R2
June 5 Rl
June 5 R2
June 18
June 25
Break borders
(spread over entire
field area)
Chisel
Disc 1
Disc 2
Lister
Build up ditch and
borders
Break down ditch,
break down borders,
Cultivator 1 & 2,
Roller
Plant
Fertilizer Injections
Cultivator 3
Summed Emissions
2
1
1
1
1
4,2
12,4,
2, 1
1
1
1
13
—
—
—
—
—
—
1.5 ±0.6
6.1 ±0.6
12.6 ±
11.4
0.9 ±0.1
—
0.4 ±
0.1
0.5 ±
0.2
1.0 ±
0.6
1.2 ±
0.6
0.9 ±
0.1
—
0.2 ±
0.2
0.4 ±
0.2
3.9±
2.9
0.5 ±
0.2
10.4 ±
3.0
—
—
—
—
—
—
3.4 ±1.7
—
102.7 ±
64.5
10.7 ±
4.9
—
25.0 ±
4.6
18.4 ±
6.9
40.5 ±
21.4
56.2 ±
33.1
39.2 ±
4.7
—
4.4 ±
1.7
10.4 ±
3.0
115. 9±
60.9
16.5 ±
5.7
376.8 ±
73.9
4.0
—
—
—
—
—
—
40.4
692.4
90.3 ±
13.7
—
301.7 ±
42.0
93.7 ±
36.2
269.0 ±
143.4
325.7 ±
179.1
226.3 ±
41.9
—
21.6 ±
12.0
54.9 ±
14.1
829.6 ±
376.4
110.5 ±
32.0
2688.5
± 451.2
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Table 26. Mean emission rates per unit area (± 95% CI for n > 3) for each operation as calculated by inverse
modeling using AERMOD.
Sample
Operation
#of
passes
PM25
(mg/m2/
operation)
MiniVol
OPC
PM10
(mg/m2/
operation)
MiniVol
OPC
TSP
(mg/m2/ operation)
MiniVol
OPC
Conservation Tillage Method
May 17 Rl
June 7
June 11
Strip-till
Plant
Herbicide
Application
Summed Emissions for
Conservation Tillage
1
1
1
3
31.7±
5.6
91.5
36.7
159.9
12.7 ±
2.8
4.9 ±
2.2
0.6 ±
0.3
18.1 ±
3.5
275. 8 ±
76.7
93.5
52.1
421.4
397.2 ±
80.4
126.9 ±
25.1
34.1 ±
10.3
558.2
±84.9
1448.3 ±
812.5
636.8 ±
372.8
113.3 ±
66.2
2198.4 ±
896.4
1917.1 ±
547.0
645.9 ±
166.2
94.7 ±
47.5
2657.7 ±
573.7
Conventional Tillage Method
May 17 R2
May 18
May 19 Rl
May 19 R2
May 20 Rl
May 20 R2
June 5 Rl
June 5 R2
June 18
June 25
Break borders
(spread over entire
field area)
Chisel
Disc 1
Disc 2
Lister
Build up ditch and
borders
Break down ditch,
break down borders,
Cultivator 1 & 2,
Roller
Plant
Fertilizer Injections
Cultivator 3
Summed Emissions for
Conventional Tillage
2
1
1
1
1
4,2
12,4,
2,1
1
1
1
13
—
—
—
—
—
—
43.8 ±
11.1
86.9 ±
13.5
50.3 ±
46.7
21.0 ±
3.4
—
1.2 ±
0.3
11. 3±
4.2
18.0 ±
9.9
20.2 ±
9.9
16.3 ±
1.7
—
6.6 ±
4.1
5.2 ±
2.5
15.0 ±
11.2
7.5 ±
2.8
123.1
±20.4
—
—
—
—
—
—
90.3 ±
45.6
—
399.4 ±
250.7
155. 5 ±
71.6
—
83.0 ±
15.4
409.5 ±
154.2
704.7 ±
372.4
957.7 ±
564.2
714.8 ±
86.3
—
116.5 ±
46.0
142.6 ±
40.7
450.5 ±
236.9
239.3 ±
82.3
4374.0
± 752.6
130.6
—
—
—
—
—
—
555.6
2692.1
1307.3 ±
198.0
—
999.1 ±
139.2
2085.4 ±
804.5
4677.8 ±
2492.6
5545.3 ±
3049.5
4131. 1±
764.0
—
578.0 ±
320.8
754.5 ±
193.6
3225.5
±1493.4
1599.8 ±
462.7
27,351.4
± 4438.3
4.5 DERIVED EMISSION RATE COMPARISON
Two emission rate determination approaches were employed to calculate three different sets of
emission rates in order to quantify differences between conventional tillage methods and a
conservation tillage method using strip-till technology. Problems with filter-based PM
measurements prevented emission rate calculations for all size fractions over five consecutive
sample periods and some size fractions during two other periods, preventing the summation of
ISCST3- and AERMOD-based emission rates for MiniVol data across the conventional tillage
SDL/08-556
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81
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method operations. The OPC-based PM measurements passed QA/QC and were used to
calculate emission rates through inverse modeling for all size fractions for all sample periods.
The lidar system effectively sampled the vertical downwind plane and measured time-resolved
plume characteristics for each operation at each paniculate size fraction, except for when a
critical component failure in the lidar system prevented it from collecting data for the last tillage
operation. Sample period average emission rates, with their respective error estimates, are shown
in Figure 42 for comparison when values from all methods are available.
A summary table of the PMio emission rates calculated from each approach is given in Table 27
for comparison. The calculated PMio emission rates demonstrate that for total mass of PMio per
unit area per unit time of operation (|ag/m2/s), the conservation tillage method produced about
10% as much as the conventional method based on lidar measurements. PMio emission rates
calculated by the lidar and the inverse modeling method are within the extent of their 95%
confidence intervals for the May 19 R2, June 5 Rl, and June 18 samples, while differences
between them are statistically significant for the May 17 Rl, May 18, May 19 Rl, and June 5 R2
samples. Another difference is that the filter and OPC-based inverse modeling did report
emission rates for the herbicide application in the conservation tillage field while the lidar
reported upwind and downwind concentration differences less than the MDL. A potential
explanation for this is that the lidar beam was only able to come down to 6-8 m agl for safety
reasons, while most filter and OPC samplers were located below this level. If a plume stayed
close to the ground, the lidar may have been unable to detect it.
The largest difference in derived emission rates is between the model-derived values and the
lidar. This is likely due to the fact that ISCST3 and AERMOD are very similar numerical models
and the lidar measures actual fluxes. The models are limited to the types and configuration of
sources that can be used (point source, volume source, area source, or line source), which means
that a compromise must be made when dealing with a small, moving area source such as is the
case in agricultural tillage. Also, the temporal and spatial resolution of the lidar allows it to see
micro-scale variations in plume characteristics and movement such as plume strength, frequency,
lofting and detachment from the surface, wind direction and wind speed effects.
The models must assume a constant emission from the entire emitting surface area, while the
lidar sees the plume movement as the tractor moves across the field. This kind of microstructure
cannot be captured by the long-term sampling required for implementation of ISCST3 and
AERMOD. Consequently, ISCST3 and AERMOD are incapable of generating fine levels of
spatial and temporal detail. As discussed previously [7], examples of such differences in
technique are evident when comparing single-scan and sample period average PM concentrations
measured by the lidar with average modeled concentrations in a vertical plane. In this work, it is
obvious that the plumes measured by lidar were detected at much higher elevations than
predicted by the models, as observed by the greater than background concentrations measured at
heights up to and exceeding 100 m. Since the point sensors were deployed near the surface (at 2
and 9 m) downwind of the source, these higher plumes were only sampled by the lidar.
Another factor to consider is the exhaust emissions from the tractor engines. The report detailing
the comparison of fall CMP and conventional tillage method measurements and findings [7], the
companion study to the study herein described, calculated exhaust PMio emissions based on fuel
usage information provided by the cooperating producer and a fuel use-based PMio exhaust
emission rate given by Kean et al. [30]. Exhaust emissions in the fall tillage report were
SDL/08-556 California 2008 Tillage Campaign 82
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30
25
c 20
re
•£ 15
fVI •*—>
I ISCSTS-MiniVol
IISCST3-OPC
I Lidar
PM2.5
AERMOD-MiniVol
180
160
140
120
100
80
60
40
20
0
1000
900
800
700
600
500
400
300
200
100
0
I ISCSTS-MiniVol
IISCST3-OPC
I Lidar
PM10
AERMOD-MiniVol
IAERMOD-OPC
COV
I ISCSTS-MiniVol
IISCST3-OPC
I Lid
TSP
AERMOD-MiniVol
IAERMOD-OPC
Figure 42. A comparison of PM2.5, PM10, and TSP emission rates ± 95% confidence intervals derived through
lidar scanning techniques and inverse modeling using ISCST3 and AERMOD dispersion models and
measured PM concentrations.
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Table 27. Calculated PM10 emission rates (± 95% confidence interval) from the lidar and inverse modeling
using two dispersion models.
Sample
Operation
Emission Rates (|ig/m2/s)
Lidar
ISCST3
MiniVol
OPC
AERMOD
MiniVol
OPC
Conservation Tillage Method
May 17 Rl
June 7
June 11
Strip-till
Plant
Herbicide Application
Sum for Conservation Tillage
(mg/m2)
47.7 ±
10.7
12.8 ±
3.8
-------
5. SUMMARY AND CONCLUSIONS
A project to determine the control effectiveness of Conservation Management Practices (CMPs)
for agricultural tillage was funded by the San Joaquin Valleywide Air Pollution Study Agency
and carried out in the San Joaquin Valley of California in May and June of 2008. The study was
conducted to quantify particulate emissions (PM2.5, PMio, and TSP) from conventional
agricultural tillage methods and a conservation tillage CMP utilizing an Orthman 1-tRIPr, a strip-
till implement. The Orthman 1-tRIPr is a tillage implement that incorporates multiple
conventional tillage implements into one piece of equipment and applies these to narrow strips
across the field, as opposed to the conventional tillage method which applies more operations to
the entire surface area of the field. The objective of this study was to address three fundamental
research questions:
1) What are the magnitude, flux, and transport of PM emissions produced by
agricultural practices for row crops where tillage CMPs are being implemented
vs. the magnitude, flux, and transport of PM emissions produced by
agricultural practices where CMPs are not being implemented?
2) What are the control efficiencies of equipment being used to implement the
"conservation tillage" CMP? Jf resources allow assessing additional CMPs,
what are the control efficiencies of the "equipment change/technological
improvements" CMP?
3) Can these CMPs for a specific crop be quantitatively compared, controlling for
soil type, soil moisture, and meteorological conditions?
The study was carried out from May 15 until June 25, 2008 in the San Joaquin Valley of
California on a commercial production farm. Two adjacent fields with the same crop and similar
soil properties were selected for observation. The study investigated conventional 13-pass spring
tillage sequence for a field going from winter wheat to corn was: in-field irrigation border
breakdown (x2), chisel, disc (x2), lister, cultivate (x2), top roll, plant, fertilizer injection, and
cultivate (x2). The conservation tillage CMP sequence consisted of three passes: strip-till,
plant/fertilize, and herbicide spray. Both fields also required passes over very small areas (<5%
of total field area) to build and break down ditches and field-edge borders, but this step was only
measured in the conventional tillage application. Particulate emissions were determined using
arrayed filter and optical-based sampling coupled with inverse modeling, using both ISCST3 and
AERMOD, as well as advanced scanning lidar techniques. Supporting operation characteristics
(operation time, number of tractors in operation, potential contamination issues, etc.) were
recorded and meteorological, soil characteristic, and particle chemical composition
measurements were made and are reported in this document.
Overloading of downwind filter-based samplers and contaminated upwind samplers prevented
emission rates from being calculated through inverse modeling for some or all PM size fractions
during seven sample periods. The collocated OPCs were found to have not been overloaded, thus
allowing emission rates to be calculated from mass converted OPC data for all sample periods.
The scanning lidar technology employed was able to calculate emissions for all but one
measurement period.
Table 28 summarizes PMio aerosol emission values found during this study along with results
from previous studies found in the literature in units of mass emitted per unit area tilled. The
summed emissions for the conventional tillage method based on lidar data includes the following
SDL/08-556 California 2008 Tillage Campaign 85
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13 passes in order: two break down in-field border passes, chisel, two disc passes, lister, two
cultivator passes, roll, plant, fertilizer injection, and two more cultivator passes. The summed
conservation tillage sequence consists of the following three passes: strip-till, plant, and
herbicide application.
Some of the values herein reported are in agreement with those reported by Flocchini et al.
(2001) and Madden et al. (2008), as well as the PMio emission factors used by CARB, such as
the strip-till and plant passes in the conservation tillage method and the cultivate and roll passes
in conventional tillage [12][16][13]. Other emission rates are different and larger than previous
values reported in the literature, especially the discing 1 and 2, chisel, and lister passes of the
conventional tillage, though those derived through inverse modeling are below the estimated
95% level for their respective distributions as presented in the uncertainty calculations in this
report. The lidar-derived emission rates for the disc 1, disc 2, chisel, and lister passes are high
when compared to values found by inverse modeling coupled with OPC PM data, values in the
literature for the same operations, and values reported in the 2007 fall CMP tillage study which
used the same lidar methodology. These relatively high emission rates provide indirect support
for the conclusion that downwind PM samplers were likely overloaded during those sample
periods by high aerosol concentrations. While the values from listed published studies are
generally not in close agreement, they are relatively well fit by lognormal or Weibull
distributions, which have previously been shown to represent emissions factors datasets well
[31]. In addition, they are within the range of the variability expected from measurements made
under different meteorological and soil conditions, as demonstrated by the wide range of values
from Flocchini et al. (2001) [12] summarized in Table 2. The results from this campaign are not
in as good agreement as previous results have been [7]. In general our results are larger than their
corresponding literature values.
Emission rates for PM2.5, PMio, and TSP from both lidar data and inverse modeling coupled with
OPC data by operation are presented in Table 29 in units of mass emitted per unit area tilled per
operation pass, along with the average tillage rate in hours per hectare where the hour represents
total tractor operation time. In the case where two tractors were operating at the same time for
the duration of the sample period, the total tractor operation time would be twice that of the
elapsed time of the operation. Comparisons were made between the conventional and CMP
tillage practices based on these variables and are shown in the same table. The conservation
tillage method produced 9.5% as much PM2.5, 6.3% as much PMio, and 9.1% as much TSP as the
conventional method according to the lidar data and 14.7% as much PM2.5, 12.8% as much PMio,
and 9.7% as much TSP as the conventional method according to the AERMOD-OPC
combination. Therefore, the control efficiency of the CMP for particulate emissions from these
three data sets was as follows: lidar - 0.91, 0.94, and 0.91 for PM2.5, PMio, and TSP,
respectively; and AERMOD-OPC - 0.85, 0.87, and 0.90 for PM2.5, PMio, and TSP, respectively.
The control efficiency (r\) was calculated according to Eq. 23, which was based on a collection
efficiency equation found in Cooper and Alley (2002)
1= ECT~£ST (23)
hCT
where ECT is the calculated emission rate for the conventional tillage method and EST is the
calculated emission rate for the strip-till conservation tillage method [58]. These differences in
SDL/08-556 California 2008 Tillage Campaign 86
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total emissions per tillage treatment are significant at the 99.99% level for PM2.5, PMio, and TSP
for both emissions calculation methodologies employed.
Table 28. A comparison of PM10 emission rates herein derived and found in literature.
Operation
Flocchini
etal.
(2001)
[12]*
(mg/m2)
Madden
etal.
(2008)
[16]*
(mg/m2)
CARB
(2003a)
[13]
(mg/m2)
Previous
SDL
CMP
Study:
Lidar
m*
(mg/m2)
This study
Lidar
(mg/m2)
ISCST3
MiniVol
(mg/m2)
OPC
(mg/m2)
AERMOD
MiniVol
(mg/m2)
OPC
(mg/m2)
Conservation Tillage Method
Strip-till
Plant
Herbicide
Application
Conservation
Method Sum
180.5
233.8
523.8 ±
117.8
175.6 ±
52.6
-------
Table 29. Participate emissions, from Lidar data and inverse modeling with OPC data, and tillage rate
comparison between conventional and conservation tillage.
Average Emission Rates ± 95% CI (mg/m2)
PM2.5
Lidar
Conservation Tillage Method
Strip-till
Plant
Herbicide
Application
Sum
26.9 ±
6.1
50.2 ±
15.0
-------
The time per hectare required to perform the CMP work was 16.3% of the conventional method,
similar to the percent difference between PM emissions of the two treatments. It is interesting to
note that while the absolute values of calculated emissions are different between the two
techniques, the control efficiencies are very close to each other. Also, the control efficiencies are
similar across the three size fractions. It should be noted that other reductions in emissions
between the two tillage management practices are likely to have occurred, mainly due to
decreased fuel usage in tractor engines. Unlike the previously conducted companion study, fuel
usage was not quantified in this study and prevented accounting for the associated reductions in
emissions. However, these reductions are expected to be similar to the reduction in tractor
operation time (-85%).
Our lidar measurements indicate comparable levels of suspended particulate as is predicted by
our modeling. Previous results supported the use of lidar measurements as an important
complement to ground-based sensors because ground-based sensors cannot measure elevated
plumes and ISCST3 and AERMOD would realize significant benefit if lidar-derived information
could be incorporated into their calculations. The present results are consistent with that claim as
during this campaign we were able to generate a more consistent picture of the emission rates
using both technologies.
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6. LESSONS LEARNED
The sampling difficulties encountered during this field study, specifically concerning filter-based
measurements, prompt us to provide a list of practices and procedures that can mitigate these
problems in future field measurements. There are several lessons to be learned from the
experiences of this study.
1) Each kind of instrument has a specific operational range in terms of concentration, mass,
temperature, etc. Therefore, adequate accommodations must be made during the planning
stages of the field exercise such that each instrument can operate within its specified
range. In this study, PM2.5 and PMio MiniVol samplers were very likely overwhelmed by
concentrated plumes from the tillage operations during five sample periods. To prevent
overwhelming the samplers in future field measurements, they can be moved further
away from the source, thereby allowing more time and distance for dispersion of the
plume and a decrease in concentration.
2) Frequent (e.g. daily or even immediately prior to each sampler period) inspection of
instrument condition is important and may provide early warnings of potential problems,
especially in high concentration conditions. During this experiment large crustal particles
and organic matter were found in the sample head assembly, pointing to potential
problems with the impactor, overloading of the sampler, and/or contamination. In
addition, very heavy cumulative particle loading of the silicon vacuum grease used in the
impactor assembly to prevent particle bounce and/or re-entrainment was observed after
the May sample periods. Heavy loading of the grease may increase the probability of
particle bounce and/or re-entrainment into the air stream. These two observations lead us
to the conclusion that extreme vigilance regarding instrument condition is required at all
times - the MiniVols shall not be considered "set and forget" instruments. In the present
case, had we performed a more thorough daily inspection of the sampler heads we would
have been able to take corrective action earlier. One example of corrective action is, in
the case of PIVb.s samplers, is to install a PMio impactor upstream of the PIVb.s impactor
head to act as an additional large particle filter and decrease the overall loading on the
PM2.5 impactor. Another preventative action is to clean and regrease the MiniVol
impactor assemblies after each use if exposed to high aerosol concentrations.
3) Filter handling and storage must occur in a clean environment, with potential
contamination factors minimized. During this experiment, filter handling was performed
onsite in a trailer. While it was maintained as clean as possible, there were high winds
with blowing dust on May 20 that may have been a source of contamination on the filters
being handled. If an adequately clean onsite filter processing location cannot be
identified, an appropriate solution might be to transport the impactor heads to an offsite
location for processing (such as a hotel room).
Careful application of these lessons learned will help prevent future problems similar to those
encountered during this field campaign. However, constant evaluation of actual conditions for
potential problems is strongly encouraged, with proper mitigation promptly employed.
In addition to the above lessons learned, peer review of this report also brought to our attention
that measurements of additional parameters should be considered in future studies. Specifically,
SDL/08-556 California 2008 Tillage Campaign 90
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it was suggested that soil surface temperature measurements be made. Differences in soil surface
temperature may result in different vertical and horizontal PM dispersion (with all other
conditions being equal) as surface conditions are known to affect turbulence and dispersion.
SDL/08-556 California 2008 Tillage Campaign 91
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7. ACKNOWLEDGMENTS
The Space Dynamics Laboratory would like to thank the individuals and groups whose efforts
made this study and subsequent analysis possible. Cooperators include the USDA-ARS National
Laboratory for Agriculture and the Environment (Dr. Jerry Hatfield, Dr. John Prueger, and Dr.
Richard Pfeiffer), Utah State University (Mark Erupe, Dr. Randy Martin, Derek Price, Emyrei
Reese, Dr. Phil Silva), U.S. EPA (Sona Chilingaryan, Kerry Drake, Ron Myers, Dr. Robert
Vanderpool, and Dr. David J. Williams), the San Joaquin Valleywide Air Pollution Study
Agency, the San Joaquin Valley Ag Technical Group, the San Joaquin Valley Air Pollution
Control District (Jessi Fiero, Sheraz Gill, Ramon Norman, Samir Sheikh, Patia Siong, James
Sweet), California Air Resources Board (Kevin Eslinger, Shelby Livingston, Karen Magliano)
and the cooperative agricultural producers and industry representatives. Individuals from SDL
who participated in the field measurement campaign or contributed to the report include Dr. Gail
Bingham, Bill Bradford, Jennifer Bowman, Eve Day, Carrie Farmer, Eva Gillespie, Cassi Going,
Dr. Allen Howard, Everett Ito, Derek Jones, Tanner Jones, Spencer Kitchen, Richard Larsen,
Christian Marchant, Kori Moore, Brad Peterson, Andrew Pound, Shane Topham, Nathan
Whipple, Dr. Tom Wilkerson, Dr. Michael Wojcik, Cordell Wright and Dr. Vladimir Zavyalov.
SDL/08-556 California 2008 Tillage Campaign 92
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8. PUBLICATIONS
G. E. Bingham, C. C. Marchant, V. V. Zavyalov, D. J. Ahlstrom, K. D. Moore, D. S. Jones, T. D.
Wilkerson, L. E. Hipps, R. S. Martin, J. L. Hatfield, J. H. Prueger, R. L. Pfeiffer. 2009.
Lidar based emissions measurement at the whole facility scale: Method and error
analysis. Journal of Applied Remote Sensing 3(1):033510 [doi: 10.1117/12.829411].
C. C. Marchant, T. D. Wilkerson, G. E. Bingham, V. V. Zavyalov, J. M. Andersen, C. B. Wright,
S. S. Cornelsen, R. S. Martin, P. J. Silva, J. L. Hatfield. 2009. Aglite lidar: A portable
elastic lidar system for investigating aerosol and wind motions at or around agricultural
production facilities. Journal of Applied Remote Sensing 3(1):033511 [doi:
10.1117/12.829412].
C. C. Marchant, K. D. Moore, M. D. Wojcik, R. S. Martin, R. L. Pfeiffer, J. H. Prueger, J. L.
Hatfield. 2011. Estimation of dairy particulate matter emission rates by lidar and inverse
modeling. Transactions of ASABE 54:1453-1463.
K. D. Moore, M. D. Wojcik, C. C. Marchant, R. S. Martin, R. L. Pfeiffer, J. H. Prueger, J. L.
Hatfield. 2011. "Comparisons of measurements and predictions of PM concentrations and
emission rates from a wind erosion event," in Proc. International Symposium on Erosion
and Landscape Evolution (ISELE) 2011, D. C. Flanagan, J. C. Ascough II, and J. L.
Nieber, eds., American Society of Agricultural and Biological Engineers, St. Joseph.
K. D. Moore, M. D. Wojcik, R. S. Martin, C. C. Marchant, G. E. Bingham, R. L. Pfeiffer, J. H.
Prueger, J. L. Hatfield. 2013. Parti culate emissions calculations from fall tillage
operations using point and remote sensors. Journal of Environmental Quality [doi:
10.2134/jeq2013.01.0009].
M. D. Wojcik, G. E. Bingham, C. C. Marchant, V. V. Zavyalov, D. J. Ahlstrom, K. D. Moore, T.
D. Wilkerson, L. E. Hipps, R. S. Martin, J. L. Hatfield, J. H. Prueger. "Lidar based
particulate flux measurements of agricultural field operations," in IGARSS 2008, Boston,
Massachusetts, July 2008.
M. D. Wojcik, K. D. Moore, "Laboratory for Atmospheric and Remote Sensing (LARS)", Wind
Erosion Workshop organized by the Agriculture Research Service - Wind Erosion
Research Unit, Manhattan, Kansas, April 2008.
M. D. Wojcik, R. S. Martin, J. L Hatfield. 2012. "Using lidar to characterize particles from point
and diffuse sources in an agricultural field," in Environmental Remote Sensing and
Systems Analysis, ed. N.-B. Chang, CRC Press, Taylor and Francis Group, Boca Raton,
pp. 299-331.
V. V. Zavyalov, C. C. Marchant, G. E. Bingham, T. D. Wilkerson, J. L. Hatfield, R. S. Martin, P.
J. Silva, K. D. Moore, J. Swasey, D. J. Ahlstrom, T. L. Jones. 2009. Aglite lidar:
Calibration and retrievals of well characterized aerosols from agricultural operations
using a three-wavelength elastic lidar. Journal of Applied Remote Sensing 3(1):033522
[doi: 10.1117/12.833365].
SDL/08-556 California 2008 Tillage Campaign 93
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9. REFERENCES
[1] U.S. EPA. 1995. User's guide for the Industrial Source Complex (ISC3) Dispersion
Models. Research Triangle Park, NC: U.S. Environmental Protection Agency, Office of
Air Quality Planning and Standards Emissions. Monitoring, Analysis Division. January,
2008. http://www.epa.gov/scram001/userg/regmod/isc3v2.pdf.
[2] U.S. EPA. 2005. Revision to the Guideline on Air Quality Models: Adoption of a
Preferred General Purpose (Flat and Complex Terrain) Dispersion Model and Other
Revisions; Final Rule. 40 CFRPart 51. Washington, D.C., U.S. Environmental Protection
Agency. January 2008. http://www.epa.gov/ttn/scram/guidance/guide/appw 05.pdf.
[3] Pope, C.A. 1991. Respiratory hospital admissions associated with PM10 pollution in
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[4] Hinds, W. C. 1999. Aerosol Technology: Properties, Behavior, and Measurement of
Airborne Particles, 2nd Edition. John Wiley & Sons, New York. 233-242.
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[6] 40 CFR 50.7. National primary and secondary ambient air quality standards for PM2.5.
[7] Williams, D.J., Chilingaryan, S., Hatfield, J. 2012. Los Banos, CA Fall 2007 Tillage
Campaign: Data Analysis. U.S. Environmental Protection Agency Report EPA/600/R-
12/734, U.S. Government Printing Office, Washington, D.C. Available:
http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=248752. Date accessed:
March 1,2013.
[8] Holmen, B.A., Eichinger, W.E., Flocchini, R.G. 1998. Application of elastic LIDAR to
PMio emissions from agricultural nonpoint sources. Environmental Science and
Technology 32:3068-3076.
[9] Conservation Management Practices Program Report. January 2006. San Joaquin Valley
Air Pollution Control District for 2005.
[10] Holmen, B.A., James, T.A., Ashbaugh, J.L., Flocchini, R.G. 2001a. LIDAR-assisted
measurement of PMio emissions from agricultural tilling in California's San Joaquin
Valley—Part I. LIDAR. Atmos. Env. 35:3251-2364.
[11] Holmen, B.A., James, T.A., Ashbaugh, J.L., Flocchini, R.G. 2001b. LIDAR-assisted
measurement of PMIO emissions from agricultural tilling in California's San Joaquin
Valley—Part II: Emission factors. Atmos. Env. 35:3265-3277.
[12] Flocchini, R.G., James, T.A., Ashbaugh, L.L., Brown, M.S., Carvacho, O.F., Holmen,
B.A., Matsumura, R.T., Trezpla-Nabalgo, K., Tsubamoto, C. 2001. Interim Report:
Sources and sinks of PMio in the San Joaquin Valley. Crocker Nuclear Laboratory, UC-
Davis, CA.
SDL/08-556 California 2008 Tillage Campaign 94
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[13] California Air Resources Board (CARB). 2003 a. Area Source Methods Manual, Section
7.4: Agricultural Land Preparation.
[14] CARB. 2003b. Area Source Methods Manual, Section 7.5: Agricultural Harvest
Operations.
[15] U.S. Environmental Protection Agency (EPA). 2001. Procedures document for national
emission inventory. Criteria Air Pollutants 1985-1999. EPA-454/R-0 1-006.
[16] Madden, N.M., Southard, R.J., Mitchell, J.P. 2008. Conservation tillage reduces PM10
emissions in dairy forage rotations. Atmospheric Environment 42:3795-3808.
[17] Wang, I, Miller, D.R., Sammis, T.W., Hiscox, A.L., Yang, W., Holmen, B.A. 2010.
Local dust emission factors for agricultural tilling operations. Soil Science 175:194-200.
[18] Kasumba, J., Holmen, B.A., Hiscox, A., Wang, I, Miller, D. 2011. Agricultural
emissions from cotton field disking in Las Cruces, NM. Atmospheric Environment
45:1668-1674.
[19] Clausnitzer, H., Singer, MJ. 1996. Respirable-dust production from agricultural
operations in the Sacramento Valley, California. Journal of Environmental Quality
25:877-884.
[20] Clausnitzer, H., Singer, MJ. 2000. Environmental influences on respirable dust
production from agricultural operations in California. Atmospheric Environment
34:1739-1745.
[21] Baker, J.B., Southard, R.J., Mitchell, J.P. 2005. Agricultural dust production in standard
and conservation tillage systems in the San Joaquin Valley. Journal of Environmental
Quality 34: 1260-1269.
[22] Mitchell, J.P., Southard, R.J., Madden, N.M., Klonsky, K.M., Baker, J.B., De Moura,
R.L., Horwath, W.R., Munk, D.S., Wroble, J.F., Hembree, K.J., Wallender, W.W. 2008.
Transition to conservation tillage evaluated in San Joaquin Valley cotton and tomato
rotations. Journal of California Agriculture 62(2): 74-79.
[23] Veenstra, J.J., Horwath, W.R., Mitchell, J.P., Munk, D.S. Conservation tillage and cover
cropping influence soil properties in San Joaquin Valley cotton-tomato crop. Journal of
California Agriculture 60(3):146-153.
[24] Upadhyaya, S.K., Lancas, K.P., Santos-Filno, A.G., Raghuwanshi, N.S. 2001. One-pass
tillage equipment outstrips conventional tillage method. Journal of California Agriculture
55(5);44_47.
[25] Mitchell, J.P., Munk, D.S., Prys, B., Klonsky, K.K., Wroble, J.F., De Moura, R.L. 2006.
Conservation tillage production systems compared in San Joaquin Valley cotton. Journal
of California Agriculture 60(3): 140-145.
SDL/08-556 California 2008 Tillage Campaign 95
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[26] U.S. EPA. March 17, 2008. NONROAD Model (nonroad engines, equipment, and
vehicles). Accessed: August 21, 2008. Available:
http://www.epa.gov/otaq/nonrdmdl.htm#techrept.
[27] U.S. EPA. 2004. Exhaust and Crankcase Emission Factors for Nonroad Engine Modeling
- Compression-Ignition. EPA420-P-04-009. April 2004.
[28] U.S. EPA. 1991. Nonroad Engine and Vehicle Emission Study - Report. EPA 460/3-91-
02. November 1991.
[29] CARB. 1999. Emissions inventory of off-road large compression-ignited engines (>25hp)
using the new OFFROAD Emissions Model. Mail-Out #MSC99-32. December 1999.
[30] Kean, A.J., Sawyer, R.F., Harley, R.A. 2000. A fuel-based assessment of off-road diesel
engine emissions. Journal of the Air and Waste Management Association 50:1929-1939.
[31] RTI International. 2007. Emissions Factor Uncertainty Assessment, Review Draft. 29
March 2013. http://www.epa.gov/ttn/chief/efpac/uncertainty.html.
[32] USDA National Resource Conservation Service (NRCS). 2009. Web Soil Survey 2.0. 2
January 2009. http://websoilsurvey.nrcs.usda.gov/app/.
[33] Geology.com. May 2009. http://geology.com/state-map/california.shtml.
[34] Live Search Maps. December 2008. http://maps.live.com.
[35] California Irrigation Management Information System (CEVIIS). 2009. Data for Station
#15 (Stratford) for May and June of 2005 through 2007. July 2009.
http://wwwcimis.water.ca.gov/cimis/data.jsp.
[36] Cooper, D.C., Alley, F.C. 2002. Air pollution control: A design approach. Waveland
Press Inc. Prospect Heights, Illinois. 575.
[37] Doran, J.W., Jones, A. 1996. Methods for assessing soil quality. SSSA Special
Publication Number 49. Soil Science Society of America. Madison, Wisconsin.
[38] Soil Sampling and Methods of Analysis, ed. by M.R. Carter, Canadian Society of Soil
Science. Lewis Publishers, 1993:508-509.
[39] Ibid. 659-662.
[40] Chen, F.-L., Williams, R., Svendsen, E., Yeatts, K., Creason, J., Scott, J., Terrell, D.,
Case, M. 2007. Coarse particulate matter concentrations from residential outdoor sites
associated with the North Carolina Asthma and Children's Environment Studies (NC-
ACES). Atmospheric Environment 41:1200-1208.
SDL/08-556 California 2008 Tillage Campaign 96
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[41] Chow, J. C., Watson, J. G., Lowenthal, D. H., Chen, L.-W A., Tropp, R. I, Park, K.,
Magliano, K. A. 2006. PM2.5 and PM10 Mass Measurements in California's San Joaquin
Valley. Aerosol Science and Technology 40(10):796-810.
[42] Airmetrics MiniVol Portable Air Sampler Operation Manual v. 5.
[43] Hinds, W. C. 1999. Aerosol Technology: Properties, Behavior, and Measurement of
Airborne Particles, 2nd Edition. John Wiley & Sons, New York. 75-82.
[44] Rupprecht & Patashnick, n.d. Series 5400 Elemental Carbon/Organic Carbon Analyzer
Instrument Manual.
[45] Malm, W.C. and J.L. Hand. 2007. An examination of the physical and optical properties
of aerosols collected in the IMPROVE program. Atmospheric Environment, 41, 3407-
3427.
[46] Marchant, C. 2008. Algorithm Development of the AGLITE-LIDAR Instrument, MS
Thesis, Utah State University.
[47] Zavyalov, V. V., Marchant, C. C., Bingham, G. E., Wilkerson, T. D., Hatfield, J. L.,
Martin, R. S., Silva, P. J., Moore, K. D., Swasey, J., Ahlstrom, D. J., Jones, T. L. 2009.
Aglite lidar: Calibration and retrievals of well characterized aerosols from agricultural
operations using a three-wavelength elastic lidar. Journal of Applied Remote Sensing
3(1):033522 [doi: 10.1117/12.833365].
[48] Klett, J.D. 1985. LIDAR inversion with variable backscatter/extinction ratio. Appl. Opt.
24: 1638-83.
[49] U.S. EPA. 2009. Guideline on Air Quality Models. Appendix W. U.S. Code of Fed.
Regulations, 40 CFR Part 51. May 2009.
http ://www. epa. gov/scramOO 1/guidance/guide/appw_05. pdf.
[50] Cooper, D.C., Alley, F.C. 2002. Air pollution control: A design approach. Waveland
Press Inc. Prospect Heights, Illinois. 611-626.
[51] Turner, D.B. 1970. Workbook of Atmospheric Dispersion Estimates. Washington, D.C.,
U.S. Environmental Protection Agency.
[52] Paine, R.J., Lee, R.F., Erode, R., Wilson, R.B., Cimorelli, A.J., Perry, S.G, Weil, J.C.,
Venkatram, A., Peters, W.D. 1998. Model evaluation results for AERMOD. Research
Triangle Park, NC:U.S. Environmental Protection Agency, Office of Air Quality
Planning and Standards Emissions, Monitoring, Analysis Division. March 2008.
http://www.epa.gov/scram001/7thconf/aermod/evalrep.pdf.
[53] National Research Council, National Academies of Science. 2003. Air emissions from
animal feeding operations: Current knowledge, future needs. The National Academies
Press. Washington, D.C. 95.
SDL/08-556 California 2008 Tillage Campaign 97
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[54] Arya, S.P. 1998. Air Pollution Meteorology and Dispersion. Oxford University Press.
[55] Lakes Environmental. 2009. Terrain Data: 7.5-Min DEM Native Format - United States.
July 2009. http://www.webgis.com/terr us75m.html.
[56] U.S. Department of Agriculture, Natural Resources Conservation Service. 2007. National
Soil Survey Handbook, title 430-VI. Available: http://soils.usda.gov/technical/handbook/.
[57] Bingham, G.E., Marchant, C. C., Zavyalov, V. V., Ahlstrom, D. I, Moore, K. D., Jones,
D. S., Wilkerson, T. D., Hipps, L. E., Martin, R. S., Hatfield, J. L., Prueger, J. H.,
Pfeiffer, R. L. 2009. Lidar based emissions measurement at the whole facility scale:
Method and error analysis. Journal of Applied Remote Sensing 3(1):033510 [doi:
10.1117/12.829411].
[58] Cooper, D.C., Alley, F.C. 2002. Air pollution control: A design approach. Waveland
Press Inc. Prospect Heights, Illinois. 100.
[59] Hinds, W. C. 1999. Aerosol Technology: Properties, Behavior, and Measurement of
Airborne Particles, 2nd Edition. John Wiley & Sons, New York. 402-408.
SDL/08-556 California 2008 Tillage Campaign 98
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10. APPENDICES
10.1 APPENDIX A: DATA AND SETTINGS TABLES
Table 30. Settings for the ISCST3 and AERMOD dispersion models for the tillage study in the ISC-
AERMOD View software by Lakes Environmental, Inc. All settings were held constant across the sample
periods except the source area size and shape, which changed each day, and the downwind receptor locations,
which were specific to each field studies. (— = not applicable)
Setting
ISCST3
AERMOD
Control Pathway
Dispersion Options
Output type
Plume Deposition
Pollutant
Averaging Time
Dispersion Coefficient
Terrain Height Options
Terrain Calculation
Algorithms (ISC), Receptor
Elev./Hill Hghts (AERMOD)
Regulatory Default
Concentration
None
Other - PM
Period
Rural
Elevated
Simple terrain only
Regulatory Default
Concentration
—
Other - PM
Period
Rural
Elevated
Run using the AERMAP Receptor
Output file
Source Pathway
Source type
Base Elevation
Release height
Emission rate
Initial Vertical Dim.
of the plume
Building downwash
Area Poly
0.0m
0.0 m AGL
8.6 E-6 g/(s-m2)
Blank
None
Area Poly
0.0m
0.0 m AGL
8.6 E-6 g/(s-m2)
Blank
None
Receptor Pathway
Uniform Cartesian Grid
(# Receptors: 4200)
Discrete Cartesian Receptors
(# Receptors: 10)
138x130, lOxlOm spacing,
flagpole height z = 2.0 m AGL
Placed at sample locations, z = 2, 5,
and 9 m AGL
138x130, lOxlOm spacing,
flagpole height z = 2.0 m AGL
Placed at sample locations, z = 2, 5,
and 9 m AGL
AERMET View Settings
Hourly Surface Data
Adjustment to Local Time
Application Station
Elevation MSL
Upper Air Data
Mode
Sectors Parameters
Time Zone
Randomize NWS Wind
Directions
Anemometer Height
Wind direction sectors
Land Use Type
—
—
—
—
—
—
—
—
—
—
—
Source: on-site data, mixing height =
1000 m AGL
8 hr (Pacific)
76.2m
Source: calculated based on on-site
data, mixing height = 1000 m AGL
Upper Air Estimator
8 (Pacific)
Yes
6.2 m AGL
1 : Start = 0°, End =360°
Cultivated Land
SDL/08-556
California 2008 Tillage Campaign
99
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Setting
Surface parameters per
sector
ISCST3
AERMOD
Annually, using spring-time default
values for Midday Albedo = 0.14,
Bowen Ratio = 0.3, and Surface
Roughness = 0.03 m
Meteorology Pathway
Surface Met Data
Profile Met Data
Anemometer Height (agl)
Primary Met Tower Base
Elevation above MSL
Read Entire Met Data File
Specify Data Periods to
Process
Wind Speed Categories
Source: on-site data, mixing height =
1000 m AGL
—
6.2m
—
No
Set to sample period times, varied by
sample
Default
Source: calculated from on-site data
and default values
Source: estimated from on-site data
—
76.2m
No
Set to sample period times, varied by
sample
Default
Output Pathway
Tabular Outputs
All
Not available, no short term
averaging times selected
Not available, no short term
averaging times selected
Buildings
None
None
Terrain
Calculated values from AERMAP
using 7.5 Min DEM
Calculated values from AERMAP
using 7.5 Min DEM
SDL/08-556
California 2008 Tillage Campaign
100
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Table 31. Calculated PM concentrations (|ig/m3) measured during May 2008 at all sample locations.
Location
(height
agl)
Tl(9)
10.0 (2)
11.0(2)
T2(9)
T3(9)
AQT (5)
2.4/2.5 (2)
6.4/6.5 (2)
7.4/7.5 (2)
8.4/8.5 (2)
9.4/9.5 (2)
12.0 (2)
Size
TSP
PM10
PM25
TSP
PM10
PM25
TSP
PM10
PM25
TSP
PM10
PM25
TSP
PM10
PM25
TSP
PM10
PM25
TSP
PM10
PM25
PM10
PM25
TSP
PM10
PM25
PM10
PM25
PM10
PM25
TSP
PM10
PM25
Sample Period
May 17
Rl
138.2
64.6
59.7
135.4
75.7
43.5
559.3
154.4
48.8
380.0
167.0
53.9
610.7
161.3
49.4
157.7
65.2
140.5
62.3
191.4
57.4
140.6
65.9
May 17
R2
330.0
239.3
128.1
628.2
205.5
266.8
533.8
199.6
92.0
368.3
109.0
141.6
*
186.4
187.8
1458.4
3244.9
83.8
153.8
300.9
179.8
203.6
144.4
May 18
118.2
*
133.8
266.1
38.1
27.1
479.6
100.1
46.2
154.3
59.4
65.1
1233.4
135.3
63.3
140.2
90.1
75.7
60.9
141.2
36.9
149.1
*
May 19
Rl
242.0
83.2
35.2
178.6
*
29.2
1138.2
231.5
71.7
188.3
*
38.2
*
312.1
135.0
278.2
132.7
268.7
153.5
562.5
171.0
570.6
265.5
May 19
R2
153.5
65.1
23.2
267.9
*
24.8
965.0
195.9
41.4
224.5
76.9
34.5
1894.2
387.8
232.4
408.4
491.0
215.9
202.6
402.4
102.8
335.3
344.8
May 20
Rl
242.3
115.7
31.6
260.5
*
51.2
1511.7
253.5
77.5
286.1
98.1
52.3
*
347.1
459.7
287.3
465.9
250.9
398.0
*
284.9
347.3
392.7
May 20
R2
343.4
469.8
205.5
355.1
*
89.3
2276.9
258.4
324.4
709.1
172.9
107.3
*
399.2
215.7
730.6
171.1
364.8
136.5
437.0
139.3
348.9
186.7
* Filter sample had a noted problem during or after sampling
(blank) = no sample collected
SDL/08-556
California 2008 Tillage Campaign
101
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Table 32. Calculated PM concentrations (|ig/m3) measured during June 2008 at all sample locations.
Location
(height
agl)
Tl(9)
10.0 (2)
11.0(2)
T2(9)
T3(9)
AQT (5)
2.4/2.5 (2)
6.4/6.5 (2)
7.4/7.5 (2)
8.4/8.5 (2)
9.4/9.5 (2)
12.0 (2)
Size
TSP
PM10
PM25
TSP
PM10
PM25
TSP
PM10
PM25
TSP
PM10
PM25
TSP
PM10
PM25
TSP
PM10
PM25
TSP
PM10
PM25
PM10
PM25
TSP
PM10
PM25
PM10
PM25
PM10
PM25
TSP
PM10
PM25
Sam
June 5
Rl
102.9
47.4
26.7
227.9
86.2
101.5
*
58.0
32.2
104.3
58.6
38.2
*
80.4
57.6
102.5
62.6
53.5
42.8
80.8
86.9
77.9
43.9
June 5
R2
158.5
*
68.0
231.4
272.6
165.2
353.8
109.6
138.8
243.8
221.3
252.5
*
137.5
174.3
229.4
123.1
246.2
140.4
163.4
110.0
131.6
114.6
June 7
110.7
57.2
27.5
*
80.7
38.8
204.6
61.5
29.5
158.7
78.0
30.8
335.7
80.3
47.8
78.0
36.9
59.0
33.2
88.3
56.1
98.4
33.1
pie Period
June 11
73.6
188.8
117.9
183.1
137.6
106.3
262.9
96.7
76.1
250.0
75.1
122.7
246.9
80.2
213.6
172.9
102.6
176.3
149.8
182.6
73.2
111.9
101.4
June 18
195.4
56.5
30.1
233.6
68.6
28.2
1896.9
333.6
42.9
489.4
38.9
1495.1
195.3
60.2
207.1
35.1
61.6
30.0
June 24
180.1
101.8
71.2
195.6
89.6
64.4
554.2
142.2
74.0
116.7
70.7
717.0
142.4
64.3
126.9
76.4
196.9
65.0
700.7
203.4
74.9
* Filter sample had a noted problem during or after sampling
(blank) = no sample collected
SDL/08-556
California 2008 Tillage Campaign
102
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Table 33. PM concentrations (|ig/m3) used in emission rate calculations from sample periods in May 2008.
Location
(height
agl)
Tl(9)
10.0 (2)
11.0(2)
T2(9)
T3(9)
AQT (5)
2.4/2.5 (2)
6.4/6.5 (2)
7.4/7.5 (2)
8.4/8.5 (2)
9.4/9.5 (2)
12.0 (2)
Size
TSP
PM10
PM25
TSP
PM10
PM25
TSP
PM10
PM25
TSP
PM10
PM25
TSP
PM10
PM25
TSP
PM10
PM25
TSP
PM10
PM25
PM10
PM25
TSP
PM10
PM25
PM10
PM25
PM10
PM25
TSP
PM10
PM25
Sample Period
May 17
Rl
138.2
64.6
59.7
135.4
75.7
43.5
559.3
154.4
48.8
380.0
167.0
53.9
610.7
161.3
49.4
157.7
65.2
140.5
62.3
191.4
57.4
140.6
65.9
May 17
R2
330.0
—
—
+
+
+
533.8
§
§
368.3
§
§
*
§
§
§
§
§
§
§
§
§
§
May 18
§
*
§
§
§
§
§
§
§
§
§
§
§
§
§
§
§
§
§
§
§
§
*
May 19
Rl
§
§
§
§
*
§
§
§
§
§
*
§
*
§
—
§
§
§
§
§
§
§
§
May 19
R2
§
§
§
§
*
§
§
§
§
§
§
§
§
§
§
§
§
§
§
§
§
§
§
May 20
Rl
§
§
§
§
*
§
§
§
§
§
§
§
*
§
§
§
§
§
§
*
§
§
§
May 20
R2
§
§
§
§
*
§
§
§
§
§
§
§
*
§
§
§
§
§
§
§
§
§
§
* = Filter sample had a noted problem during or after sampling
— = Removed due to ring particle density classification of medium or higher
+ = Removed due to exceptionally high or low value and not supported by OPC time series
0 = PM2 5 and PM10 levels indicated filters switched during sampling, concentrations switched
§ = Removed due to contaminated background or overloaded downwind filters
(blank) = no sample collected
SDL/08-556
California 2008 Tillage Campaign
103
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Table 34. PM concentrations (|ig/m3) used in emission rate calculations from sample periods in June 2008.
Location
(height
agl)
Tl(9)
10.0 (2)
11.0(2)
T2(9)
T3(9)
AQT (5)
2.4/2.5 (2)
6.4/6.5 (2)
7.4/7.5 (2)
8.4/8.5 (2)
9.4/9.5 (2)
12.0 (2)
Size
TSP
PM10
PM25
TSP
PM10
PM25
TSP
PM10
PM25
TSP
PM10
PM25
TSP
PM10
PM25
TSP
PM10
PM25
TSP
PM10
PM25
PM10
PM25
TSP
PM10
PM25
PM10
PM25
PM10
PM25
TSP
PM10
PM25
Sam
June 5
Rl
102.9
47.4
26.7
§
—
—
*
58.0
32.2
104.3
58.6
38.2
*
80.4
—
102.5
—
53.5
42.8
80.8
—
77.9
43.9
June 5
R2
158.5
*
68.0
§
—
353.8
§
—
243.8
—
+
*
§
—
§
123.1
§
140.4
§
—
§
114.6
June 7
110.7
57.2
27.5
*
80.7
38.8
204.6
61.5
29.5
158.7
—
30.8
335.7
80.3
—
—
—
59.0
33.2
88.3
56.1
98.4
33.1
pie Period
June 11
+
188.8
117.9
183.1
137.6
106.3
262.9
96.7
76.1
250.0
122.7 0
75.10
246.9
213.60
80.20
172.9
102.6
176.3
149.8
182.6
73.2
111.9
101.4
June 18
195.4
56.5
30.1
233.6
68.6
28.2
1896.9
333.6
42.9
489.4
38.9
1495.1
195.3
60.2
207.1
35.1
61.6
30.0
June 24
180.1
101.8
71.2
195.6
89.6
64.4
554.2
142.2
74.0
116.7
70.7
717.0
142.4
64.3
126.9
76.4
196.9
65.0
700.7
203.4
74.9
* = Filter sample had a noted problem during or after sampling
— = Removed due to ring particle density classification of medium or higher
+ = Removed due to exceptionally high or low and not supported by OPC time series
0 = PM2 5 and PM10 levels indicated filters switched during sampling, concentrations switched
§ = Removed due to contaminated background or overloaded downwind filters
(blank) = no sample collected
SDL/08-556
California 2008 Tillage Campaign
104
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Table 35. PM concentrations (|ig/m3) as measured by OPCs from sample periods in May 2008. (OPC
V, x MCF,)
Location
(height
agl)
Tl(9)
10.0 (2)
11.0(2)
T2(9)
T3(9)
AQT (5)
2.4/2.5 (2)
7.4/7.5 (2)
8.4/8.5 (2)
12.0 (2)
Size
TSP
PM10
PM25
TSP
PM10
PM25
TSP
PM10
PM25
TSP
PM10
PM25
TSP
PM10
PM25
TSP
PM10
PM25
TSP
PM10
PM25
TSP
PM10
PM25
TSP
PM10
PM25
TSP
PM10
PM25
Sample Period
May 17
Rl
95.3
50.6
9.2
597.1
142.3
12.2
636.3
174.7
13.2
601.4
165.2
12.9
816.6
199.0
13.7
May 17
R2
181.6
68.6
4.3
1777.6
197.0
6.3
417.1
75.5
4.8
3251.9
318.8
7.5
2767.5
292.0
7.7
May 18
109.1
48.9
7.7
490.0
110.3
9.7
164.6
54.4
7.9
813.2
178.0
11.1
692.0
141.5
10.1
May 19
Rl
181.7
70.3
12.8
159.0
61.9
12.4
1585.2
283.4
18.9
227.8
74.0
12.9
2263.5
375.8
19.9
2523.2
406.8
20.5
May 19
R2
127.8
58.6
8.4
275.9
89.1
9.7
1296.9
241.6
12.5
207.1
71.3
8.9
3271.9
641.1
20.0
2553.9
451.5
15.9
May 20
Rl
224.7
74.5
12.6
296.3
87.9
12.2
1282.2
252.3
16.5
331.3
102.3
13.2
2550.4
462.7
20.7
1656.4
342.1
18.5
May 20
R2
SDL/08-556
California 2008 Tillage Campaign
105
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Table 36. PM concentrations (|ig/m3) as measured by OPCs from sample periods in June 2008. (OPC
V, x MCF,)
Location
(height
agl)
Tl(9)
10.0 (2)
11.0(2)
T2(9)
T3(9)
AQT (5)
2.4/2.5 (2)
7.4/7.5 (2)
8.4/8.5 (2)
12.0 (2)
Size
TSP
PM10
PM25
TSP
PM10
PM25
TSP
PM10
PM25
TSP
PM10
PM25
TSP
PM10
PM25
TSP
PM10
PM25
TSP
PM10
PM25
TSP
PM10
PM25
TSP
PM10
PM25
TSP
PM10
PM25
Sam
June 5
Rl
95.3
41.2
11.7
172.4
69.5
13.6
205.9
63.8
13.3
113.3
47.2
11.6
386.1
93.2
14.7
180.4
64.0
12.6
June 5
R2
170.5
47.7
5.7
571.5
160.2
12.1
364.4
80.9
6.9
221.8
67.2
6.4
1162 A
213.0
12.9
769.2
193.9
11.6
June 7
104.8
43.1
16.2
194.6
94.4
19.3
190.9
59.2
16.9
194.2
62.3
16.8
318.3
83.4
18.3
245.8
72.2
17.1
pie Period
June 11
222.4
122.4
11.4
198.4
120.7
10.7
213.4
132.6
11.2
257.4
141.1
11.4
282.4
155.8
11.4
357.1
171.0
12.0
June 18
179.5
47.0
4.8
153.6
44.2
5.4
475.1
97.0
6.4
2816.4
467.4
22.0
2084.4
295.0
12.7
1703.5
214.5
9.6
June 24
127.0
85.0
56.9
172.4
81.7
55.8
334.9
109.4
57.8
720.8
165.8
60.2
822.3
177.1
58.5
868.3
203.6
58.3
SDL/08-556
California 2008 Tillage Campaign
106
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Table 37. PM concentrations (|ig/m3) as measured by OPCs used in emission rate calculations from sample
periods in May 2008. (OPC PMft = Vk x MCFft)
Location
(height
agl)
Tl(9)
10.0 (2)
11.0(2)
T2(9)
T3(9)
AQT (5)
2.4/2.5 (2)
7.4/7.5 (2)
8.4/8.5 (2)
12.0 (2)
Size
TSP
PM10
PM25
TSP
PM10
PM25
TSP
PM10
PM25
TSP
PM10
PM25
TSP
PM10
PM25
TSP
PM10
PM25
TSP
PM10
PM25
TSP
PM10
PM25
TSP
PM10
PM25
TSP
PM10
PM25
Sample Period
May 17
Rl
95.3
50.6
9.2
597.1
142.3
12.2
636.3
174.7
13.2
601.4
165.2
12.9
816.6
199.0
13.7
May 17
R2
181.6
68.6
4.3
1777.6
197.0
6.3
417.1
75.5
4.8
3251.9
318.8
7.5
2767.5
292.0
7.7
May 18
109.1
48.9
7.7
490.0
110.3
9.7
164.6
54.4
7.9
813.2
178.0
11.1
692.0
141.5
10.1
May 19
Rl
156.5*
63.3*
12.6*
159.0
61.9
12.4
1585.2
283.4
18.9
227.8
74.0
12.9
2263.5
375.8
19.9
2523.2
406.8
20.5
May 19
R2
127.8
58.6
8.4
137.1*
56.1*
8.4*
1296.9
241.6
12.5
207.1
71.3
8.9
3271.9
641.1
20.0
2553.9
451.5
15.9
May 20
Rl
224.7
74.5
12.6
169.0*
64.2*
11.7*
1282.2
252.3
16.5
331.3
102.3
13.2
2550.4
462.7
20.7
1656.4
342.1
18.5
May 20
R2
Significant plumes at upwind location removed
SDL/08-556
California 2008 Tillage Campaign
107
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Table 38. PM concentrations (|ig/m3) as measured by OPCs used in emission rate calculations from sample
periods in June 2008. (OPC PM* = Vk x MCFft)
Location
(height
agl)
Tl(9)
10.0 (2)
11.0(2)
T2(9)
T3(9)
AQT (5)
2.4/2.5 (2)
7.4/7.5 (2)
8.4/8.5 (2)
12.0 (2)
Size
TSP
PM10
PM25
TSP
PM10
PM25
TSP
PM10
PM25
TSP
PM10
PM25
TSP
PM10
PM25
TSP
PM10
PM25
TSP
PM10
PM25
TSP
PM10
PM25
TSP
PM10
PM25
TSP
PM10
PM25
Sam
June 5
Rl
95.3
41.2
11.7
93.9*
41.0*
11.8*
205.9
63.8
13.3
113.3
47.2
11.6
386.1
93.2
14.7
180.4
64.0
12.6
June 5
R2
170.5
47.7
5.7
127.9*
42.6*
6.2*
364.4
80.9
6.9
221.8
67.2
6.4
1162 A
213.0
12.9
769.2
193.9
11.6
June 7
104.8
43.1
16.2
98.6*
41.9*
16.3*
190.9
59.2
16.9
194.2
62.3
16.8
318.3
83.4
18.3
245.8
72.2
17.1
pie Period
June 11
222.4
122.4
11.4
198.4
120.7
10.7
213.4
132.6
11.2
257.4
141.1
11.4
282.4
155.8
11.4
357.1
171.0
12.0
June 18
179.5
47.0
4.8
153.6
44.2
5.4
475.1
97.0
6.4
2816.4
467.4
22.0
2084.4
295.0
12.7
1703.5
214.5
9.6
June 24
127.0
85.0
56.9
172.4
81.7
55.8
334.9
109.4
57.8
720.8
165.8
60.2
822.3
177.1
58.5
868.3
203.6
58.3
Significant plumes at upwind location removed
10.2
APPENDIX B: INVESTIGATIONS INTO AND CONCLUSIONS FROM FILTER-
BASED DATA
During the post-weighing process it was noticed that filters used for PM2.s, PMio, and TSP had
particles large enough to be visible to the naked eye, not all of which were embedded in the filter
medium and thus could move around and off the filter. Particles small enough to pass through
the impactor assembly in the sample head are typically too small to be seen with the naked eye,
almost always being less than 10 jam and 2.5 jam in diameter for PMio and PM2.5, respectively.
Particles collected during sampling that were not stuck into or onto the filter medium may move
off of the filter, yielding a variable low bias in reported PM concentrations. In addition, several
of the calculated PM2.5 concentrations significantly exceeded the collocated PMio concentrations,
which is theoretically impossible under the ideal sampling scenario since PM2.5 is a subset of
PMio and should always be less than or equal to the PMio concentration. These inverted PM2.5
and PMio concentrations, as we will call them, were very prevalent in the data for several sample
periods, with up to 75% of the downwind PM2.s and PMio concentrations inverted for the lister
SDL/08-556
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108
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pass on May 20, 2008. These size-inverted concentrations and the presence of visible particles on
the filters prompted an investigation to determine their causes as well as potential solutions for
the current data set and prevention methods for future measurements. This section describes
these investigations, as well as the conclusions drawn about the suitability of the dataset for use
in emission rate calculations, and options for preventing such problems in the future.
All aspects of the pre-, intra-, and post-sampling filter handling were examined in detail for
potential problems. Filter identification numbers were recorded throughout the preparation,
sample, and analysis phases, which allowed each filter to be tracked throughout the entire
process. It was thought that the cause could be improper and/or inaccurate weighing methods and
instruments. This possibility, however, was ruled out as all filters were preconditioned according
to U.S. EPA standards in dessicators before both pre- and post-weights were determined.
Measurements of weight to the 1 ng (1x10~6 g) level were performed by experienced personnel
using a vibration-isolated micro-balance at the UWRL that had been calibrated on-site by
manufacturer personnel earlier in 2008; a certified 1.000 mg calibration mass was used to
monitor balance accuracy and drift every ten readings. The correct calculation and use of average
pre- and post-weights for all filters were verified, and the filter numbers recorded on sample run
sheets were checked for potential errors. A few errors were found during this verification process
in the calculation and use of average pre- and post-weights, but they did not explain the majority
of the suspect filters with the problems stated above. The errors found have been corrected in the
concentrations reported in Section 4.2.1 and found in Table 31 and Table 32.
Another potential cause of inverted collocated PM2.s-PMio concentrations was that the filters
were switched during sampling due to human error, causing the filter designated for PM2.s to
actually sample PMio and vice versa. Data were examined on a location-by-location basis by
switching the suspected PM2.5 and PMio concentrations and comparing them with other
downwind PM concentrations of the same size fraction measured during the same sample period.
The results suggested that this likely occurred on a total of four occasions at two sample
locations and across two sample periods. Switching these PM2.5 and PMio concentrations yielded
a more consistent set of concentrations in all cases. In addition, examination of the OPC time
series yielded no irregularities that would suggest problems with either one or both samples. The
collocated PM2.s and PMio concentrations were therefore switched for subsequent analysis.
Similar to the verification of correct filter tracking and the average weights used, this
investigation found some inverted collocated PM2.s-PMio concentrations could be explained by
the filters being switched during sampling but that a significant number of instances of the
phenomena were unexplained. It was determined that this was likely not the cause in most cases
due to the pervasiveness of this phenomenon during only a few sample periods and because the
personnel that were operating the filter-based measurements were experienced and capable.
Further investigations were made through visual inspection of the filters through microscopy.
Several representative filters were examined through a microscope to size and count collected
particles. Examined filters included some from each of the following categories: collocated
PM2.s and PMio samples where the PM2.5-PMi0 concentrations were inverted; samples collected
in upwind/background locations on days with multiple suspect filters; and blank filters that were
taken to the field but not used. The microscope used was a BX51 from Olympus (Center Valley,
PA) with a stage that could be controlled either manually or through computer software. An
Olympus Model DP71 camera was mounted on the microscope to capture digital images of the
microscope field of view. The MicroSuite Five Imaging software, also by Olympus, controlled
SDL/08-556 California 2008 Tillage Campaign 109
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the camera and stage automatically to photograph a user-specified area of the filter, combine
adjacent images, and identify and size particles in the images. The 5x, lOx, 50x, and lOOx lenses
were all tested, but the software analysis on images taken using 50x and lOOx lenses under the
given light, microscope, and software settings were not used due to inaccurate sizing and
counting. The 5x lens was mainly used to scan over the filter area for large particles (> -25 jam
in diameter), while most images used for counting and sizing particles were taken with the lOx
lens because smaller particles could be detected. The minimum particle diameters detected by the
5x and lOx, lenses under the light, microscope, and software settings were 5.22 jam and 2.61 jam,
respectively. Projected area particle diameter (dpA) is the diameter of a circle having the same
area as the 2D projected area of the 3D particle and is a common representation of particle size
based on microscopic sizing [59]; the dpA were calculated based on individual particle area
values provided by the MicroSuite Five Imaging software. Smaller particles could be visually
identified in the images, but the software could not sufficiently resolve them from the
background light intensity variation inherent in the Teflon filter medium. The entire microscope
and computer imaging system were housed in a room maintained at Class 100 clean room
standards to prevent particle deposition on the filters during analysis. All of the microscope work
was performed by just one person in order to maintain consistent procedures and settings.
The 47 mm diameter Teflon filters used in PM mass sampling have a collection area composed
of an exposed mesh of Teflon fibers about 41 mm-diameter and a 3 mm-wide plastic ring which
holds the Teflon fibers in place; this plastic ring is used for filter handling and also for securing
the filter into the MiniVol sampling fixture. The Teflon area is fragile. The sample collection
area is about 1320 mm . The sampling fixture is a two-part, push-on assembly which sandwiches
the Teflon filter between two plastic fittings. Figure 43 shows an example of an image of a PMio
filter sample collection area taken using the microscope and the lOx lens. This image is
composed of four adjacent pictures that were automatically combined by the software. Note the
varying shades of the grey background from the Teflon fibers. Particles are shown as both dark
and bright white spots, depending on chemical composition, size, and orientation with respect to
the light source and microscope lens. The average dpA ± la of detected particles in this image
was 4.47 ± 2.56 jam (n 2977) and the median mean diameter was 3.50 jam with 25 and 75
percentile values of 2.86 and 5.08 jam; 95.9 % of the detected particles had a dpA less than 10.0
jam. The largest particle, marked by the blue ring, was measured at a dpA of 31.94 jam.
Due to the magnification and time limits of this analysis at each position, a total of about 1% of
the collection area on most filters was sampled after integration over three to four sample
locations. Sample locations were chosen randomly within quadrants, with each sample from a
quadrant that was previously not sampled. While a total sample area of at least 10% is preferred,
the variability of the cumulative distribution between sample locations on the same filter was less
than 10%. Therefore, multiple sample areas per filter with a total area of about 1% of the
potential collection area were deemed adequate for the purposes of this analysis.
SDL/08-556 California 2008 Tillage Campaign 110
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Figure 43. Four adjacent microscopic images taken with the lOx lens that have been combined for
determining the particle size distribution and count at this location on a PM10 sample filter. The largest
particle with dPA = 31.94 |im is shown by the blue circle.
Results of the microscope particle sizing and counting on two sets of PM2.5 and PMio samples
where the concentrations were inverted showed that the dpA size distributions on the sample
collection areas were similar in shape, with the PMio filters having a much higher particle
density than the PM2.5 filter, as seen in Figure 44. Note that the size distribution is given in
number of particles per square mm of filter surface per jam of bin width; incorporating the bin
width into the denominator removes the bin number concentration dependence on bin width.
Also, it must be remembered that the MDL was 2.61 jam and that dpA is related to but different
from aerodynamic diameter (daero). For a given particle, dpA and daero values should be similar but
will vary according to shape, density, and orientation at the time of detection. These data,
coupled with visual scans of the entire collection area to find and size large particles, suggest that
the inverted PM2.5-PMio concentrations for these filters cannot likely be explained by large
particle contamination on the PM2.5 collection area. Additionally, the data show that there was
neither a complete failure of the size fractionating impactors nor were many of the filters
switched during sampling. Very similar particle count densities in each size fraction between the
two downwind sites, 7.4 and 2.4, suggest that in-plume number size distribution was roughly
spatially homogenous during the May 20 Run 1 sample period, assuming spatially homogeneous
deposition on the filter area. The differences between the upwind PM2.5 size distribution and the
downwind PM2.5 size distributions are due to the plumes that impacted the downwind samplers.
SDL/08-556
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111
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A potentially significant number of larger-than-expected particles (dpA > ~2.5 jam for PM2.5 and
dpA > ~10 jam for PMio) were found on the downwind PMio and PM2.5 filters; there were even
particles greater than 100 jam in diameter. Particles with dpA greater than 25 jam were found on
the downwind PM2.s and PMio filters whose size distributions are presented in Figure 44. Such
large particles are not unexpected on TSP samples collected downwind of agricultural tillage
operations, but they should not be present on samples with a PMio or PM2.5 size-separating
device upstream of the filter in the sample head assembly. In addition, large particles were found
on the annular plastic ring of many of the examined filters by both the microscope and inspection
with the naked eye. This was troubling since this annular area is completely covered by the
sampling fixture itself and therefore cannot collect particles during active sampling. Most
observed particles on the annular filter ring area were sitting on the surface, suggesting either
migration from the collection area or deposition after removal from the sampling device. Several
particles were even pressed into the plastic, suggesting that they were either present on the filter
prior to being loaded for sampling or were on the bottom of the filter holder piece when pressed
down onto the plastic ring medium and remained there upon removal from the sampling fixture.
•Upwind PM2.5
•7.4 PMIO
•2.4 PMIO
7.4PM2.5
•2.4PM2.5
300
E
250 -
200 -
150 -
100 -
50 -
5/20 Run 1
100
Figure 44. Number distributions based on projected area diameter as measured via microscopy for two
downwind and one upwind PM2.S filters and two downwind PM10 filters used during the Lister pass on May
20, 2008.
The presence of such large particles on both the collection area and the annular area of multiple
filters may be explained by several potential factors, including but not limited to:
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1) The collection efficiency curve of the PMio and PM2.5 size fractionation devices
employed by both FRMs and MiniVols are S-curves with respect to particle aerodynamic
diameter, designed to mimic the particle removal efficiency of the human respiratory
system. The removal efficiency curves have a 50% collection efficiency at the designed
cut-off size, with some smaller particles being removed and some larger particles passing
through the system. The potential exists for high numbers of large particles to be present
in plumes emitted from sources such as agricultural tillage activities and, if the plume is
being sampled by an instrument a short distance away as was the case in this study, some
particles larger than the cut size would be expected to pass through the size-separating
devices. This potential factor would best apply to particles close to the cut size as the
collection efficiency curve theoretically approaches 100% soon after passing the design
cut-off diameter, with the MiniVol S-curve being less steep than the FRM and
approaching 100% at a larger size.
2) Improper assembly of the size-fractionating impactor is another potential explanation for
passing larger-than-expected particles through the impactor. While possible on an
individual basis, such an improper assembly problem across up to 75% of the downwind
samplers is not likely (75% of downwind PMio and PM2.5 samples had significant
numbers of visible particles). In addition, two full sets of sample heads were used
throughout the study period to minimize the time required for preparation between
sample periods, and the problematic PM concentrations were present in filters which
were placed in both sets of sample heads.
3) A phenomenon commonly referred to as "particle bounce" may have occurred. Particle
bounce refers to when a particle collides with the impactor assembly but then returns to
the airstream and is collected downstream at the filter. To aid in removal of particles
colliding with the impactor plate, grease may be applied on the impaction surface. If too
many particles accumulate in the grease, however, the impaction surface loses its
"stickiness" and larger particles may bounce off, leading to higher reported PM2.5/PMi0
concentrations than actually existed. Impactor plate stickiness is influenced by a
combination of total exposure time and PM concentrations during exposure. The smaller
effective impact area and increased particle velocity in the MiniVol PM2.5 impactor
assembly makes it more susceptible to particle bounce than the PMio assembly under the
same conditions, which could result in higher reported levels of PM2.5 than PMio.
Airmetrics, Inc. suggests cleaning and regreasing of the impactor plates every five to
seven samples to maintain the design removal efficiency, but states that the need to renew
the plate may change based on exposure levels. In the case of this study, silicone-based,
high-vacuum grease was applied to all impaction surfaces on-site prior to sampling in
May. Personnel noted that impactor assemblies in downwind samplers had collected
significant amounts of particles near the end of the seven sample periods in that month
(May 17-20), which suggests that particle bounce likely occurred during some sample
periods. The sample heads and impactor assemblies were cleaned and greased during the
break in measurements, with evaluation and re-greasing if necessary after each
measurement in June. In addition, the enhanced susceptibility of the PM2.5 assembly to
particle bounce could explain the inverted PM2.s and PMio concentrations observed at
downwind locations during the May 19 and May 20 sample periods.
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4) Exposure to dust plumes while the sampler was deployed but not actively pulling air
through its system is another potential cause. In such a case, settling of particles may
allow some to reach the filter. Dirt access roads surround each field at this location and
the standoff distance for the samplers usually put them on the downwind side of these
access roads. While travel was limited to farm activity, it is possible that a vehicle driving
on these roads may have passed shortly before a programmed start time and resulted in
large particles being deposited on the filter.
5) Contamination prior to or after sampling is a potential cause that cannot be ruled out,
especially in a dry, dusty environment. Filters were held in individual Petri dishes at all
times except during sample collection. The filters and Petri dishes were stored on-site in
dessicators. All required filter handling for sampling was performed on-site in a trailer
over white laboratory paper which was changed as needed. Personnel handling the filters
wore latex gloves and used tweezers to move filters; the tweezers were rinsed with DDI
water as needed. Any filters dropped prior to sampling were not used, and those that were
dropped after sampling were noted and not used in further calculations. While the trailer
was kept as clean as possible, the environment outside of the trailer was dusty. In fact,
blowing dust was a problem during a couple sample periods, especially May 20 Rl and
R2.
While any one of these potential causes can significantly impact the mass collected on a filter,
there is evidence from examination of the filters under the microscope that a combination of
several of these phenomena potentially occurred. It was suspected that contamination prior to or
after sampling accounted for the largest portion of the problems in the MiniVol mass
concentration data, with additional contributions at locations heavily impacted by agricultural
activity, or traffic on nearby dirt roads from particle bounce, and the passage of large particles
due to the S-shaped collection efficiency curve of the impactor assembly.
While the microscopic analysis of the filters provided insight into the number/size of particles on
the filters and causes of possible contamination, it did not provide a feasible solution to correct
this problem in the current data set. Microscopically examining and analyzing both the collection
area and the ring of each of the 296 filters used was prohibitively time intensive. A surrogate
method for determining the compromised/contamination level of each filter was devised in order
to feasibly remove those samples that were compromised. The ring on each filter was visually
inspected by just one person with a Doublet lOx single lens magnifier by Selsi Company, Inc.
(Midland Park, NJ) against a white background and the relative amount of particles found on the
ring was classified into one of the following ring particle density categories: 1) None, 2) Very
light, 3) Light, 4) Medium, 5) Heavy, 6) Very heavy, and 7) Extremely heavy. The following two
categories were also used for the specified reasons: 8) Not available - filters were not available
for visual inspections because the 1C ion analysis had already been performed; and 9) Filters
with notes - filters that had noted problems either during or after sampling that could affect the
mass collected, such as being dropped after sampling or a malfunction of the flow system. While
this method is subjective it has the virtue of minimizing the impact of grossly contaminated
filters on the final PM emission values.
A significant number of particles were found on the rings of upwind/background and downwind
samples for all three PM mass fractions measured. In fact, none of the inspected filters had a
completely clean ring that would correspond to the "None" category. Table 39 presents the
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results of the visual categorization according to the measured PM fraction. Filters with noted
problems (i.e., stable post-weights could not be obtained, filter was dropped during post-sample
handling, large particles/organic matter were present on the filter immediately after sampling)
numbered 17, 5.7% of the 296 samples collected during the study. Most samples (66.9%) were
classified as very light or light, having just a few small detectable particles. As TSP sampling
does not use an impactor assembly, a TSP filter would be expected to have larger and much more
numerous particles that may more easily move to the ring of the filter. Therefore, more TSP
filters would be expected to have a significant number of particles on the ring than the collocated
PMio and PM2.5 filters collected at locations exposed to heavy PM plumes. This expected result
was supported in the ring particle density categorization of the filters: the only filter classified as
extremely heavy was a TSP filter. TSP filters dominated the very heavy category, most of which
had visible particles in the Petri dish in addition to the filter ring.
Table 39. Size fractionated results of the visual inspection of annular filter rings.
Size
TSP
PM10
PM25
Total
%of
Total
Ring Particle Density Category
None
0
0
0
0
0
Very
Light
2
45
35
82
27.7
Light
22
51
43
116
39.2
Medium
19
9
19
47
15.9
Heavy
8
2
8
18
6.1
Very
Heavy
4
0
1
5
1.7
Extremely
Heavy
1
0
0
1
0.3
Not
available
0
1
9
10
3.4
Filter
w/ notes
8
8
1
17
5.7
Sum
64
116
116
296
A troubling finding was that a significant percentage (24%) of PM2.5 filters was categorized as
medium or greater. Most of these occurred on downwind samples collected May 18-20, while the
collocated PMio samples are generally in the very light and light categories. This finding
suggests that contamination during non-sample times is likely not a significant cause of the
problems in the data for these days because all the filters used on a given day were treated the
same during filter handling and storage and it would be expected that filters of all size fractions
would be impacted equally. This disparity in the number of PM2.s and PMio filters in the medium
or greater ring particle density categories suggests that the dominant cause of larger-than-
expected particles and PM2.5 concentrations higher than PMio occurs at the impactor assembly in
the sample head, which was the only difference between the PM2.5 and PMio MiniVol samplers
used. As the PM2.s impactor assembly removes more particles from the airstream than a
collocated PMio impactor, it is likely that particle bounce would occur sooner and at a higher rate
in the PM2.5 impactor than in the PMio impactor under the same plume conditions.
Table 40 presents the ring particle density category results by sample period, with a graphical
presentation in Figure 45. In general, the light category has the greatest number of filters,
followed by the very light category. The greatest frequency of filters classified as medium and
greater occurs in samples collected May 18-20, which corresponds to the greatest occurrence of
PM2.5 samples classified as medium or greater. One conclusion drawn from this is that the
downwind filter-based PM samplers were likely overloaded during those sample periods,
suggesting that the reported downwind concentrations should not be used for emission rate
calculations. This removes 104 samples, leaving 175 samples for emission rate calculations and
six others with noted problems. Table 41 shows the operation(s) examined in each sample
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period, whether or not the filter-based PM dataset for each period was suitable for calculating
emission rates, and, if it was not used, the reasons for excluding the entire dataset.
Table 40. Results of the visual inspection of filter rings by sample date.
Sample
Period
May 17 Rl
May 17 R2
May 18
May 19 Rl
May 19 R2
May 20 Rl
May 20 R2
June 5 Rl
June 5 R2
June 7
June 11
June 18
June 25
Sum
%of
Total
Ring Particle Density Category
Very
Light
6
3
3
6
7
7
5
0
0
4
10
12
19
82
27.7
Light
13
13
7
5
8
3
11
12
12
12
13
6
1
116
39.2
Medium
3
4
8
6
3
2
3
3
7
5
0
1
2
47
15.9
Heavy
0
1
1
1
1
5
1
5
1
0
0
1
1
18
6.1
Very
Heavy
0
0
1
1
1
2
0
0
0
0
0
0
0
5
1.7
Extremely
Heavy
0
0
0
0
1
0
0
0
0
0
0
0
0
1
0.3
Not
available
1
1
1
1
1
1
1
1
1
1
0
0
0
10
3.4
Filter w/
notes
0
1
2
3
1
3
2
2
2
1
0
0
0
17
5.7
Sum
23
23
23
23
23
23
23
23
23
23
23
20
23
296
100.0
For the remaining samples, the ring particle density classifications were used to separate out
problematic PM concentrations. In the PM2.5 and PMio size fractions, all filters that were
classified as medium or greater and those with noted problems were removed, totaling 12 (6.9%)
and 8 (4.6%) PM2.5 and PMio samples, respectively, for remaining sample periods. In addition,
two PM2.5, one PMio, and two TSP samples were removed from consideration due to
exceptionally high or low concentrations, often with significantly greater PM2.5 or PMio
concentrations than PMio or TSP, respectively. Two of the five were collocated with an OPC and
the OPC time series (based on 20-second sample times) in each case was examined and provided
no evidence of contamination/concentrations significantly above background during sampling.
Four of the five removed for this reason were collected at background locations, and the fifth
was at the AQT location while the tillage operation was occurring in Field 4, several hundred
meters to the north. The other three samples removed were background TSP, PMio, and PM2.5
samples for May 17 Run 2, which were not collocated with an OPC at the time. Unfortunately,
this did not leave a background PMio or PM2.5 concentration to use in calculating the operation's
contribution to downwind concentrations for this run as the PMio and PM2.5 filters at the other
background location had ring particle density categorizations of medium and were also removed.
Therefore, a TSP emission rate will be calculated using inverse modeling for May 17 Run 2, but
not PMio and PM2.5 emission rates.
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c
I
• Very Light
• Light
• Medium
• Heavy
• Very Heavy
• Extremely Heavy
Not available
• Filters w/notes
f-Very Light
Medium
Heavy
Very Heavy
Extremely Heavy
Notavailable
Filters w/notes
Jll
J1S
J25
Figure 45. Line graphs showing the number of filters in each ring particle density category for each sample
period.
Traffic on access roads adjacent to upwind/background locations was recorded by field personnel
during most sample periods. Therefore, possible contamination due to sources nearby the upwind
sample locations was closely investigated during each sample period. This was accomplished by
examination of the available OPC time series for significant spikes in particle counts that
indicate such an event. The OPC time series for upwind location 10.0 on June 5 Run 1 (Rl) and
June 5 Run 2 (R2) showed that nearby road traffic significantly impacted the PM samplers:
between 50% and 80% of the TSP volume concentrations were attributed to such events. In
addition, the ring particle density categories of the six filters collected at this location on June 5
were: PM2.5 heavy and medium; PMio heavy and medium; and TSP = medium and light.
Based on these categorizations, the PM2.5 and PMio values had previously been removed for
being medium or greater. In light of this, the two TSP concentrations at location 10.0 were also
removed from emission rate calculations. Samples collected at the other background location,
Tl, were not compromised by road traffic.
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Table 41. Sample period filter datasets used in calculating emissions, with reasons for why some datasets were
not used in further calculations.
Sample
Period
Operation(s)
Used
If not, reason excluded
Conservation Tillage
May 17
Rl
June 7
June 11
Strip-Till
Plant and Fertilize
Herbicide application
Yes
Yes
Yes
—
—
___
Conventional Tillage
May 17
R2
May 18
May 19
Rl
May 19
R2
May 20
Rl
May 20
R2
June 5 Rl
June 5 R2
June 18
June 25
Break down in-field borders
Chisel
Disc 1
Disc 2
Lister
Build ditch and field-edge
borders
Break down ditch and field
edge borders, Cultivator
passes 1 and 2, and Roller
Plant
Fertilize
Cultivator pass 3
TSP - Yes
PMio, PM2.5 - No
No
No
No
No
No
Yes
TSP, PM2.5 - Yes
PMio - No
Yes
Yes
Problems with most
background samples
Downwind samplers
overloaded by plumes
Downwind samplers
overloaded by plumes
Downwind samplers
overloaded by plumes
Downwind samplers
overloaded by plumes
Windblown dust
contamination
Problems with both PMio
background samples
___
—
Results of these investigations into the inverted PM2.5-PMi0 concentrations and visible particles
led to the removal of many collected filter samples from emission rate calculations using inverse
modeling, including all downwind samples collected during five consecutive sampling periods.
A graphical representation of the filters used and reasons for not using others is presented in
Figure 46. Note that the four samples for which it was determined that the PM2 5 and PMio filters
had been switched were corrected (i.e., the concentration values were reassigned) and used in
emission rate calculations. The final PM concentrations used in emission rate calculations are
tabulated in Table 33 and Table 34. Most of the samples removed during this investigation were
not used in MCF calculations, as explained in Section 4.3.2.
There are several practices and procedures which may prevent such problems in future filter-
based sampler deployments in such conditions. Filter handling steps include, but are not limited
to, the following: use EPA-recommended filter storage, handling, and mass determination
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Total = 296 samples
5, 2%
I Pass Inspection
I PM2.5-PM10 Switched
Note on runsheet
I Contaminated/Overloaded
I Ring density > medium
I Abnormally high/low
Figure 46. Categorization of filter samples to determine suitability for use in emission rate calculations in
inverse modeling.
methodologies; use a calibrated mass balance, with continuing calibration verification; carefully
track each filter throughout the pre-weighing, sampling, and post-weighing steps; and ensure that
the filter storage and handling areas, both in the lab and the field, are clean and will minimally
contribute to the particles present on the filter. During the sample preparation and sampling
periods, the following practices will help to prevent problems with filter samples: inspect each
sample assembly before and after use for potential problems, specifically checking the condition
of the layer of high-vacuum grease on the impactor plate; place sample locations at a sufficient
distance downwind from the source(s) such that heavy plumes will not overload the sampler; if
possible, restrict access to upwind areas to prevent contamination of upwind samplers, but if it is
not possible place upwind samplers in positions that will not be impacted by nearby activities.
Additionally, record in detail all activities under study (e.g., tractor operation time, tractor
position via GPS) and any activities in upwind areas that may potentially affect upwind and/or
downwind samplers. Proper training of personnel is also critical, both in prevention and
identification of potential problems. Placing a more time-resolved PM sampler, such as the OPCs
employed herein, with filter-based samplers provides the opportunity to examine changes in
aerosol loadings to identify impacts from source activities, as well as potential contamination
during sampling. It should be noted that most of these procedures were already in use when the
problems in this campaign occurred; future mitigation of such problems requires more careful
evaluation and application.
As discussed in the conclusions, the fact that we had to dedicate so many resources to extracting
any useable data from our MiniVol samplers tells us that some field situations are in fact "too
dusty" for the sampling techniques as we applied them. Furthermore, even when we tried to
counter some of these effects (by using a PMio impactor as a pre-filter for the PM2.5 impactor)
we continued to run into difficulty. By employing as many of the best-practices listed above we
expect to avoid many of these difficulties in the future.
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10.3 APPENDIX C: RESPONSES TO COMMENTS RECEIVED RE: CALIFORNIA
SPRING 2008 TILLAGE CAMPAIGN: DATA ANALYSIS REPORT
Responses to comments are in italics with all changes to the report text indented. Note that all
page numbers mentioned refer to the page number in the final version.
10.3.1 Draft Date: 16 March 2011 (Organization: U.S. EPA)
10.3.1.1 General Comments
1) Since the goal of this project was to calculate emissions for the purposes of assessing
control efficiencies and any emission calculations were ultimately used to compare
emissions from a conventional practice with one that reduces emissions, we believe
that it is appropriate to use a combination of point samplers and inverse modeling as a
part of the methodology used to calculate emissions. EPA would want to further
scrutinize this methodology if the main goal of the study was to inform baseline
emission factors.
No response required
2) The dependence on the use of historical emissions factors and the comparison to
emissions factors estimated in this study is understandable. In addition, the
discussions describing the variations of measured emissions and the uncertainties
associated with historical emissions factors is refreshing. However, some of the cited
uncertainties appear quite low compared to some recent assessments of emissions
factors uncertainties of several sources which would be thought to be less variable
than tilling emissions (see http://www.epa.gov/ttn/chief/efpac/uncertainty.html ). It is
suggested that the uncertainties associated with the historical emissions factors and
the estimated emissions factors associated with this study be re-evaluated based upon
the conclusions in the draft emissions factor uncertainty report (i.e. the number of
independent test runs and the underlying variability of the measured emissions). Then
some of the processes which are described as not being in close agreement may be
more in line with the overall uncertainties associated with the emissions factors and
variabilities.
The authors agree that a better analysis of tillage emissions factors uncertainty is
warranted and have done so to the extent data are available, following the emissions
factor dataset analysis procedure used in the emissions factor variability assessment
referenced above. Results of two additional studies that have been released since
submittal of this document to EPA have been included in the literature review. The
following paragraph describing the uncertainty analysis methodology and results has
been added to the end of the literature review section (page 10). Refer to page 11 in
the document to view the tables and figures.
"An uncertainty analysis was conducted to determine the statistics of the
preceding PM10 emissions factors reported from measurements. This analysis
was performed following the emissions factor dataset analysis technique used by
RTI International in 'Emission Factor Uncertainty Assessment, Review Draft'
[29]. Data points were categorized according to the following tillage operations,
with the number of values given in parenthesis: chisel (2), disc (67), land planing
(1), listing (8), ripping (5), root cutting (3), standard tillage planting (11), strip-
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till planting (9), strip-tilling (6), and weeding (15). In cases where a report/paper
only provided an average emissions factor, the average value was used only once.
RTI International found that two parametric models, the lognormal and Weibull
distributions, best fit the analyzed datasets. These same two parametric models
were fitted to the tillage operation datasets with eight or more data points. The
estimated parameters for these models and the corresponding goodness-of-fit to
the available data points, expressed as the root mean square error (RMSE), are
given in Table 5 with the better fit model values in bold. Note that a smaller
RMSE represents a better fit to the data. The Weibull distribution proved to be a
better fit for four out of the five examined datasets. The fits to the disc and
weeding datasets were better than that for the remaining three, both visually and
based on the RMSE values. Figure 1 presents the histogram for the disc operation
dataset and cumulative density functions developed from the data and the Weibull
distribution fit to the data. The mean and median values of the fitted Weibull
distribution are shown on the cumulative density function line, along with the
emissions factor value given by ARE for a disc operation. The ARE emissions
factor of 134.5 mg/m2 is very close to the Weibull distribution median of 136.3
mg/m2. The emissions factor values corresponding to the 5%, 25%, median, 75%,
and 95% levels along the cumulative distribution curve, as well as the average
emissions factor, were calculated for the five operations and are presented in
Table 6. The 95% level emissions factors for the three operations with poorer fits
(i.e., higher RMSE) seem very high; this is likely an artifact of fitting the
distributions to a limited number of datapoints. In this analysis, the better fits
were obtained for datasets with n > 15. "
The discussion of the results on page 86 of the document has been updated to reflect
the findings of the uncertainty analysis, which did indeed include a portion of the
higher values as suspected by the reviewer. The paragraph now reads:
"Some of the values herein reported are in agreement with those reported by
Flocchini et al. (2001) and Madden et al. (2008), as well as the PM10 emission
factors used by CARB, such as the strip till and plant passes in the conservation
tillage method and the cultivate and roll passes in conventional tillage
[12][16][13]. Other emission rates are significantly different and larger than
previous values reported in the literature, especially the discing 1 and 2, chisel,
and lister passes of the conventional tillage, though those derived through inverse
modeling are below the estimated 95% level for their respective distributions as
presented in the uncertainty calculations in this report. The lidar-derived
emission rates for the disc 1, disc 2, chisel, and lister passes are high when
compared to values found by inverse modeling coupled with OPC PM data,
values in the literature for the same operations, and values reported in the 2007
fall CMP tillage study which used the same lidar methodology. These relatively
high emission rates provide indirect support for the conclusion that downwind
PM samplers were likely overloaded during those sample periods by high aerosol
concentrations. While the values from listed published studies are generally not in
close agreement, they are relatively well fit by lognormal or Weibull distributions,
which have previously been shown to represent emissions factors datasets well
[31]. In addition, they are within the range of the variability expected from
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measurements made under different meteorological and soil conditions, as
demonstrated by the wide range of values from Flocchini et al. (2001) [12]
summarized in Table 2. The results from this campaign are not in as good
agreement as previous results have been [7]. In general our results are larger
than their corresponding literature values. "
A sentence has also been added to the executive summary on page 2 about the
comparative results with the uncertainty analysis:
"Emissions factor values estimated through inverse modeling for these operations
are below the 95% level predicted by statistical distributions fitted to published
data; emissions estimates from lidar for the same operations are above the 95%
level"
3) The use of reverse modeling for particulate matter is of concern since the deposition
and removal mechanisms are limited in EPA models. The EPA models only have a
percentage of the parti culate reflect off a smooth surface when in reality, the ground
surface is not smooth and if there is vegetation more removal may occur. In addition,
electrostatic and other forces may cause small particulate to aggregate to form larger
particulate. These effects are not included in EPA models and as a result a reverse
model would tend to underestimate the actual source strength. The amount of
underestimation may depend upon not only these items but also the distance at which
the measurement is removed from the emissions source.
The authors agree with the limitations of the ISC andAERMOD models stated by the
reviewer concerning deposition and removal mechanisms, resulting in an
underestimation of the actual source strength. The effect of the distance at which the
measurement is removed from the source was minimized in this study by locating the
sampling sites immediately adjacent to the field under study; additionally, the
constant motion of the tractor and implement lead to varying distances between the
measurement and source. It should also be noted that any emission estimation
methodology for a tillage source that does not include a correction for the deposition
and removal of PM due to surface roughness, vegetation, etc. as a junction of
distance from the source will underestimate actual emissions. The following text has
been added to the inverse modeling methodology section on page 38:
" In addition, the deposition and particle removal mechanisms are limited in the
ISCST3 and AERMOD models herein employed. Insufficient correction within a
given model for these and other processes that decrease downwind pollutant
concentrations will lead to an underestimation of emission rates based on
measured downwind impacts."
10.3.1.2 Comments about Appendix B
1) While the selection and use of low-cost, battery-powered samplers is understandable
in a study of this type (particularly requiring a large number of concurrently operated
samplers), it should first be recognized that the MiniVol samplers are not EPA-
approved reference method or equivalent method samplers for making either PM2.5
or PM10 compliance measurements. Although the MiniVol samplers have
sometimes been shown to provide similar PM concentrations to collocated FRM
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samplers under some sampling conditions, their ability to extract representative
samples of large particles under high wind speeds has not been demonstrated. In fact,
a limited wind tunnel evaluation of the MiniVol samplers (conducted by Research
Triangle Institute in 1991) indicated that the sampler's ability to obtain representative
aerosol samples degrades rather dramatically as a function of wind speed. In
agricultural environments where elevated wind speeds are the not at all uncommon,
representative sampling of large aerosols characteristic of tilling operations cannot be
assumed. This is particularly the case when interpreting data from MiniVol samplers
operated as "TSP" samplers. For this reason, data obtained with the MiniVol
samplers in this type of study should only be considered as approximate measures of
PM2.5, PM10, and "TSP" concentrations.
We thank the reviewer for bringing to our attention the collection efficiency versus
wind speed issued found in the 1991 MiniVol study. This is an issue of concern,
especially when dealing with aerosols with relatively large size distributions as found
downwind of most agricultural sources. It should be noted that the sampler model of
those tested in 1991 were < 3.x, while the model number of the units employed during
this study were 4.2. Attempts to contact Airmetrics to inquire about potential changes
to the sampling inlet/size separator that might affect sampling efficiency between the
models have not yet been successful.
Text was added to the Section 3.1.4.1, page 27, which describes the MiniVols, to
clarify that the MiniVol is neither a FRMnor a FEM.
"The MiniVol is a battery operated, ambient air sampler that gives results that
closely approximate air quality data collected by a Federal Reference Method
(FRM) PM sampler. The MiniVol is neither designated as an FRM nor a Federal
Equivalency Method (FEM) by the EPA, and results should be considered as
approximate measures ofPM."
A short discussion of general meteorological conditions observed during the
sampling periods and a table listing period-average values has been included in
section 4.1.2.2, page 43. The period-average wind speeds observed during this study
varied between 2 and 6 m/s, within the range of 0.5 m/s and 6.7 m/s at which the 1991
study was conducted.
Section 4.1.2.2, page 43, reads:
"Meteorological characteristics were monitored on-site throughout the field
study. Sample period average conditions were calculated and are presented in
Table 14 based on measurements taken at the WM and EM locations. As can be
seen from these data, all measurements were made during warm and dry
conditions. Winds were consistently out of the northwest with average speeds
between 2 and 6 m/s. "
2) Appendix B provides an account of discrepancies noted during visual observations of
collected field filters and offers several possible explanations for these
observations. While the discussion on page 108 correctly points out that there may be
differences in the position and shape of the MiniVol and FRM's fractionation curves,
it is not correct to state "Ideally, the collection efficiency of the size fractionation
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device would be a step function that goes from 0% to 100% at the desired cut-off
size." In fact, the slope of the FRM's PM10 fractionation curve was intentionally
designed to match the performance of the human respiratory system - which does not
provide step-function performance. The fractionation curve of the PM2.5 FRM was
also not designed to provide step-function performance.
This error has been corrected in the text with the first and second sentences of this
paragraph, page 113, now stating
"The collection efficiency curve of the PM10 and PM2.5 size fractionation
devices employed by both FRMs and MiniVols are S-curves with respect to
particle aerodynamic diameter, designed to mimic the particle removal efficiency
of the human respiratory system. The removal efficiency curves have a 50%
collection efficiency at the designed cut-off size, with some smaller particles being
removed and some larger particles passing through the system. "
3) As hypothesized on page 109 of the report, observations of very large particles on
collected field filters and of uncharacteristic deposition patterns are almost certainly
due to "particle bounce" occurring in the MiniVol samplers. Unlike EPA's PM2.5
FRM which uses either the WINS well or the very sharp cut cyclone (VSCC) to
minimize substrate overloading and particle bounce, the MiniVol samplers were not
designed to handle high concentrations of ambient aerosols. The design of the
impaction substrate for both the PM2.5 and PM10 MiniVols is such that particle
bounce may occur from the impaction stage once a few monolayers of particle
deposits occurs during sampling. Once particle bounce begins, the cutpoint of the
stage can shift dramatically upwards and result in inaccurate PM concentration
measurements. In the case of the MiniVol design, the reduction in a cutpoint from 10
micrometers to 2.5 micrometers requires a reduction in impaction jet diameter from
approximately 0.69 cm to approximately 0.29 cm. As a result, the jet velocity
through the PM2.5 MiniVol is approximately 5.5 times that of the PM10 MiniVol.
This increase in jet velocity results in an increase in particle kinetic energy and makes
the PM2.5 MiniVol more susceptible to particle bounce than the PM10 MiniVol.
And because the PM2.5 MiniVol has a much smaller stage deposition area than that
of the PM10 MiniVol, it is possible for the mass gain of PM2.5 MiniVol's filter to
exceed that of the PM10 MiniVol filter. As a result, measured PM2.5 concentrations
may often exceed those of collocated PM10 concentration measurements. Although
occasional inadvertent switching of PM2.5 and PM10 filters was noted in the report,
substrate overloading was almost certainly the cause of the majority of the "inverted"
PM2.5 and PM10 measurements.
The authors thank the reviewer for providing a very good analytical description of
the particle bounce phenomenon as it may occur in the MiniVol impactor assembly.
The discussion under point 3 on page 113 in the report has been changed to include
portions of this description. It now reads:
"A phenomenon commonly referred to as "particle bounce " may have occurred.
Particle bounce refers to when a particle collides with the impactor assembly but
then returns to the air stream and is collected downstream at the filter. To aid in
removal of particles colliding with the impactor plate, grease may be applied on
SDL/08-556 California 2008 Tillage Campaign 124
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the impaction surface. If too many particles accumulate in the grease, however,
the impaction surface loses its "stickiness" and larger particles may bound off,
leading to higher reported PM2.s/PMjo concentrations than actually existed.
Impactor plate stickiness is influenced by a combination of total exposure time
and PM concentrations during exposure. The smaller effective impact area and
increased particle velocity in the MiniVol PM2.5 impactor assembly makes it more
susceptible to particle bounce than the PMjo assembly under the same conditions,
which could result in higher reported levels of PM2.5 than PMjo- Airmetrics, Inc.
suggests cleaning and regreasing of the impactor plates every five to seven
samples to maintain the design removal efficiency, but states that the need to
renew the plate may change based on exposure levels. In the case of this study,
silicone-based, high-vacuum grease was applied to all impaction surfaces on-site
prior to sampling in May. Personnel noted that impactor assemblies in downwind
samplers had collected significant amounts of particles near the end of the seven
sample periods in that month (May 17-20), which suggests that particle bounce
likely occurred during some sample periods. The sample heads and impactor
assemblies were cleaned and greased during the break in measurements, with
evaluation and re-greasing if necessary after each measurement in June. In
addition, the enhanced susceptibility of the PM2.5 assembly to particle bounce
could explain the inverted PM2.5 and PMw concentrations observed at downwind
locations during the May 19 and May 20 sample periods. "
4) If funding were not a limitation, a much better alternative to the use of the MiniVols
would be BGI PQ100 samplers. These 16.7 Lpm, filter-based samplers are EPA-
designated for both PM2.5 and PM10 compliance testing, and operate with 120 VAC
line power or internal 12 VDC battery (with solar panel option). The PM2.5 version
of the BGI PM2.5 PQ100 can be equipped with a 2.5 micrometer cutpoint VSCC
which has been shown to maintain its fractionation performance under high loading
conditions. Use of PQ100 samplers should totally prevent the occurrence of PM2.5
and PM10 data inversion that was reported in the study. Even if a suite of these BGI
samplers could not be procured, it would be useful to collocate a pair of them with the
currently operating MiniVol samplers in order to help isolate and resolve the
performance issues currently noted with the MiniVol samplers. Funding required to
purchase a pair of the PQ100 samplers would more than be compensated for the
resources currently being expended attempting to extract and interpret useful
information from field data of uncertain quality.
The BGI PQ100 units have recently been investigated as potential future purchases.
The reviewer's description and suggestion for comparison with the MiniVols
currently deployed are compelling and a future purchase of some PQlOOs is planned.
This comment highlights our ongoing effort to utilize instruments of higher fidelity,
such as the BGI PQ100.
5) If continued use of the MiniVol samplers is envisioned, it is absolutely critical that
the MiniVol's impaction surface be renewed after every sampling event. In the
MiniVol design, this is not a difficult or time consuming process. While this
procedure will not totally prevent substrate overloading during sampling of high
concentration aerosols, it will at least minimize the effect and should substantially
SDL/08-556 California 2008 Tillage Campaign 125
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reduce the frequency of the PM2.5 and PM10 concentration "inversion" thats
currently being observed.
It is anticipated that the MiniVols will continue to be used to measure ambient and
near-source PM. The reviewer's suggestion to renew the impaction surface between
each use will be implemented in future uses. This has also been included as a
potential preventative action in the Lessons Learned section, page 90, under the
secondpoint:
"Another preventative action is to clean and regrease the MiniVol impactor
assemblies after each use if exposed to high aerosol concentrations. "
10.4 DRAFT DATE: 15 APRIL 2013 (ORGANIZATION: SAN JOAQUIN VALLEY
AIR POLLUTION CONTROL DISTRICT)
1) Page 45 Section 4.2.1 discussion of 165 of 296 samples rejected by QA requires some
immediate discussion as well as reference to Appendix B. This amount of QA
rejection is very high and will raise an immediate red flag. The importance of this
issue merits added text in the paragraph.
A summary of likely causes of the high failure rate has been included. The section on
page 47 now reads as follows:
"Of the 296 filter samples collected, 165 did not pass quality analysis/quality
control (QA/QC) steps applied to the dataset, leaving 131 for use in calculating
emission rates using inverse modeling. An investigation into this high rate of
failure was conducted and a detailed description is provided in Appendix B. In
summary, filters that did not pass QA/QC were suspected to have been
contaminated either during sampling or during storage and handling. Evidence of
"particle bounce " was found on many PM2.5 and PMio samples collected during
May sample periods. Particle bounce occurs when particles that collide with the
impactor plate, the mechanism used by the MiniVols to exclude particles larger
than the design size, are re-entrained in the air stream and collected on the filter
downstream and result in higher measured levels than actually existed. This issue
is most likely due to exposing the MiniVol samplers to dust plumes exceeding the
maximum recommended exposure level and improper instrument maintenance
and cleaning through the May sample periods. Corrective action was taken
during the June sample periods and no issues associated with particle bounce
were observed in the second portion of the study.
"Additionally, some particles were observed on top of and imbedded into the
plastic annular ring around the Teflon filter material - the plastic ring is covered
by the filter holder assembly during deployment. This was likely due to
contamination during on-site filter storage or handling. Efforts were made to
minimize this issue throughout, especially during the June sample periods.
However, windblown dust did impact the handling and storage area during the
last sample periods in May. In addition, contamination of upwind samplers
prevented emission rate calculations for some size fractions for two other
operations. The size fraction distribution of approved filters was nearly identical
to the total sample set: 51 (39%) were PM2.5, 50 (38%) were PM10, and 30 (23%)
SDL/08-556 California 2008 Tillage Campaign 126
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were TSP. These finalized concentrations are given in Table 33 and Table 34 in
Appendix A. See Appendix B for a detailed discussion of the QA/QC steps, filter
inspection failure, possible causes of the failures, and preventative solutions for
future sampling."
2) In sharp contrast to section 4.2.1, sections 4.2.3 through 4.3.3 (starting at page 49)
never mention QA. Some discussion of data completeness and redactions should be
included for these sections.
Sentences and paragraphs addressing QA have been included in each of the sections
referred to above. Some of the material were previously in the methodology portion of
the report and have been pulled into the results section. While the lidar concentration
measurements section had some discussion of QA screening, it has been augmented,
clarified, and moved to the first paragraph of the section.
Page 48, 4.2.2.1 Organic Carbon/Elemental Carbon Analyzer
"As mentioned previously, the organic carbon/elemental carbon analyzer passed
the manufacturer's suggested in-field audits and after completion of the field
project the data were manually screened for completeness and potential outliers.
During the two distinct periods of sampling, May 13-20 and June 3-21, 2008, the
EC/OC instrument operated continually except for brief periods for QA/QC
checks, servicing of the system generator, or significant breaks in the producer
operations. An unanticipated consequence of the one hour sample times, coupled
with the dual channel operation of the R P 5400 EC/OC Analyzer, was that the
sampling/analysis/cleaning cycles extended beyond the planned two hours. The
net result was a sampling profile wherein every third hour of data was missing
(66% sample collection efficiency over the entire deployment). However, because
the actual farm practice periods varied from one to eight hours, the actual,
observed data periods ranged from one to eight hours, the data coverage was
from 50-100%, averaging 78.2% ± 10.3%. "
Page 50, 4.2.2.2 Ion Chromatographic (1C) Analysis
"As such, a total of nine filters were analyzed for soluble ions. However, two of
these filters, from 5/20/08 and 6/5/08, were subsequently suspected as potentially
having contamination issues (see Section 4.2.1) and were discarded from further
analysis."
Page 54, 4.2.3 Aerosol Mass Spectrometer
"During the tillage experiment, the AMS acquired chemical composition data
from May 14-May 19 with some significant gaps in the data due to mass
spectrometer malfunctioning. This required manual screening of the AMS data.
Approximately 35% of data over the time period was found to be valid. Large
gaps occurred throughout the sampling period with, for example, no data
acquired on May 17. "
Page 56, 4.3.1 Met One Optical Particle Counter
"The collected OPC data were analyzed for particle size distribution, particle
volume concentrations, and converted to particle mass concentration through
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multiplication with the MCF, as described in Section 3.1.4.2. Table 35 and Table
36 present the PM2.5, PMw, and TSP concentrations as reported by the OPCs.
Three to four OPCs were in positions immediately downwind of the field under
study in each sample period, with between one and four OPCs in upwind
locations. Unlike the downwindMiniVol samplers, the downwind OPCs were not
overwhelmed by the dust plumes from the tillage activities - the manufacturer
specified range of the OPC ofO to 318,000,000 particles/m3 was never exceeded-
and thus provided usable data throughout all sample periods. Upwind OPC time
series data were examined for contamination from activities upwind of the site,
such as unpaved road traffic. Contamination was found in six of the 12 sample
periods, with five of those occurring at the 10.0 sample site that was immediately
downwind of an unpaved road (see Figure 7). Large spikes indicative of
contamination were removed from the upwind OPC time series data in these
instances to estimate the background aerosol concentration; the estimated
background levels were in very good agreement with those measured by an OPC
at a different, uncontaminated upwind location (see Table 37 and Table 38).
Data completeness for the OPC datasets was calculated as a ratio of the number
of valid samples per sample period over the possible number of valid samples and
expressed as a percentage. Data completeness was less than 100% due to
communication errors between the OPCs and the computer logging the data,
resulting in lost packets of 20 second sample data. Communication error
frequency was variable between OPCs and across time. Data completeness per
sample period ranged from 81.8% to 100.0%, and averaged (± la) 97.4 ± 3.7%. "
Page 61, 4.3.2 Optical to PMmass concentration conversion
"As OPC data collected at each site and during each sample period passed
QA/QC, the calculation of MCF values was dependent solely on the presence of a
valid filter-based PM measurement. "
Page 66, 4.3.3 Lidar Aerosol Concentration Measurements
"Lidar data was collected throughout all sample periods, except for the
Cultivator 3 pass monitored on June 25 due to an equipment failure after the
previous measurement. All lidar scans collected during times when no tillage
activity was occurring, based on detailed field notes, were removed from further
calculations. The remaining scans were visually checked to remove scans with
data acquisition errors and to prevent the use of data that was contaminated by
other sources; sources of observed contamination were vehicular traffic on
unpaved roads, agricultural activities immediately upwind, activities associated
with the adjacent dairy, and windblown dust. Contaminated upwind scans, as well
as the corresponding downwind scans, were removed from further emission rate
calculations. In most cases two or more downwind scans use the same upwind
scan as a reference because multiple downwind scans were made for each upwind
scan. Downwind scans were not used in further calculations if the corresponding
wind direction was outside of ± 70° from perpendicular to the downwind lidar
beam path; if the lidar scan contained apparent plumes from an outside source
(such as from unpaved road traffic or the dairy); if no plumes were detected; or if
SDL/08-556 California 2008 Tillage Campaign 128
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the tillage plume had a potentially significant portion crossing the lidar beam
closer than 500 meters down beam. In addition, upwind scans were invalidated if
none of the associated downwind scans were usable. Light wind speeds (< 1 m/s)
were recorded during portions of some measurement periods. Light winds
challenge the measurement system and resulted in additional invalidation of some
lidar scans. The total number of lidar scans made along both upwind and
downwind planes is presented in Table 19, along with the number of valid scans,
i.e. those that passed all quality checks, and the percent of valid scans compared
to the total number of scans.
In several cases, the percent of valid scans was very low. Although wind and
background conditions were good during the herbicide application on 6/11, none
of the scans taken were deemed valid because the mass difference between the
upwind and downwind scans was below the minimum detection level (MDL). This
means that the operation didn't produce plumes significant enough to be detected
by the lidar; this operation was performed by a very small tractor pulling a small
spraying apparatus - the only disturbance of the ground was due to moving tires.
Plumes of insufficient concentration differences from background levels led to
downwind scan invalidation in many other instances also. The dairy pen areas
adjacent to Field 4 proved to be sufficient PM sources such that lidar scans
showing dust plumes passing over the pens prior to crossing the scanning plane
were nearly always invalidated. Additionally, windblown dust was entrained off of
both Field 4 and upwind field surfaces during both the May 20 sample periods.
All of these factors combined to significantly decrease the number of valid lidar
scans available for emissions estimation from most operations. "
3) Strip till is mentioned beginning at page 1, but never clearly defined. Page 13 comes
close but there is no adjacent description for comparison to conventional tilling. The
two terms, as observed for the study, should be clearly defined.
A sentence has been added to Section 2.2, Operation Description, to describe a
conservation tillage CMP and some text has also been added further down in that
paragraph to contrast the level of soil disturbance between the strip-till and discing
and plow ing passes of the conventional method. It now reads as follows:
"As described in the Conservation Management Practices Program Report
(2006), the conservation tillage CMP "involves using a system in which the soil is
being tilled or cultivated to a lesser extent compared to a conventional system "
and it is "intended to reduce primary soil disturbance operation such as plowing,
discing, ripping, and chiseling". The Conservation Tillage CMP under study is a
strip-till method which combines multiple operations to reduce the number of
passes required and disturbs the soil only in 8 "-wide strips centered every 30 "
instead of disturbing the entire surface like the plowing, discing, and listing
operations of a conventional method. "
4) One question is why the total |ig/(m2 x s) PM emission rates were used to derive the
percent emission reductions when comparing the conventional tillage and
conservation tillage methods rather than comparing the total mg/m2 emission rates.
SDL/08-556 California 2008 Tillage Campaign 129
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Please provide some clarification on why this metric was selected to compare PM
emissions from the practices.
PM emission factors for tillage are generally given in mass per area for each
operation (e.g. Ib-PMlO/acre-pass, mg-PM10/m2-pass, etc.) and this was also the
basis for comparing PM emission rates from this study with emission rates found in
literature and used by ARB (see Page 4, Table 1; Page 6, Tables 2 and 3; Page 7,
Table 4 and following paragraph; and Page 87, Table 28). It would seem that
comparing the total mg/m PM emission rates for conventional tillage and
conservation tillage would provide a more useful and accurate representation of the
change in PM emissions. At a minimum, the percent reductions based on the total
mass of PM emitted per area tilled should also be shown and included in the
conclusions. This would not really change the conclusions of the report but would
probably be a more accurate representation of the total PM emission reductions.
The authors agree that this comparison should be made using an emission factor
rather than an emission rate. The values and corresponding control efficiency
calculations in Table 29 have been changed to emissions in mg/m2 format. Text in the
conclusion, page 86, has been changed to the following and numbers in the executive
summary have been updated:
"Emission rates for PM2.5, PMjo, and TSP from both lidar data and inverse
modeling coupled with OPC data by operation are presented in Table 29 in units
of mass emitted per unit area tilled per operation pass.... The conservation tillage
method produced 9.5% as much PM2.5, 6.3% as much PMjo, and 9.1% as much
TSP as the conventional method according to the lidar data and 14.7% as much
PM2.5, 12.8% as much PMw, and 9.7% as much TSP as the conventional method
according to the AERMOD-OPC combination. Therefore, the control efficiency of
the CMP for particulate emissions from these three data sets was as follows: lidar
- 0.91, 0.94, and 0.91 for PM2.5, PMW, and TSP, respectively; and AERMOD-
OPC-0.85, 0.87, and 0.90 for PM2.5, PM10, and TSP, respectively. "
10.4.1.1 Comment for Further Discussion
1) Interagency discussion should review the difference between entrained and emitted.
Measuring too close to a source will observe entrained material that will not remain in
the air; it is entrained but will not be observed as an atmospheric contaminant
downwind. We need to be sure that we have reasonable definitions for quantification
of emissions. This impacts review of the best modeling approach to determine
emissions - do we mean everything entrained or only that portion of material that will
mix in the atmosphere sufficiently to be observable downwind and impact public
health.
EPA and SJVAPCD agree that additional discussions need to occur about definitions
and methodologies.
SDL/08-556 California 2008 Tillage Campaign 130
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10.4.2 Presentation of Results and Discussion with the San Joaquin Valley Air
Pollution Control District's Ag Technical Group: 22 April 2013
10.4.2.1 Notes
USDA and the Space Dynamics Lab (SDL) did a presentation on the study results, emphasizing
that the conservation tillage practice that was assessed reduced emissions by over 85% . USDA
also noted that while different conservation management practices (CMPs) have differed
reductions, the method with the greatest reduction in passes and soil disturbance of those studied
had the greatest reduction in emissions. EPA stated that it would use the study results to help
create incentives to implement conservation tillage, and that the Agency understands that
variable conditions on farms restrict the applicability of this measure. Stakeholders and USDA
also noted that producers needed flexibility to address different crops and on-farm conditions,
and that the measure that was assessed in this research is not appropriate for every farm.
Stakeholders also pointed out that in addition to reducing emissions through reductions in passes,
California agriculture has reduced emissions by switching to equipment with cleaner burning
diesel engines. The discussion included the questions below from stakeholders, which were
answered by USDA and SDL.
The following sentence was included in Section 2.1 Site Description, page 12, to state the
point brought out by stakeholders and USDA that tillage operations are not used in all
agricultural crop production systems: "Tillage management practices are often crop
specific and are not appropriate for use in all crop production activities. The
effectiveness of CMPs used in other crop systems at reducing PM emissions should be
investigated."
10.4.2.2 Questions and Answers
Additional comments and portions of the report related to questions are provided after the answer
in the indented sentences
Q: Do the results effectively capture the emission reductions associated with less fuel usage?
A: We were able to calculate this on the previous study because the producer quantified the
amount of fuel going into his tractor. We anticipate that it's tied to the amount of time using fuel,
and would guess that there's probably a close to 85% reduction in fuel emissions also.
The previous study referenced in this answer is Williams et al. (2012; available:
http://cjpub.epa.gov/si/sijfublic record' report.cfm?dirEntryId=248752) in which the
control effectiveness of a Combined Operations CMP was investigated by USDA and
SDL in Fall 2007.
The following sentences have been added to the conclusion section of the report on page
89 to address the additional reductions through reduced tractor operation time:" It
should be noted that other reductions in emissions between the two tillage management
practices are likely to have occurred, mainly due to decreased fuel usage in tractor
engines. Unlike the previously conducted companion study, fuel usage was not quantified
in this study and prevented accounting for the associated reductions in emissions.
However, these reductions are expected to be similar to the reduction in tractor
operation time (-85%)."
Q: Is it possible that there are some extra reductions to be claimed?
SDL/08-556 California 2008 Tillage Campaign 131
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A: Yes, if we looked at carbon emissions as well as potential PM coming from fuel usage we
could get additional reductions— we didn 't capture all of that with the samplers we had in the
field for this study.
See the response to the above question.
Q: Did you analyze yield differences?
A: We were primarily interested in emission aspects and didn't do any yield comparisons in this
study.
The following sentence has been added on page 12 to clearly state this in the report:
"The focus of this study was comparing emissions resulting from the tillage operations in
each field. Therefore, no data were collected to make comparisons of other potentially
varying characteristics, such as crop yield, soil organic matter, etc., between the two
management practices."
Q: Were either of the fields already in conservation tillage?
A: The field that was under conservation tillage had been under conservation tillage for several
years; the other one was still being operated with conventional tillage with all the passes.
This is addressed on page 13 of the report
Q: Did you look at the difference between soil temperature or ambient air temperature?
A: We did not make soil surface temperature measurements. We agree that this factor may have
a significant effect on emissions transport. We plan to make such measurements in future studies.
An additional paragraph has been added to the Lessons Learned section, pages 90-91, to
address this comment in the report: "In addition to the above lessons learned, peer
review of this report also brought to our attention that measurements of additional
parameters should be considered in future studies. Specifically, it was suggested that soil
surface temperature measurements be made. Differences in soil surface temperature may
result in different vertical and horizontal PM dispersion (with all other conditions being
equal) as surface conditions are known to affect turbulence and dispersion. "
Q: Were different tractors used for conventional vs. conservation tillage?
A: It was usually the same tractor but for some of the passes a different tractor may have been
used.
The equipment, including the tractor, used for each tillage operation are provided in
Table 8, page 17
Q: Did you do anything with N20?
A: No. We didn't have the equipment at the time of the study.
All equipment deployed for this study is provided in Section 3, pages 20-40.
SDL/08-556 California 2008 Tillage Campaign 132
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