Regional Ammonia & Windblown Dust Models Status &
Lessons Learned Gerard Mansell ENVIRON International WRAP Workshop
on Regional Emissions & Air Quality Modeling Studies Denver, CO
29-30 July 2008 Slide 2 PRESENTATION OVERVIEW Windblown Fugitive
Dust Fugitive Dust Sources Regional WB Dust Modeling WRAP WB Dust
model oMethodology Development oSensitivity Studies oModel Results
Specific Model Applications & Enhancements Summary &
Recommendations Regional Ammonia Models Inventory Development &
AQ Modeling issues WRAP NH3 Model oModel Development & Data
Sources oModel Results Other Regional NH3 Models oCarnegie Mellon
University (CMU) NH3 Model oLADCo Process-Based NH3 Model Summary
& Recommendations Slide 3 FUGITIVE DUST What is fugitive dust?
Particulate matter (PM) emissions that can be suspended in air that
are not ducted or capable of being ducted Examples Entrained paved
and unpaved road dust Construction and demolition activities Mining
and mineral processing activities Agricultural operations Windblown
dust from disturbed surfaces Others (unpaved shoulders, leaf
blowers) Slide 4 Fugitive Dust Source Emission Estimates
Traditional: AP-42 (USEPA) Emissions = activity level x emission
factor Emissions are not spatially-resolved - need to use spatial
surrogates, GIS overlays CARB fugitive dust methodologies:
http://www.arb.ca.gov/ei/areasrc/index7.htm
http://www.arb.ca.gov/ei/areasrc/index7.htm New: Emission Models
Emissions are calculated by an emissions model that attempts to
capture the physical properties of the source oExample: windblown
dust models Better, spatially- and temporally-resolved emission
estimates Need significant input information Technical expertise to
exercise Slide 5 Examples Unpaved Roads AP-42: emissions a function
of VMT, silt content, speed, weight, number of wheels and mean
precipitation Controls: speed limits, water, chemical stabilizers,
gravel, paving, access restrictions Construction CARB Emissions =
activity (in acre-months) *EF (0.11 tons/acre-month); assumes
watering Spatial surrogates needed oSouth Coast using GIS-based
approach Controls: watering, phased grading, stabilization (unpaved
roads, disturbed areas), track-out controls Agriculture Tilling (by
crop): acreage, acre-passes Harvest (by crop): acreage,
crop-specific EF Controls: Conservation Management Practices (CMPs)
Windblown Dust Equation-based: WEQ, RWEQ, WEPS, etc. Emissions
models: ENVIRON/RMC, Draxler et al., Zender et al. (DEAD model),
Shao Controls: watering, stabilization, re-vegetation, access
restrictions, CMPs Slide 6 WRAP/RMC WINDBLOWN FUGITIVE DUST MODEL
BACKGROUND WRAP recognized the need for WB Dust inventory in
regional haze modeling efforts previously no dust from wind erosion
available on regional-scale Recognition of relative magnitude and
importance with respect to other source categories in regional
emission inventories Consideration of EPA guidance &
requirements DEJF funded a number of studies to investigate and
evaluate impacts of dust on visibility and air quality at Western
Class I Areas Causes of Haze (COHA) project Causes of Dust (COD)
Analysis of Fine Fraction of PM in Fugitive Dust Phase I & II
WB Dust studies funded to develop methodology and modeling system
to estimate WB Dust emissions applicable for Regional Haze Modeling
Slide 7 METHODOLOGY DEVELOPMENT Phase I Developed a general
methodology and modeling approach/platform Identified and evaluated
data requirements and sources oSurface characteristics LULC; Soil
Texture oMeteorology MM5; CALMET; other oAgricultural data Crop
types, calendars, CMPs (i.e., tilling, harvesting, irrigation,
etc.) oEmission Factors and threshold velocity relationships
Utilized a simplified estimation methodology Phase II Reviewed and
refined overall objectives Conducted literature reviews oGlobal
models Zender, 2003; Draxler et al., 2001; Shao, 2001; Marticorena
et al., 1997; Alfaro et al., 2003 oField studies Gillette et al,
1988, 1982; Gillette, 1988; Nickling & Gillies, 1989 oOther
wind erosion models - Mac Dougall method; Alfaro, et al, 2003
Identified and evaluated current updated databases LULC Developed
and implemented revised emission estimation methodology Sensitivity
studies oDust reservoir treatment and assumptions oSoil disturbance
assumptions Other Model Parameters oLULC (1992 NLCD vs. 2000 NALC)
oTransport Fractions oFine/Coarse PM ratios Slide 8 METHODOLOGY
DEVELOPMENT Phase II Estimation Approach Dust = f(LULC,z 0,u *,u
*th,SC) u * = f(u,z 0 ) u *th = f(z 0 ) z 0 = f(LULC) Threshold
Friction Velocities Emission Rates Slide 9 SENSITIVITY STUDIES
Sensitivity simulations performed to evaluate effects of soil
disturbance and dust reservoir assumptions Assumptions associated
with scenario (b) were used in all subsequent model applications
for the WRAP loose undisturbed soils; wind event duration limited
to 10 hrs/day Slide 10 OTHER MODELING INVENTORY PARAMETERS Land
Use/Land Cover (LULC) 1992 National Land Cover Database (1992 NLCD)
o2001 NLCD is now currently available high spatial resolution, more
detailed classifications 2000 North America Land Cover database
(2000 NALC) Slide 11 OTHER MODELING INVENTORY PARAMETERS LULC
Summary & Comparison LULC critical input as it determines
surface roughness threshold velocity wind erosion potential Slide
12 OTHER MODELING INVENTORY PARAMETERS Fugitive Dust Transport
Fractions Transport fractions used to model affects of near-source
removal of dust emissions through gravitational settling and/or
deposition to surfaces or captured in the surround canopy or on
physical structures Reduces the amount of dust emitted to the
atmosphere for regional air quality modeling. Applied to gridded
modeling inventory Transport fractions updated in all fugitive dust
emission inventories Slide 13 MODEL RESULTS Slide 14 How Important
is Fugitive Dust in Western States? Of the 68 State Implementation
Plans (SIPs) and Natural Event Action Plans (NEAPs) from Western
States: 30 cite dust as the primary pollutant to control 15 site
dust as the second highest pollutant Other PM10 contributors
include on- and off-road mobile emissions (e.g. diesel PM) and
pre-cursors such as NOx and SOx from combustion sources and ammonia
AreaFugitive PM10 Total PM10 % ambient PM10 from primary PM South
Coast20830830-40% Coachella Valley2324~95% San Joaquin Valley
26535629-36% Imperial County230236~100% Clark County NV419467~100%
Maricopa County AZ 164198~100% Slide 15 OTHER APPLICATIONS AND
RELATED STUDIES State/local NEI & AQ modeling inventory
development (HI, AR, AZ, WY, NV, FCAQTF, Columbia Gorge AQ Study,
many others) NEAP applications (Salt Lake City, UT area) PM
Nonattainment Area Maintenance Plans (Phoenix/Maricopa Co.;
Imperial Valley, CA) WRAP Dust Definition & New Mexico Pilot
Dust Regional Haze State Implementation Plan for the Salt Creek
Wilderness Area Slide 16 APPLICATIONS FOR PM 10 MAINTAINENCE PLANS
Phoenix/Maricopa Co., AZ Used Phase II Methodology LULC based on
more detailed local data MAG LULC data STATSGO Soil texture data
updated/augmented with SSURGO database Revised agricultural
adjustment info based on input from MAG staff oConsideration given
to temporal variation of disturbance levels based on crop calendars
Model revised to consider LULC-specific Z0 values & disturbance
levels for all LULC categories Sensitivity simulations implemented
for variations in soil disturbance for various LULC categories.
Significant improvements over standard model application realized
Imperial Valley, CA Used Phase I Methodology LULC based on more
detailed data CA DWR merged with NLCD Revised ag adjustment info
based on DWR data multi-cropping, inter-cropping, etc.. Sensitivity
simulations implemented varying threshold friction velocities.
Specific adjustments to LULC data based on local input LULC-based
Z0 values & soil disturbance adjusted for Imperial Sand Dunes
and surrounding areas Slide 17 Slide 18 APPLICATIONS FOR WRAP DUST
DEFINITION & NM PILOT DUST REGIONAL HAZE SIP Study Objectives
Collect & summarize fugitive dust PM10 emissions to support
Salt Creek Wilderness Pilot PM10 SIP project Provide fugitive dust
PM10 emissions for use in Dust Definition Case Study Develop
inventory refinements based on WRAP modeling data, revised spatial
allocation, local data, back-trajectory residence time analyses,
etc. Inventory Refinements and Evaluation Focus on emission sources
within 100-km buffer zone around Salt Creek Revised allocation of
county-level dust emissions to 100-km buffer zone using 2000 NALC
data Extract and evaluate modeling (gridded) data within buffer
zone Estimate wind blown dust emissions from unpaved road using
data from NMDOT and WRAP WB Dust model Estimate wind blown dust
emissions from O&G well sites using WRAP WB Dust model Assess
dust emission impacts at Salt Creek using back-trajectory analyses
Slide 19 APPLICATIONS FOR WRAP DUST DEFINITION & NM PILOT DUST
REGIONAL HAZE SIP Satellite photograph of oil and gas production
facilities. Lightly shaded areas are well-pads, which are connected
by access roads Most significant source: Windblown from shrub/grass
lands, much of which has been or can be grazed by cattle (mixed
source) Major sources: Other windblown dust (mixed sources);
agriculture, construction, road dust (anthropogenic) Conceptual
Model for Salt Creek identified an uninventoried major source: oil
and gas production areas / unpaved roads Slide 20 APPLICATIONS FOR
WRAP DUST DEFINITION & NM PILOT DUST REGIONAL HAZE SIP PM10
Dust Emission Inventory Refinements Unpaved road dust WRAP WB Dust
model applied to estimate additional dust from unpaved roads
Landuse input data developed using road mileage and assumed road
width Assumed barren land; distributed uniformly across grid cells
Sensitivity simulations used to evaluate assumed soil disturbance
levels Oil & Gas Well Sites WRAP WB Dust model applied to
estimate additional dust from unpaved roads Landuse input data
developed using well site location, status and acreage Assumed
barren land; allocated to grid cells using site location
Sensitivity simulations used to evaluate assumed soil disturbance
levels oUndisturbed; 100% disturbed; 50% disturbed Slide 21
APPLICATIONS FOR WRAP DUST DEFINITION & NM PILOT DUST REGIONAL
HAZE SIP Unpaved roads based on NMDOT unpaved road miles by County.
Assumed 27 ft width. Area distributed uniformly across counties.
Oil & Gas well sites based on acres per site. Location based on
geographic coordinates Slide 22 RECOMMENDATIONS & FUTURE WORK
Numerous assumptions are implemented in the model need to be
reviewed on a case-by-case basis for applicability Results are
highly dependent of accurate and detailed databases Model
limitations are related to regional-scale nature of input data can
be resolved through implementation at small local scale with
detailed surface characteristics databases Additional further
research is recommended Continued investigation and evaluation of
surface characterizations LULC; Surface roughness parameters; soil
disturbance Identification of local detailed databases Local scale
applications and evaluations Further investigation into various
model assumptions reservoir characteristics, soils, disturbance
levels Refinement of agricultural data and adjustments temporal
& spatial variations Slide 23 AMMONIA EMISSIONS & AQ
MODELING NH3 Emission Estimation & Modeling Issues Emission
Rates (Emission Factors) AP-42: E = A*EF Process-based: N mass
balance Spatial Resolution Locational accuracy of emission sources
oMobile roadways oAgricultural/Livestock ag land surrogates
oDomestic population distribution oFeedlots Impacts on AQ Modeling
oPoint source vs. distributed area source oAQ Model grid resolution
Temporal Patterns Temporal profiles Meteorology Activity data Slide
24 REGIONAL AMMONIA MODELS WRAP NH3 Model Overview Developed as
GIS-based modeling system Incorporates environmental parameters
soil pH, met data (winds, temperatures) Source categories include:
Livestock Fertilizers Native Soils Domestic Sources Wild Animals
Based on 2002 activity data (no future year projections done to
date) Monthly activity data for fertilizers; annual for livestock,
domestic, wild animals Activity data for soil emissions based on
LULC (2000 NALC) Hourly emission estimates based on temporal
variations of met data & temporal profiles No Mexico or Canada
Slide 25 MODEL DEVELOPMENT & DATA SOURCES Emission estimates
calculated as: E nh3 = EF * Activity EF based on whole animal
approach Activity data annual; monthly Emission factors adjusted
for environmental effects (soil pH; meteorology) Fertilizer
application Natural soils Temporal variation based on environmental
parameters & temporal profiles Livestock Fertilizer application
Natural soils Gridded emissions spatially allocated based on LULC
2000 NALC data; 2000 Census populations Model developed in Arc/INFO
GIS Slide 26 MODEL DEVELOPMENT & DATA SOURCES Activity Data
Livestock Headcounts National Agricultural Services Statistics
(NASS, 2003) Fertilizer Usage Assoc. of American Plant Food Control
Officers (AAPFCO, 2003) USDA NASS Natural Soils Land cover acreage
(2000 NALC; 1992 NLCD) Domestic 2000 US Census Pet per capita
ratios from Dickson et al., 1991 Wild Animals CMU NH3 model Slide
27 MODEL DEVELOPMENT & DATA SOURCES Battye et al (2003)
Recommended Battye et al (2003) Handbook Chinkin et al (2003)
Recommended Doorn et al., (2002) Measured Keener et al. (2001)
Pinder et al (2003) European Environment Agency (2002) Dairy
Cow2820-702513.1-55 28.5 Beef Cow10.29-189 14.3 Poultry0.28,
0.370.1-0.40.10.16, 0.52 0.28, 0.37 Swine11.49.5-13.577 6.39, 16.43
Horses812.2 8 Sheep1.343.4 1.34 Battye et al., 1994 EEA, 2002 Group
I EEA, 2002 Group II Pottet et al., 2001 Anhydrous Ammonia14442.4
Aqua ammonia1 2.4 Nitrogen solutions2.58882.4 Urea152015 2.4
Ammonium nitrate2.13212.4 Ammonium sulfate8151052.4 Calcium
ammonium nitrate 3212.4 Ammonium thiosulfate2.5 2.4 Other straight
nitrogen4 2.4 Ammonium phosphates4.85552.4 N-P-Ka 3212.4 Potassium
nitrate 2.4 Livestock Emission factors (kg/head/yr) Fertilizer
Emission factors (%N) Slide 28 MODEL DEVELOPMENT & DATA SOURCES
DescriptionEmission FactorSource Urban10 Battye, et al. (2003)
Chinkin, et al. (2003) Barren10 Forest174 - Deciduous 54 -
Evergreen 114 Mixed Shrub/Grassland400 Fallow205 Wetlands400
SourceEmission FactorUnit Cats0.348lb N/cat/yr Dogs2.17lb N/dog/yr
Human Perspiration0.55lb/person/yr Human
Respiration0.0035lb/person/yr Cloth Diapers6.9lb/infant/yr
Disposable Diapers0.36lb/infant/yr Soil Emission Factors
(kg/km2-yr) Domestic Emission Factors Slide 29 MODEL DEVELOPMENT
& DATA SOURCES Emission Factors Natural soil emission factors
adjusted to account for soil conditions (Potter, et al., 2003) EF
adj = EF * (1-M){1/[1+10 (0.09+2730/T-c*pH) ]} oM = soil moisture
oT = soil temp (K) oC = constant determines sensitivity to pH
(=1.3) Fertilizer emission factors adjusted for soil pH (Potter, et
al. 2001) EF adj = EF * (0.3125pH 1.01) Normalized for 4% at pH=6.5
Temporal Allocation Livestock Monthly profiles recommended by
Chinkin et al. (2003) Diurnal profiles based on Russell & Cass
(1986) E i ~ [2.36 (Ti-273)/10 ] V i A Fertilizers Monthly
variation from fertilizer usage data Diurnal profiles based on
Russell & Cass (1986) Native Soils Temporal variation based on
environmental parameters EF adj = EF * (1-M){1/[1+10
(0.09+2730/T-c*pH) ]} Domestic & Wild Animal Sources Temporally
invariant Slide 30 Emission Factor Adjustments & Impacts Slide
31 MODEL DEVELOPMENT & DATA SOURCES Temporal Allocation
Livestock Diurnal Profiles Slide 32 MODEL RESULTS Gridded NH3
Emissions Total Livestock Annual 2002 Slide 33 MODEL RESULTS
Comparison with CMU Model Slide 34 OTHER REGIONAL NH3 MODELS CMU
NH3 Model -- http://www.envinst.cmu.edu/nh3/ Windows-based emission
model Uses AP-42 type estimation approach Incorporates GIS data for
LULC (USGS data) Spatial Resolution National Level State Level
County Level (ArcINFO/ArcView compatible) Sub-county (200 m
resolution) based on land use GIS data (ArcINFO/ArcView compatible)
Temporal Resolution Annual Monthly (for fertilizer application)
Slide 35 OTHER REGIONAL NH3 MODELS LADCo Process-based NH3 Model
http://www.ladco.org/reports/rpo/MWRPOprojects/Emissions/Technical_Paper1.pdf
Consider and analyze all physical, chemical and biochemical
processes and reactions that take place and influence ammonia
emission rate, Employ processed based mechanistic and empirical
models (new and existing) Keep mass balances for the flow of
nitrogen through each component of an animal waste management
system. Slide 36 LADCo Process-based NH3 Model NH3 Animal
Allocation Processor NH3 Farm Emissions Model: Animal excretion
model Housing emissions model Feedlot emissions model Storage
emissions model, and Land emissions model Animal species
considered: Dairy cows Beef cattle Swine Poultry (layers, broilers,
and turkeys) Commercial Fertilizers Slide 37 LADCo Process-based
NH3 Model Animal Allocation Processor (AAP) Distribute county-level
animal head counts to defined Manure Management Trains (MMTs)
Spatially allocate MMTs to grid cells using gridded surrogates
(agricultural land) Format input data for Farm Emission Model (FEM)
Actual Farm Data Commercial Fertilizers Farm Emission Model (FEM)
FEM computes NH3 emissions with animal numbers by each Manure
Management Train (MMT) for each grid cell: Reads in ASCII outputs
from AAP Reads in meteorology file from CONCEPT met tables Run
Animal Excretion Model Based upon MMTID: oRun Housing Emissions
Model oRun Storage/Feedlot Emissions Model oRun Land Emissions
Model Output total NH3 based on animal type & MMTID Output
commercial fertilizer NH3 emissions estimates Slide 38 LADCo
Process-based NH3 Model AAP Input Data Sources Livestock Data 2002
and 1997 Census of Agriculture Data EPA MMT Distributions Revised
MMT by ISU for Midwest states FEM Defaults from UCD and ISU EPA
Animal Population Category FEM Categories from UCD and ISU
Commercial Fertilizers Carnegie Mellon University (CMU) NH3 Model
County-level fertilizer application rates by month for 2002 FEM
Input/Output Input Data AAP ASCII outputs Meteorological Data
oLat/Lon Coordinates oWind velocity and direction oRelative
Humidity, Rain, oFrictional velocity, etc. Output Data Based upon
animal type & MMTID Format: oCONCEPT ready format oNIF 3.0
format oASCII csv format Slide 39 Example Manure Management Train
(MMT) Animal Housing and Management Practices Slide 40 LADCo
Process-based NH3 Model Data Requirements Animal excretion
sub-module: Nitrogen excretion from animals is influenced by the
age, species, and diet of animals. Required data include animal
populations, age, and feed ratios. Animal housing sub-module:
Emissions depend on the specific housing design and practices.
Collection and storage of manure varies by indoor and outdoor
storage. Required data include specific housing design and
operation, animal populations by age and species and climatic
conditions. Storage sub-module: Emissions associated with manure
storage varies by type of manure Dry manure storage is typical of
beef cattle feedlot, dairy corrals, high-rise layer facility,
broiler and turkey facilities. Wet manure storage is commonly used
in swine and layer facilities. Emissions are based on the type of
storage facilities and manure. Required data include type and
number of storage facilities and environmental data. Land
application module: Emission rates from different land application
vary by type of manure, crop management practices and climatic
conditions. Required data include nutrient content of manure by
animal type, specific application and crop management practices and
environmental conditions. Required data for each sub-module are not
typically available with the type of detail required for a region
encompassing the entire U.S. Default values for each sub-module,
based on information in the Midwest are used. Data can vary
considerably across the US, a set of ranges for these parameters
are provided. Slide 41 LADCo Process-based NH3 Model - Results
Slide 42 Slide 43 SUMMARY & RECOMMENDATIONS
