OVERVIEW OF OWENS LAKE DUST ID AND 2016 SIP MODELINGnas-sites.org/dels/files/2019/08/K.Richmond-Topic-1-air-modeling-17... · 17/07/2019 · emission flux = horizontal sand motion

JUL 17, 2019OWENS LAKE MODELING OVERVIEW


OVERVIEW OF OWENS LAKE DUST ID AND 2016 SIP MODELINGKen Richmond – Ramboll

OLSAP WebinarBishop, CA

Jul 17, 2019


• Owens Lake PM10 Modeling History (1995 to 2016 SIP)

• 2016 SIP Modeling Approach

• 2016 SIP Model Performance

• 2016 SIP Attainment Demonstration

• Questions

PRESENTATION OUTLINE



OWENS LAKE MODELING HISTORY


OWENS LAKEAIRSHED


• Preliminary Owens Lake Air Quality Modeling Study – 1995

• Mono Lake wind tunnel emissions

• Industrial Source Complex (ISC) dispersion model

• 3 separate source sub-regions; each with a meteorological site

• Owens Lake Model Evaluation - 1996

• Owens Lake wind tunnel emissions; episode emissions

• 6 historical episodes

• 1998 SIP Attainment Demonstration Modeling

• ISC dispersion model

• 3 separate area source sub-regions; 35 mi2 uniformly emitting

• 2 years of meteorology (1994-1995)

• Wind speed dependent emissions based on Owens Lake wind tunnel; average emissions



• Dust ID Program Data Collection – Jan 2000 to present

• Evolved out of ARB recommendations to improve 1998 SIP

• Need to better temporarily and spatially resolve source area emissions and activity

• Need to simulate the Airshed as a single domain, not 3 modeling sub-regions

• Sand motion data network, additional meteorological stations, visual observations

• CALPUFF modeling system used to examine source to receptor relationships



PM10 SUSPENSION PROCESS


DUST ID METHOD FOR ESTIMATING VERTICAL PM10EMISSION FLUX

• Journal Articles: Shao, et. al., 1993; Gillette, et. al., 2004; Ono, et. al., 2011

• EPA Other Test Method (OTM) 30 (http://www.epa.gov/ttnemc01/prelim/otm30.pdf)

Fa = Kf x q

Fa = PM10 vertical emission flux [g/cm2/s]

Kf = K-factor

q = sand flux at 15 cm [g/cm2/s]


SAND FLUX MONITOR SITE AT OWENS LAKE 2003



• CALPUFF dispersion modeling system

• January 2000 – June 2002 Dust ID sand motion and meteorological data

• 50 square area sources (1 km2 each)

• Emissions driven by hourly sand motion

• Seasonal and spatial variable “K-factors” for 4 general regions of the lake and the Keeler Dunes: PM10emission flux = horizontal sand motion x K-factor

• Periods with a radar wind profiler at Dirty Socks then Mill Site

• Relatively good model performance using 75th percentile K-factors

• Attainment assessed at the 3600’ shoreline

• Controls areas assume 99% efficiency and Keeler Dunes not included in attainment demonstration, included in model performance assessment and K-factor analysis

• Background of 20 µg/m3



2003 SIP DUST ID NETWORK AND SOURCE CONFIGURATION



• CALPUFF dispersion modeling system

• July 2002 – June 2006 Dust ID data

• Additional surface meteorological and sand motion data

• Upper air radar wind profiler discontinued in June 2004

• Time lapse video observations & GPS source area mapping

• Seasonal and Spatially Variable 75th percentile K-factors

• 4 general areas (North Sand Sheet, South Sand Sheet, Central Area, Keeler Dunes)

• Defaults from 2003 SIP

• Source Configurations

• Irregular source areas assigned to each sand motion site

• Irregular areas, most represented by 250m x 250m squares

• 4 general configurations used for July 2002 to June 2006



• 2008 SIP Attainment Demonstration Modeling (Continued)

• Background 20 µg/m3: lowest network PM10 for days with PM10 > 150 µg/m3 excluding winds from the sources included in the modeling

• Keeler Dunes excluded in the attainment demonstration, but included in development of K-factors

• No model performance assessment included in the SIP

• Attainment assessed at the 3600’ shoreline

• 2003 SIP Dust Control Areas excluded

• Controlled areas assume control efficiencies of 0 – 99% with Moat & Row design assumed to provide less control than full BACM 99%; DWP provided the control efficiencies for each Moat & Row Area



2008 SIP DUST ID MONITORING NETWORK

406 408 410 412 414 416 418 420 422 424East - West UTM (km)

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1891

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7144 7145

7167 7168 7172 7173 7174 7175

7190 7191 7192 7193 7195 7196 7197 7198 7199

7214 7215 7216 7217 7218 7219 7220 7221 7222 7223

7240 7241 7242 7243 7244 7245 7246 7247

7266 7267 7268 7269 7270

7290 7291 7292 7293 7294

7314 7315 7316 7317

7337 7338 7339 7340

7360 7361 7362 7363 7364

7384 7385 7386 7387

7406 7407 7408 7409 7410

7429 7430 7431 7432

7451 7452 7453 7454 7455

7473 7474 7475 7476 7477

7495 7496 7497 7498 7499

7518 7519 7520 7521 7522

7540 7541 7542 7543 7544 7545

7561 7562 7563 7564 7565 7566 7567

7584 7585 7586 7587 7588 7589

7604 7605 7606 7607 7608 7609 7610 7611

7627 7628 7629 7630 7631 7632 7633

7650 7651 7652 7653 7654 7655

7673 7674 7675 7676 76777678

7696 7697

8384

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8563 8566

8587 8588

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7/2002-6/2003 7/2005-6/2008


• Supplementary Control Requirement Determinations

• Using data from the Dust ID Program and the procedures outlined in the 2008 SIP to assess whether additional controls are necessary

• Source area delineations, sand motion data, & meteorological data from Dust ID Program (+DWP sites)

• CALPUFF simulations of period to infer source-to-receptor relationships. Note 5-minute sand motion and meteorological data from March 2009

• Model performance assessment

• Weight-of-evidence approach; modeling provides a candidate list of supplemental control areas

• 2011 SCRD: Period July 2006 to June 2010




• Pre 2016 SIP: Period June 2013 to July 2014



• Performed ~yearly since the 2016 SIP

• On-Lake vs Off-Lake Analysis for Days > 150 µg/m3

• Source Impact Matrices

• For each source configuration, SIMs list the source contributions for the top 10 simulated concentrations at each shoreline receptor and monitoring site

• Variable controls and K-factors can be assigned to each source area to test control strategy

• Lone/Watch Lists

• Provide candidate lists for SCRD

• 2016 SIP “ambient air” receptors: along shoreline (~100m), District receptors of interest, 500m grid off-lake, and at off-lake TEOMS

• Lone: Individual source area’s 24-hr contribution to a compliance receptor is > 130 µg/m3

• Watch: Individual source area’s 24-hr contribution to a compliance receptor is > 80 µg/m3

• For each source area contribution show sand motion on that day and distance to receptor with the peak concentration

CONTROL STRATEGY DEVELOPMENT TOOLS



2016 SIP MODELING APPROACH


• Simulation of on-lake sources followed 2003 SIP, 2008 SIP, and SCRD modeling procedures except:

• June 2009 to July 2014 period with 5-minute meteorological and sand motion data

• CALPUFF/CALMET version 6.4 needed for 5-minute data

• Revised default K-factors assigned to 7 general source area regions on the lakebed and Keeler Dunes

• Tried simulation off-lake areas within 2-km of the shoreline by assigning sand motion vs wind speed relationships (Gillette Model) based on similar Sensit sites, but model performance was deemed unsatisfactory

• Hybrid model used for attainment demonstration

• Monitor centric with focus on days exceeding 150 µg/m3

• Each event divided into “from-the-network” and “out-of-network” WDs. From the network contribution based on CALPUFF simulations. Out-of-network based on monitoring

• Attainment: Scaled CALPUFF with future controls + rollback of monitoring data



• Domain: 34km-by-48km with 1km horizontal mesh; 10 geometrically spaced vertical levels. Same as in the 2003, 2008 SIPs and SCRDs

• CALMET (Meteorological model) procedures

• Version CALMET 6.4 and 5-minute data

• Land Use from USGS 1:250,000 data sets; modified default surface roughness for smoother Owens Lake surfaces, but does not reflect current actual land use on the lake bed

• Surface winds, temperature, RH and pressure from Dust ID network (fixed and portables) + DWP sites

• Cloud cover from Bishop and China Lake

• Upper air temperature profiles from Las Vegas, Reno, China Lake, and Desert Rock depending on the year

• Upper winds based on fit to Radar Wind Profiler observations (Jan 2001 – Jun 2004)



400 405 410 415 420 425 430East - West UTM (km)

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Sensit/Cox Sand CatcherSource AreaTEOMMet. Tower

lone

stan

olan

pduc

t1a1

t7

t12cott

scut

btowflat

t27

keel

lizt

atow

norbpbou

bart

400 405 410 415 420 425 430East - West UTM (km)

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Sensit/Cox Sand CatcherSource AreaTEOMMet. Tower

lone

stan

olan

dirt

t4

t8

pt11

cott

scut

btow

flat

t23

t25mill

keel

lizt

atow

pnel1

pw3

norb

pwl1

delt

Dust ID Network For July 2009 Dust ID Network for July 2013


MODEL DOMAIN, TERRAIN (M), AND SAMPLE WIND FIELDSurface Winds 11/16/2015 Hour 1155-1200

400 405 410 415 420 425 430East - West UTM (km)

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Wind Speed 20 m/s


• CALPUFF Procedures

• Version 6.42 for 5-minute data and Version 5.85 for hourly data (QA purposes)

• Pasquill – Gifford dispersion curves

• Partial path vertical plume terrain adjustment

• Puff (not slugs) with splitting turned on

• Mass depletion through dry deposition using particle size distributions from June 1995 to March 1996 Owens Lake measurements

• Source Characterization

• 3q09-2q14 divided into 13 periods based on precipitation, storms, & changes to Sensit network

• Irregular source area shapes delineated using GPS mapping, visual plume observations, sand motion data, physical on-lake boundaries, and other methods from the Dust ID program. Also assigned based on current/future controls and ownership

• Each source area assigned a sand motion monitoring site

• Each source area is divided into small squares. Currently the widths range from 50m to 100m and the simulations include 7,000-10,000 small sources for 330-650 source areas



2013-2014 NETWORK AND SOURCE CONFIGURATION



• Modeling Procedures

• Simulations performed in parallel on Ramboll Environ Linux cluster for each irregular source area to speed up the analysis and to be able to track source contributions

• Initial emission estimates based on Kf = 5 x 10-5 (inferred from 1st simulation in January 2000)

• Refined K-factors by general source area and periods of the year are inferred from an analysis of model residuals

• Paired model predictions and TEOM observations are screened to ensure strong source to receptor connections

• Additional screening attempts to remove the influence of sources not included in the simulations by examining PM10 concentrations at upwind sites. Also case by case screening

• In the absence of at least 9 samples, default K-factors are assigned

• The initial model predictions are scaled by the refined K-factors to assess source contributions at ambient air receptors and model performance at the monitoring sites


2003 SIP VS 2016 SIP GENERAL SOURCE REGIONS


SEASONAL 75TH PERCENTILE K-FACTORS BY GENERAL SOURCE AREA

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10

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7/1/2009 7/1/2010 7/1/2011 6/30/2012 7/1/2013

K-fa

ctor

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0-5)

NW NE Keeler Kedune Central ManVeg South



2016 SIP MODEL PERFORMANCEJULY 2009 TO JUNE 2014


PRE 2016 SIP MODEL PERFORMANCE

• Model Performance Characteristics from Previous Analyses

• Pre 1998 SIP, 1998 SIP, 2003 SIP, and each SCRD, CALPUFF + constant background

• Unpaired in time performance (Q-Q plots) is usually good

• The model often does not explain the temporal variability of the observations

• Better performance for the large source areas and big events. Note the large events typically involve the more marginally erosive source areas

• Better performance at receptors close to the edge of the source

• Sporadic performance for TEOMs inside the source area and increased scatter for receptors 5-30km from the source edge

• Slightly better performance for the 5-minute vs the hourly simulations

• Slightly better performance for the revised general source regions and default K-factors

• The delineation of the source areas and sand motion data are more important than the K-factors and dispersion model options


2016 SIP MODEL PERFORMANCE• Model Performance Analyses

• Provides decision makers context for the model predictions

• 2016 SIP and Recent Periods based on Hybrid Model: CALPUFF “from the network” + “out-of-network” observations

• 24-hour Predictions and Observation at off-lake sites

• Non Symmetric Filter: PM10 Obs > 150 µg/m3

• CALPUFF “from-the-network” predictions based on WD at TEOM

• Observed “out-of-network” daily variable background (must be > 20 µg/m3)

• Statistical Measures

• Q-Q Plots

• Log-Log scatter diagrams; geometric correlation coefficients

• General descriptive stats (model vs observations): e.g. means, STD, 98%, n > 150 µg/m3, number within a factor-of-two


Exceedance Day PM10 Concentrations for July 2009 to June 2014

Site Name ID 1 Years N > 150 µg/m3

Maximum PM10 (µg/m3)

Design PM10(µg/m3) 2

Dirty Socks dirt 3 26 1,437 998

Flat Rock flat 2 9 871 233

Keeler keel 5 37 2,994 524

Lizard Tail lizt 5 42 4,571 1,654

Lone Pine lone 4 1 169 #N/A

Mill Site mill 1 7 754 712

North Beach norb 3 17 1,536 385

Olancha olan 5 22 779 310

Shell Cut scut 5 23 2,149 395

Stanley stan 5 8 286 180

Notes:1 TEOM locations are shown in previous slides.2 Design day based on n+1 highest in n years. For example the 6th highest in 5 years or the 2nd highest in 1 year.


Out-of-Network Source Contribution Summary on Exceedance Days for July 2009 to June 2014

Site Name ID 1 Median PM10(µg/m3)

Maximum PM10(µg/m3) N > 150 µg/m3

Dirty Socks dirt 4 244 4Flat Rock flat 41 652 2

Keeler keel 15 2,979 2 4Lizard Tail lizt 18 3,444 2 14Lone Pine lone 165 165 1Mill Site mill 9 350 2

North Beach norb 21 570 5Olancha olan 8 293 1Shell Cut scut 223 2,125 3 16Stanley stan 133 277 4All Sites 18 53

Notes:1 TEOM locations are shown in previous slides2 Occurred during December 1, 2011 Dust Event3 Occurred on May 25, 2012


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In-Network PM10 Portion: CALPUFF Predicted vs. Observed Scatter DiagramAll Monitored Exceedance Days, July 2009 to June 2014


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In-Network PM10 Portion: CALPUFF Predicted vs. Observed QQ-PlotAll Monitored Exceedance Days, July 2009 to June 2014


Statistics for In-Network Daily PM10 Observations vs CALPUFF Model Predictions on Exceedance Days During July 2009 to June 2014

Statistic 1 Observed Predicted

Mean (µg/m3) 397 488Geometric Mean (µg/m3) 2 259 219

Median (µg/m3) 235 25698th Percentile (µg/m3) 1,784 2,641

Maximum (µg/m3) 4,573 5,472N > 150 µg/m3 137 105

Paired Statistic 1

Linear Correlation Coef. 0.831Geom. Correlation Coef. 2 0.5613

Factor-of-2 2 69%

Notes:1 Based on 194 samples where total daily observations are greater than 150 µg/m3.2 Based on 154 samples where in-network observations and CALPUFF predictions were greater than 5 µg/m3 on days

where the total daily observations are greater than 150 µg/m3.


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Hybrid Model Predicted vs. Observed Scatter DiagramAll Monitored Exceedance Days, July 2009 to June 2014


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Hybrid Model Predicted vs. Observed QQ-PlotAll Monitored Exceedance Days, July 2009 to June 2014


Statistics for Daily PM10 Observations vs Hybrid Model Predictions on Exceedance Days During July 2009 to June 2014

Statistic 1 Observed Predicted

Mean (µg/m3) 455 529Geometric Mean (µg/m3) 321 292

Median (µg/m3) 250 27998th Percentile (µg/m3) 2,586 3,075

Maximum (µg/m3) 4,571 2 5,492N > 150 µg/m3 194 154

Paired Statistic 1

Linear Correlation Coef. 0.857Geom. Correlation Coef. 0.668

Factor-of-2 73%

Notes:1 Based on 194 samples where observations are greater than 150 µg/m3.2 The maximum observation of the combined daily total PM10 is slightly less than the in-network component in the

previous table because the out-of-network portion was slightly negative. A negative contribution can occur for a few hours following a large event as moisture evaporates from the mass sampled by the TEOM.



2016 SIP ATTAINMENT DEMONSTRATION


• Hybrid Model applied to exceedance days from July 2009 to June 2014 where dispersion model predictions are combined with out-of-network background

• For each future year of controls

• Assess PM10 concentrations at the shoreline monitoring sites on days exceeding 150 µg/m3

• Scale the model predictions by future year control efficiencies for each of 13 source configurations

• ECR and Contingency Areas are not included in the dispersion model predictions

• Combine the scaled model predictions with background

• Calculate a new design concentration based on the number of years of monitoring data at each site from July 2009 to June 2014

2016 ATTAINMENT DEMONSTRATION PROCESS


• The attainment demonstration assumes as controls are implemented on the lakebed and Keeler Dunes, emissions from secondary source areas would also be reduced over time. The reduction in contribution from such areas was specified via:

𝐶 𝐶 𝐶 𝑒 𝐶

• Where CT is PM10 contribution from out-of-network sources in future year T (µg/m3)

• Cout= PM10 contribution from out-of-network sources during the baseline period from July 2009 to June 2014 (µg/m3); must be great than 20 µg/m3

• Cb = background PM10 concentration of 20 µg/m3

• ΔT= number of years from the implementation of controls on the lakebed and/or Keeler Dune from nearby sources during the baseline period

• Ts = time scale for decay assumed to be about 3 years based on the District’s analysis of Dirty Socks PM10 reductions during southerly winds

2016 ATTAINMENT DEMONSTRATION PROCESS


Control Efficiencies for Future Years

Control Area 1 End of 2015 End of 2017 End of 2019

Phases 1-8 Yes (varies by BACM) Yes (varies by BACM) Yes (varies by BACM)

Phases 9 & 10 0% Yes (varies by BACM) Yes (varies by BACM)

Lakebed ECRs 2 0% 100% 100%

Keeler Dunes ECR 2 0% 100% 100%

Keeler Dunes DCA 95% 95% 95%

Contingency Areas 0% 100% 100%

Notes:1 The Control Areas for each source confugration period are shown in Appendix B of the SIP modeling

report.2 The ECRs are not shown on any of the maps to protect these sensitive areas.



PM10 Design Concentration Predictions

Obs. Hybrid Model Design Concentration Predictions (µg/m3) by Year

SiteID 1

2009-2014

2009-2014 20142 20152 20162 20172 20182 20192

dirt 998 1,235 1,235 213 213 93 87 83

flat 233 228 228 133 133 94 74 59

keel 524 592 592 201 196 67 55 46

lizt 1,654 1,993 1,993 1,684 1,684 142 109 85

mill 712 642 642 526 508 125 95 74

norb 385 448 445 114 87 67 54 44

olan 310 294 294 84 68 41 41 39

scut 395 586 506 212 157 105 83 70

stan 180 115 96 59 49 39 35 31

Notes:1. TEOM locations are shown in previous slides.2. Controls in place by the end of the year indicated.


OWENS VALLEY MODEL FORECAST, FUTURE YEAR PM10DESIGN CONCENTRATIONS

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2009-2014 2014 2015 2016 2017 2018 2019

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End of Year

dirt flat keel lizt mill norb olan scut stan NAAQS


We did not attain the NAAQS by the end of 2017 as predicted, why?• EPA required we show attainment by 2017 or they would not accept/approve the SIP. The

Hybrid Model approach was designed to satisfy EPA minimum requirements.

• Preliminary SIP modeling that included off-lake sources within 2km of the shoreline did not show compliance well into the 2020’s even when we considered a decay to natural desert erosion rate over a period of years. EPA found this conclusion unacceptable.

• The assumption off-lake secondary sources would have a ½ life of 3 years is too short.

• The assumption all nearby off-lake sources affected the TEOMs are caused by deposition from previous on-lake source episodes is probably not correct.

• The on-lake controls were applied to early, e.g. they were not finished in the years we assumed. For example the Keeler Dunes were supposed to be 95% controlled by the end of 2015.

• Not all ordered areas have had 99% control implemented (1.2 square miles avoided).

2016 ATTAINMENT DEMONSTRATION PROBLEMS



QUESTIONS?

Documents

OVERVIEW OF OWENS LAKE DUST ID AND 2016 SIP MODELINGnas-sites.org/dels/files/2019/08/K.Richmond-Topic-1-air-modeling-17... · 17/07/2019 · emission flux = horizontal sand motion