US Army Corps of Engineers Engineer Research & Development Center ISCMEM Groups 2 and 4 Joint Webinar Presentations Training Range Environmental Evaluation

US Army Corpsof Engineers Engineer Research & Development Center

ISCMEM Groups 2 and 4 Joint Webinar Presentations

Training Range Environmental Evaluation and Characterization System (TREECS™)

Applications

June 19, 2012


Outline• Overview of TREECS – Billy Johnson

• TREECS Application to Borschi Watershed Overview of the Chernobyl Accident and Borschi

Watershed – Boris Faybishenko Modeling of 90Sr Transport for the Borschi Watershed

– Mark Dortch

• TREECS Application to Artillery Impact Area (AIA) for RDX – Mark Dortch


Overview - Training Range Environmental Evaluation and Characterization System

Billy E. Johnson, ERDC


TREECS Problem Statement

• Residues and disturbances from range operations can impact the environment, including human and ecological health off-range. Such impacts can impact environmental compliance and range sustainment.

• Army live fire training and test ranges have unique environments in which low-order and unexploded ordnance (dud munitions) are likely to cause random and highly uncertain sources of munitions constituents (MC) contamination.

• An assessment tool is needed to forecast if, when, and at what level MC concentrations in off-range media (groundwater, surface water, and sediment) may exceed protective health benchmarks.


TREECS Solution / ApproachTraining Range Environmental Evaluation and Characterization System (TREECS) is a client-based system that provides forecasts of Munitions Constituents (MC) fate on and off range based on munitions use on range.

Development Approach:

Formulate and couple models of reduced form for MC fate/transport-transformation-sequestration in an integrated framework for fast assessments with a minimal amount of user input.

Partners:

PNNL, EPA, AEC, AIPH, ITL, and EL

http://el.erdc.usace.army.mil/treecs/


TREECS Components• Framework for Tier 1 and 2 assessments• Constituent databases• Health Benchmark database• Munitions database• MC residual mass loading module based on munitions use• GIS module• Hydro-geo-characteristics toolkit (HGCT) for estimating input

parameters• Models for soil, surface water, vadose zone, and groundwater• Simplified user input interfaces for models (GUIs)• Viewers for results• Sensitivity and uncertainty module for Tier 2 assessments


TREECS GIS Components

- For opening individual GIS files

- For saving individual GIS files

- For resampling a grid

- For zooming into an area in the workspace

- For zooming out of an area in the workspace

- For panning in the workspace

- For creating a rectangular AOI shapefile in the workspace

- For creating a polygon AOI shapefile in the workspace

- For measuring length and area in the workspace

- For converting a shapefile to a grid

- For extracting a subset of a grid

- For creating slope grid from DEM and performing simple arithmetic operations on a grid


TREECS HGCT

• To aid the user in determining input variables required by TREECS models– Soil Properties– Soil erosion rate– Hydrology (infiltration, runoff, ET, etc.)– Darcy velocity

• Allows point (single value) and spatially- varying composite estimates

• Spatial option requires use of GIS module in TREECS or externally developed map files (grids)


Tiered Approach• Tier 1 (screening)

– Steady-state, no degradation, worse case, highly conservative

– Requires little data– Can be applied very quickly– Indicates whether a problem could ever potentially exist; if

so, proceed to Tier 2

• Tier 2 (more comprehensive)– Time-varying, much more realistic and accurate– Requires more data– Requires more time to set up and apply, but still can be

done relatively quickly– Can be used to determine when benchmark exceedence

may occur– Useful for evaluating range management strategies


Tier 1 Conceptual Model


Tier 2 Conceptual Model


Constituent Selection

View MC Properties

Can use a user defined database (Create under Tools)

Select Database

Select MC


MC Residue Mass Loading Module (Operational Inputs)

Provided by user

Pulled from MIDAS Extract DB

Constant in Tier 1

Can use a User Defined munitions database


MC Residue Mass Loading

, , , , , , ,

, , ,1

100 100 100

100

j nj k LOj k j k HOj k j k j k SYMj k

i k j k i jj

LO Y HO Y DUD SYM YL N M

Li,k = Loading for constituent I for year k, g/yrDUDj,k = percent of duds for munitions item j for year kHOj,k = percent of high order detonations for munitions item j for year kLOj,k = percent of low order detonations for munitions item j for year kMi,j = mass of constituent i in munitions item j delivered to impact area, g/itemNj,k = number of munitions item j fired for year kn = total number of munitions items used at AOISYMj,k = percent of sympathetic detonation of duds for munitions item j for year kYHOj,k = percent yield of munitions item j due to high order detonation for year kYLOj,k = percent yield of munitions item j due to low order detonation for year kYSYMj,k = percent yield of munitions item j due to sympathetic detonation for year k


MEPAS Vadose & Aquifer Models(Tier 2)


Surface Water Models, RECOVERY and CMS


Advanced Tier 2

• Multiple AOIs

• Complex Pathways

• Multiple Receptors

Future capabilities will include remediation and BMP modules


Benefits of using TREECS

• Allows the user to project future conditions- Answers the question of whether there will be

a problem in the future

• Can be used to develop and assess mitigation scenarios

• Can be used to help optimize and prioritize data collection sites for future assessment activities

• Can be used in designing new training ranges to help minimize migration of MC

Europe Radioactive gases, aerosols and finely fragmented fuel were releases to the atmosphere:

• Fuel particles—finely dispersed, low volatility, settled primarily within the ChEZ

• Condensed components—from radioactive gases, settled primarily along the atmospheric flow pathways

• Hot particles—fuel particles, uranium dioxide, with a specific activity >105 Bq/g, size 1 to 100 µm, surface density ~ 1,600 per m2, to ~0.5 m depth

Russia

Ukraine

Belorus

Overview of Chernobyl AccidentBoris Faybishenko, LBNL

Example of the autoradiography film of fuel particles in wetland sediment (Freed et al., 2003)

90Sr in the Dnieper River reservoirs is still above of pre-accident levels. 137Cs in the water at the lowest reservoir returned to its pre-accidental.

There is a wealth of data within the Chernobyl Exclusion Zone and the cascade of 6 water reservoirs along the Dnieper River, which can provide important information for the testing and validation of models, parameter estimation, uncertainty quantification, and understanding conceptual models of flow and transport processes in surface water, vadose zone, and groundwater.

.

Borschi Watershed Monitoring

Freed et al., Seasonal Changes of the 90Sr Flux in the Borschi Stream,

Chernobyl, ERSP, 2003

Cooling Pond

Pripyat River

Scale

5 km

Borschi Watershed

N

ChNPP

• 3 km south of ChNNP• Area 8.5 km2

• Flows into the Chernobyl Cooling Pond drainage ditch and then into the Pripyat River

• Sandy soil underlain by clay marl• Primarily formerly cultivated land and

planted forests with two seasonal wetlands

Microbial communities

Dissolved radionuclides

Radionuclides in suspended sediments

Radionuclides in bottom sediments

Advection

Diffusion/Dispersion

Adsorption Desorption

Adsorption

Desorption

Sedimentation

Resuspension

Uptake

Post-Chernobyl Radionuclide Transport Processes in Surface Water Reservoirs

Suspended sediments

Modified after M.Zheleznyak

Hot particles


Application of TREECS™ to Borschi Watershed for 90Sr and AIA for RDX

Mark Dortch, PhD, PEContractor to the Environmental Lab, ERDC


Model Inputs in General

• MCs of interest and their properties• Source zone inputs (e.g., range munitions

use and associated parameters, or constituent loading rate)

• Average annual hydrology and soil erosion (from HGCT)

• Site specific media inputs (soil, vadose, groundwater, surface water, sediment)


Tier 2 Application to Borschi Watershed near Chernobyl for 90Sr

Cooling Pond

Pripyat River

Scale

5 km

Borschi Watershed

N

ChNPP


Borschi Watershed

• 3 km south of CNNP• 8.5 km2

• Sandy soil underlain by clay marl• Primarily formerly cultivated land and

planted forests with two seasonal wetlands

• Flows into a cooling pond drainage ditch and then into the Pripyat River


Strontium-90• Half life = 29 years• Specific radioactivity = 143 Ci/g• Approximate watershed inventory for year

2000 = 1.0 E13 Bq• Watershed stream exit activity conc. (Bq/L)

was monitored to estimate watershed export of 1.27 E10 to 1.62 E10 Bq/yr for 1999 – 2001, with average = 1.43 E10 Bq/yr, or annual export of 0.14% of 2000 inventory


Watershed Hydrology (Freed 2002)

• Average annual precipitation = 0.6 m/yr• Rainfall is approximately 0.47 m/yr based on

snow melt = 22 % of stream flow• Stream flow = 15 – 20 % of precipitation• Stream flow for 1999 – 2000 = 0.097 m/yr, or 16

% of average precip• 80 % of stream flow is base flow from

subsurface, or 0.078 m/yr; results in runoff = 0.019 m/yr

• Groundwater recharge had to be estimated from water balance


Conceptual Site Model

Surface Soil

Vadose Zone

Aquifer

AOI = Borschi Watershed

Watershed Outlet

Runoff (Q) & Erosion

Base flow = interflow

Recharge

Infiltration = Recharge + Interflow

Sr(90)

Model requires infiltration and % of infilt. going to interflow

Infiltration = P – ET – Q


Required TREECS™ Models• Only the Tier 2 soil model was needed

(includes interflow pathway)• Vadose and aquifer models were not used

since recharge was assumed to be lost• No surface water model was used since

watershed outlet is considered to be part of AOI or location of soil model export flux

• No AOI loading, only initial inventory in 2000


Soil Model Inputs• Area = 8.5 E6 m2

• Active soil layer = 0.2 m• Soil – water temperature = 7.7 deg C• Mobile Sr(90) Inventory conc. = 6.3 E-7 mg/kg

(assumed 20% non-exchangeable adsorbed); did not matter whether in solid or non-solid form

• Assumed loamy sand: 12 % water content, 1.49 g/cc bulk density, 44 % porosity

• Erosion rate = 2.5 E-6 m/yr based on USLE (insignificant amount)


Soil Model Inputs, cont.• Precip = 0.6 m/yr; rainfall = 0.47 m/yr• Runoff = 0.019 m/yr• Infiltration = 0.097 m/yr with 80% of infiltration

diverted to interflow (uncertain, so varied, i.e., doubled and halved, for sensitivity)

• Kd = 76 L/kg (initial value based on one sediment measurement; treated as uncertain)

• t1/2 = 29 yr

• Mw = 90 g/mol


Baseline Results

Year 2000

Compared to 1.43 E10


Evaluation of Uncertainties• Kd: conducted Monte Carlo simulation

assuming normal distribution and varying between 100 and 300 L/kg with mean of 200 and Std of 33

• % non-exchangeable adsorbed Sr: did not vary and kept fixed at 20%; recognize that varying this has linear inverse affect on export

• Infiltration & % interflow: doubling/halving improved estimate of ET but did not affect export in 2000; does slightly decrease export vs time


Uncertainty Analysis for Varying Kd

Compared to 0.14 %

observed


Conclusions

• Results are insensitive to distribution of inventory between solid- and non-solid-phases due to relatively fast dissolution rate of Sr particles.

• Initial export (year 2000) is insensitive to the amount of infiltration and recharge as long as the same amount of interflow is used (0.078 m/yr)

• Export of 90Sr from the Borschi watershed to surface water is predominantly a result of soil pore water containing dissolved Sr being diverted to surface waters that eventually flow out of the watershed


Conclusions, continued• The percentage of non-exchangeable adsorbed Sr

and the soil-water Kd are the two most sensitive and uncertain factors affecting the amount of export

• This application demonstrates how TREECS™ can be applied for a radionuclide. Such an application is accomplished by:– Converting radioactivity to mass units for model input

using the specific radioactivity of the radionuclide– Modeling the constituent mass as normally done– Converting the output mass concentration/flux values to

radioactivity using the specific radioactivity of the radionuclide


Conclusions, continued

• Application of TREECS could be useful for the larger Pripyat and Dnieper River watersheds during the remediation evaluation process and for the feasibility study of the remediation and decommissioning of the Chernobyl cooling pond


Tier 2 Application to an Artillery Impact Area (AIA) for RDX

Beginning of stream model

Modeled creek extended 4.4 km to a usage point

Lake

Terrain drains into small stream that flows into a creek; glacial till over bedrock

3 ranges fire 105, 60, and 81 mm artil./mortars into AOI


Conceptual Site Model

Assumption: Runoff from AOI moves quickly and un-attenuated to creek

AOI SoilCreek

All net infiltration is diverted to soil interflow that exits to surface water

X

Target receptor

Erosion and Runoff

4.4 km


Sources of Model Input Data• Google Earth™ for watershed and AOI delineation

and areas• NRCS Web Soil Survey (WSS) for AOI soil

characteristics• Daily local air temperature and precip data for

period of record from a met station near the site (used by HGCT)

• Training ammunition usage reports for 2 years for estimating munitions constituents (MC) loadings

• Application of HGCT within TREECS


Estimating MC Loading Rate

DODICRounds

fired/yrDuds, % Low order,

%Low order

yield, %

High order

yield, %

C445 (105 mm) 7855 2.8 1 50

99.999993

C868 (81 mm) 3164 2.9 0.06 50

99.999993

B642 (60 mm) 2314 2.9 0.06 50

99.999993

Inputs

Computed loading rate of RDX = 50 kg/yr (mostly from 105 mm low orders). Assumed a

constant loading for 70 years


Key Inputs for Soil Model• Soil characteristics (sandy loam, glacial till on

top of bedrock)– Bulk density = 1.48– Avg. vol. moisture content = 17%– Porosity = 44%

• Average annual hydrology/erosion from HGCT– Precip/rainfall =1.1 and 1.0 m/yr– Infiltration = 0.25 m/yr– Runoff = .52 m/yr– 141 rainfall events/yr– ULSE estimated erosion = 0.0025 m/yr


Key Inputs for Soil Model (cont.)

• RDX F/T parameters– Soil Kd = 0.42 L/kg (from GUI utility)

– Particle average initial diameter = 12 mm – No degradation

• RDX chemical-specific properties– Solubility at 10.9 deg C = 29.3 mg/L– Henry’s law const. = 6.23E-8 atm m3/mol– Molecular Wt. = 222.12– Solid phase density = 1.8 g/cm3


Key Inputs for Stream Model (used CMS)

• Model parameters– 40 computational segments for 4.4 km– Flow Dispersion = 1 m2/sec– TSS = 3 mg/L– Sediment foc = 0.02– Active sediment layer porosity = 0.7

• Hydraulics– Estimated width = 10 m and depth = 0.3 m– Average flow = 0.27 m3/sec based on runoff depth

and watershed area; resulting travel time of ½ day


Key Inputs for Stream Model

• RDX chemical-specific properties– Same as for soil for MW, He– Molecular diffusivity = 5.9E-6 cm2/sec– Kow = 7.41 ml/ml (used to estimate Kd)

• Sedimentation– Assumed equilibrium or zero burial; assumed

settling rate = 2 m/day resulting in computed resuspension = 2.5E-5 m/day


Ran both soil and stream model for 200 years with RDX loading extending over first 70 years,

from approx. 1940 to 2010


Computed RDX Water Total Concentration at Creek Usage Point


Comparison with Data Measured in Creek in 2003

• Sediment: measured < RL (RL = 0.05 mg/kg) compared to model value of 0.007 mg/kg or also < RL

• Water: measured = 1.1 ppb compared to model value of 2.5 ppb

Encouraging considering: assumed constant firing rate and assumed low order rate for 70 yrs, no loss between AOI-creek, no loss to GW, no degradation loss, constant and estimated creek flow rate


Other Validation Applications

Installation No.

AOI MC Target Receiving Water

1 Demo area RDX Groundwater

2 HE impact area SAFRs

RDX, TNT, KClO4

Pb, CuGroundwater, lake

3 HE impact area SAFRs

RDXPb

Pond

4 SAFRs Pb, Cu, Zn, Sb Pond

All model-computed results were within a factor of 10 of those observed. Agreement for HE was much better than for metals.


Conclusions/Recommendations• With these models of reduced form, applications

require a couple of days to discover info & set-up and less than one minute to simulate centuries

• Validation application results indicate TREECS™ can provide insightful predictions regarding MC fate down-gradient of ranges with predictions within an order of magnitude (or closer) of observed data

• The Monte Carlo uncertainty analysis feature in TREECS™ can be used to provide confidence bounds in predictions due to imprecise input estimates

Documents

US Army Corps of Engineers Engineer Research & Development Center ISCMEM Groups 2 and 4 Joint Webinar Presentations Training Range Environmental Evaluation