Office of Research and Development

National Risk Management Research Laboratory, Ada, Oklahoma 74820

AN ASSESSMENT OF NUTRIENT 

MANAGEMENT PLANS

Transport and Fate of Nutrients and 

Indicator Microorganisms at a 

Dairy Lagoon Water Application Site:

EPA 600/R-10/116 | March 2011 | www.epa.gov/ada

Office of Research and Development

National Risk Management Research Laboratory, Ada, Oklahoma 74820

AN ASSESSMENT OF NUTRIENT 

MANAGEMENT PLANS

Transport and Fate of Nutrients and 

Indicator Microorganisms at a 

Dairy Lagoon Water Application Site:

Scott Bradford

USDA, ARS, US Salinity Laboratory
ii

Notice

This work was supported through Interagency Agreement DW-12-92189901-0 between 

EPA’s Ground Water & Ecosystems Restoration Division, National Risk Management 

Research Laboratory (Stephen Hutchins, Project Officer) and USDA-ARS’s U.S. 

Salinity Laboratory (Scott Bradford, Principal Investigator). Although this work was 

funded substantially by the U.S. Environmental Protection Agency, it has not been 

subjected to Agency review and therefore does not necessarily reflect the views of 

the Agency, and no official endorsement should be inferred.

Corresponding author. Scott Bradford, USDA, ARS, US Salinity Laboratory, 

450 W. Big Springs Road, Riverside, CA 92507; Tel.:951-369-4857; Fax: 951-342-4964;

E-mail: Scott.Bradford@ars.usda.gov
iii

The U.S. Environmental Protection Agency (EPA) is charged by Congress with protecting the Nation’s land, air, and water 

resources. Under a mandate of national environmental laws, the Agency strives to formulate and implement actions leading 

to a compatible balance between human activities and the ability of natural systems to support and nurture life. To meet 

this mandate, EPA’s research program is providing data and technical support for solving environmental problems today 

and building a science knowledge base necessary to manage our ecological resources wisely, understand how pollutants 

affect our health, and prevent or reduce environmental risks in the future.

The National Risk Management Research Laboratory (NRMRL) is the Agency’s center for investigation of technological and management approaches for preventing and reducing risks from pollution that threatens human health and the 

environment. The focus of the Laboratory’s research program is on methods and their cost-effectiveness for prevention 

and control of pollution to air, land, water, and subsurface resources; protection of water quality in public water systems; 

remediation of contaminated sites, sediments and ground water; prevention and control of indoor air pollution; and restoration of ecosystems. NRMRL collaborates with both public and private sector partners to foster technologies that reduce 

the cost of compliance and to anticipate emerging problems. NRMRL’s research provides solutions to environmental 

problems by: developing and promoting technologies that protect and improve the environment; advancing scientific and 

engineering information to support regulatory and policy decisions; and providing the technical support and information 

transfer to ensure implementation of environmental regulations and strategies at the national, state, and community levels.

EPA currently requires that application of concentrated animal feeding operation wastes to agricultural fields follows 

a Nutrient Management Plan (NMP). The tacit assumption is that a well-designed and executed NMP ensures that all 

lagoon water contaminants (nutrients and pathogens) are retained or taken up in the root zone so that ground water is 

inherently protected. This research was designed to test the assumption that appropriate NMPs are protective of ground 

water and to address potential weaknesses in the land-application design and operation processes. A well-designed and 

managed NMP was implemented on two 6-by-6-meter plots at a dairy farm in San Jacinto, California, for several years 

using different forage and application patterns. The selected site was intensively characterized and instrumented for the 

experimental studies. Lagoon water application rates were determined by following NMP guidelines from the National 

Resource Conservation Service (NRCS).

Spatial and temporal variations in water, nutrient, and indicator microbe levels at the site were determined using a system 

of nested tensiometers and soil solution samplers, neutron probe readings, weighing lysimeters, periodic soil coring, plant 

tissue analysis, and measurements of apparent soil electrical conductivity. Along with the field experiments, laboratory 

experiments were also conducted, using microbes, lagoon water, well water, and soils from the NMP field site. Microcosm 

and batch studies were conducted to quantify microbial retention and survival. Transport experiments were conducted 

to quantify the influence of water content, microorganism size, grain-size distribution, and lagoon water composition 

on the movement and retention of microbes. Transport parameters were estimated by fitting numerical simulations to 

experimental data. 

This project is completed and has resulted in multiple journal articles that provide detailed information on the various 

aspects of the project. This EPA report summarizes these results.

 David G. Jewett, Acting Director

Ground Water and Ecosystems Restoration Division

National Risk Management Research Laboratory

Foreword

v

Contents

Notice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii

Foreword . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii

Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v

Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii

Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix

Abbreviations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . x

Executive Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi

1.0 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Environmental Contaminants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Nutrients and Organics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Salts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Pathogens . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Land Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Water Flow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

Nutrient Transport . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

Pathogen Transport . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

Treatments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

2.0 Site Characterization for Detailed NMP Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

Materials and Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

Soft Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

Hard Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

Validation and Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

Bromide Travel Times . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

Averaging of Bromide Transport . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

3.0 NMP Results - Nutrients . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

Materials and Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

Field . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

Water and Nitrogen Mass Balances in the Root Zone . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

Management Considerations for Salinity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

Management Considerations for Organic Nitrogen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

Plant Available N, P, and K . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

Nitrogen Fixation – Alfalfa 2008 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

4.0 NMP Results – Indicator Microorganisms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

Materials and Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

NMP Field Site . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

Analysis and Sampling for Indicator Microorganisms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
vi

Laboratory Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

NMP Field Site . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

Laboratory Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

5.0 Laboratory Studies Investigating Mechanisms of Colloid Retention .................... 52

Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

Materials and Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

Colloids . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

Sand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

Electrolyte Solution Chemistry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

DLVO Calculations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

Batch Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

Column Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

Colloid Transport Model for Saturated and Unsaturated Systems . . . . . . . . . . . . . . . . . . . . . 54

Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

6.0 Mathematical Models to Simulate Pathogen and Nutrient Fate . . . . . . . . . . . . . . . . . . . . . . . 58

Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

Physical Chemical Nonequilibrium (PCNE) Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

Dual-Permeability Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

Stochastic Stream Tube Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

7.0 Summary and Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66

Quality Assurance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

8.0 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
vii

Figures

Figure 1.1. A schematic of the processes and nitrogen species that are involved in the nitrogen cycle. . . . . 9

Figure 1.2. A flowchart that illustrates the steps that may be used to assess and improve the 

performance of lagoon water application sites. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

Figure 2.1. A schematic of the field site. Squares represent two 6x6 m plots. . . . . . . . . . . . . . . . . . . . . . . . 17

Figure 2.2. A 4 ha map of the vertical component of the apparent soil electrical conductivity in the field 

as measured using a mobile remote electromagnetic induction sensor system on a 5x5 m grid. 19

Figure 2.3. North-south and east-west transects of estimated values of Ks on plot 1. . . . . . . . . . . . . . . . . . . 20

Figure 2.4. Field measurements of the relative bromide concentration over time for the bromide tracer 

experiment. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

Figure 2.5. Field measurements (circle) and 2D-MIM simulation (lines and triangle) of relative bromide 

concentration over time at -95, -126 and -155 cm. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

Figure 3.1. Schematic of the field site. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

Figure 3.2. The absolute value of the soil water pressure head (|h|) in the soil profile as a function of day 

after emergence (DAE) for the cyclic and blending water application strategies during the 

sorghum 2007 growing season. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

Figure 3.3. Electrical conductivity of the soil solution (ECw) over depth and total dissolved solids (TDS) 

load during 2007 for the blending and cyclic water application strategies. . . . . . . . . . . . . . . . . . 32

Figure 3.4. Nitrogen from ammonium (N-NH4) and nitrogen from combined nitrite and nitrate (N- 

(NO2+NO3)) concentration in the soil profile at the end (final) and beginning (initial) of two 

consecutive growing seasons (triticale 2007/sorghum 2007 and sorghum 2007/alfalfa 2008). . . 33

Figure 3.5. Three conceptual approaches for nitrogen NMPs based on low (top figure-333 g of N·m-2), 

intermediate (middle figure-1666 g of N·m-2), and high (lower figure-3333 g of N·m-2) soil 

organic N reservoirs. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

Figure 3.6. The composition, distribution, and amount of organic and inorganic N species (mg L-1) in 

dairy wastewater during the triticale 2007 growing season. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

Figure 3.7: Soil inorganic and organic N reservoirs ( NI

soil and NOsoil ) and cumulative values of N uptake 

by plant ( NI

plant ), exchange to/from organic and inorganic N forms (EOI), N loss to drainage 

( NI

drainage ), supplied organic N ( NOapplication ), and supplied inorganic N minus loss to the 

atmosphere ( NI

application - NIatmosphere ) in the root zone are presented over time. . . . . . . . . . . . . 37

Figure 3.8. Plant available phosphorus (P-PO4) and potassium (K) in the soil profile at the beginning 

(initial) of the triticale and end (final) of the sorghum growing seasons in 2007 for the 

blending and cyclic water application strategies. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

Figure 3.9. Dry phytomass, cumulative water application and actual evapotranspiration throughout the 

five growing cycles of alfalfa in 2008. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

Figure 4.1. Plots of the concentration of Enterococcus (Figure 4.1a), fecal coliform (Figure 4.1b), 

somatic coliphage (Figure 4.1c), and total E. coli (Figure 4.1d) in soil (S, N g-1) as a function 

of depth at several different days after emergence (DAE) during the winter triticale growing 

season at the NMP field site. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
viii

Figure 4.2. Plots of the gravimetric water content (qg ) (Figure 4.2a), and the normalized concentration 

of Enterococcus (Figure 4.2b), fecal coliform (Figure 4.2c), somatic coliphage 

(Figure 4.2d), and total E. coli (Figure 4.2e) in soil (S/CTi ) as a function of depth at 

selected sampling times (initial, and times = 0, 14, 62, 134, and 206 h after wastewater 

application) during the summer alfalfa growing season at the NMP field site. . . . . . . . . . . . . . . 47

Figure 4.3. A semi-log plot of the relative total concentration (CT /CTi) as a function of time in batch 

survival experiments under sterile (Figure 4.3a) and native (Figure 4.3b) conditions at 80% 

water saturation for the various indicator microorganisms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

Figure 4.4. Breakthrough curves (Figure 4.4a) and retention profiles (Figure 4.4b) for Enterococcus, 

total E. coli, and fX174 in a column experiment packed with sterilized field soil. . . . . . . . . . . 49

Figure 4.5. Plots of the normalized concentration of the indicator microorganisms in soil (S/CTi) as a 

function of depth at a sampling time of 24 h after ponded infiltration of wastewater ceased 

on the undisturbed soil core from the NMP site. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

Figure 4.6. A semi-log plot of the relative total concentration (CT /CTi) of the various indicator 

microorganisms as a function of time in the ponded infiltration undisturbed soil column 

experiment. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

Figure 5.1 Observed and simulated breakthrough curves of colloids for various saturation levels in 360 

μm sand at an ionic strength of 6 mM (Figure 5.1a) and 30 mM (Figure 5.1b) . . . . . . . . . . . . . 57

Figure 6.1. Plots of simulated breakthrough curves (Figure 6.1a) and retention profiles (Figure 6.1b) 

when qt was equal to 0.0918 cm min-1 and the value of wq2 was 0, 0.001, 0.002, 0.004, 

and 0.008 cm min-1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

Figure 6.2. Plots of the simulated breakthrough curves (Figure 6.2a) and retention profiles 

(Figure 6.2b) when wq2 was 0.001 cm min-1and qt was equal to 0.024, 0.046, 0.091, 

0.181, and 0.361 cm min-1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64

Figure 6.3. (a) Plot of the relative flux concentration, < vC > /( < v > Ci), at a depth of 10 cm as a 

function of time when v and kd are both stochastic parameters and values of ρvd = -1, 

-0.5, 0, 0.5, and 1. (b, c) Corresponding normalized solid phase colloid concentration, 

< S > /Nic , and associated variance after 250 min with depth, respectively. Model 

parameters that were employed in these simulations were D = 0.0313 cm2 min-1, 

< v > = 0.313 cm min-1, < kd > = 0.03 min-1, kr = 0.001 min-1, σv = 1, and σd = 1. . . . . . . . . . . . 65
ix

Tables

Table 1.1. A summary of measured total suspended solids (TSS), electrical conductivity (EC), 

oxidation-reduction potentials (ORP), ammonium (NH4-N), nitrite and nitrate 

(NO2+NO3-N), total Kjeldahl nitrogen (TKN), total phosphorus (TP), total organic carbon 

(TOC), and potassium (K) concentrations from several swine, poultry, dairy, and beef 

lagoon water samples. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Table 1.2. Indicator microbial populations in whole lagoon samples from different CAFOs. . . . . . . . . . . . . . 4

Table 1.3. Nutrient uptake parameters for selected crops (Kellogg et al., 2000). . . . . . . . . . . . . . . . . . . . . 6

Table 1.4. Hypothetical blending ratio (well water to lagoon water) for various lagoon waters when 

winter wheat and summer corn are grown using semi-arid and temperate climates. . . . . . . . . . 6

Table 1.5. The estimated environmental loading of total salts and indicator microorganisms when the 

various lagoon waters were applied to a 1 m2 area of agricultural field to meet the nitrogen 

needs of corn during a 90 d summer growing season. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

Table 2.1. The hydraulic properties (qs is the saturated water content; qr is the residual water content; 

a is the reciprocal of the air entry pressure; and n is the pore size distribution parameter 

of the van Genuchten model) of the three major layers in the upper soil profile of the field 

plot. Soft data was estimated from particle size distribution and bulk density data using the 

ROSETTA program. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

Table 2.2. Mean and standard deviation of bromide travel time (units in hours, h) at each sampling 

depth and culvert pipe location, and associated average and variance values for each depth. . . 22

Table 3.1. Ammonia volatilization from the sprinkler irrigation system and soil surface. . . . . . . . . . . . . . . . 29

Table 3.2. Potential and actual evapotranspiration (ET), crop coefficient, rainfall and water application 

during the growing season of triticale during winter 2007 (A) and sorghum during summer 

2007 (B), DAE is day after emergence. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

Table 3.3. Salts and macro-nutrients of a raw and treated dairy wastewater (DWW). DWW was 

sequentially treated by solid separator, sedimentation tank and sand filter. . . . . . . . . . . . . . . . . . 35

Table 4.1. Representative concentrations of indicator microorganisms in raw and treated dairy 

wastewater, and the percent removal by treatment. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

Table 4.2. Actual evapotranspiration (ET), rainfall, well water application, wastewater application, and 

drainage amounts during the winter (triticale) and summer (sorghum) growing seasons as 

a function of day after emergence (DAE). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

Table 4.3. Fitted parameters (lo and a) from the time dependent decay model (Equation 4.2) for the 

various indicator microorganisms under native conditions at 80% water saturation. . . . . . . . . . 48

Table 6.1. Expressions for the second- and third-order moments for breakthrough after a Dirac delta 

input according to physical nonequilibrium (PNE), chemical nonequilibrium (CNE), and 

PCNE models. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
x

Abbreviations

AWI air water interface

CAFO concentrated animal feeding operation

CML carboxyl modified latex

CNE chemical nonequilibrium

DAE day after emergence

DWW dairy wastewater

DLVO Derjaguin, Landau, Verwey and Overbeek

EC electrical conductivity

ET evapotranspiration

FC fecal coliform

IS ionic strength

MPN most probable number

NMP nutrient management plan

ORP oxidation–reduction potentials

PET potential evapotranspiration

PCNE physical and chemical nonequilibrium

PDF probability density function

PNE physical nonequilibrium

SWI solid water interface

TKN total Kjeldahl nitrogen

TOC total organic carbon

TP total phosphorus

TSS total suspended solids
xi

Executive Summary

The Environmental Protection Agency (EPA) currently requires that application of Concentrated 

Animal Feeding Operation (CAFO) wastes to agricultural fields follow an approved Nutrient 

Management Plan (NMP). The tacit assumption is that a well designed and executed NMP 

ensures that all lagoon water contaminants (nutrients and pathogens) are retained or taken up in 

the root zone, so that groundwater is inherently protected. Recent research by the EPA, however, 

has demonstrated that land application of CAFO lagoon water can cause nitrate contamination of 

groundwater at significant depths in short time frames. The research outlined in this report was 

designed to test the assumption that a well designed and executed NMP is protective of groundwater from nutrients, salts, and indicator microorganisms, and to address potential weaknesses in the 

land application design and operation processes of NMPs. 

The report is divided into seven chapters. Chapter 1 provides a detailed review of the literature 

examining the reuse of CAFO wastewater on agricultural fields, with special emphasis on nutrients 

and pathogens. A dairy farm in San Jacinto, California, was selected for our NMP research experiments. A well designed and managed NMP was implemented on two 6x6 m plots at this site for 

several years to study the fate of nutrients and indicator microorganisms. This study required 

a high level of confidence in the water flow and solute transport behavior and therefore detailed 

information on the associated soil properties was needed. Chapter 2 provides a description of 

our field site and extensive experiments that were conducted to characterize the flow and transport properties at this site. Chapter 3 provides a detailed description of the NMP implemented 

at this site and results pertaining to the fate of nutrients and salts. Chapter 4 describes field and 

laboratory research that was conducted to study the fate of indicator microorganisms under NMP 

conditions. Many gaps still exist with regard to our understanding of microorganism transport and 

retention in the field. Laboratory experiments and mathematical model development were therefore initiated to overcome some of these limitations. Chapter 5 outlines results from laboratory 

studies that examined the coupled effects of pore structure, solution chemistry, and water velocity on colloid retention in saturated and unsaturated porous media, whereas Chapter 6 describes 

the development and application of the mathematical models for colloid transport and retention. 

Chapter 7 provides a summary of the research, and the main conclusions and recommendations.

Collectively, this research led to the development of recommendations to improve NMP performance to protect groundwater under CAFO waste application sites from both nutrients and 

indicator microorganisms. The main NMP lessons learned from the study include:

• Only minor differences in NMP performance were found between cyclic and blending application 

strategies for dairy lagoon water. 

• NMPs need to account for the soil organic N reservoir in the N mass balance. This may induce 

difficulties in conservative NMP implementation that is protective of the environment due to: i) 

difficulties in estimation of mineralization rates and their spatial variability; ii) delayed availability 

of the organic N for plant uptake; and iii) continuous mineralization and potential nutrient leaching 

during fallow periods. 

• NMPs should be designed to meet plant N uptake and to minimize the migration of nutrients 

below the root zone. This was achieved in this research by accurately applying inorganic N in dairy 

wastewater (DWW) that was treated to remove most of the suspended solids, and minimizing the soil 
xii

organic reservoir by applying only a fraction of the plant N uptake with DWW. Additional research is 

needed to optimize the use of soil organic reservoirs under other NMPs. 

• The use of leguminous crops, such as alfalfa, in NMPs is more complicated than cereal crops. Potential 

advantages of leguminous crops are their deeper root system and greater plant N uptake. However, 

atmospheric N fixation is a nutrient source for leguminous crops that is difficult to quantify, and this 

creates potential challenges for implementing environmentally protective NMPs. Additional research is 

therefore needed to optimize NMP performance with leguminous crops. 

• DWW contain much higher quantities of salts than typical irrigation water. NMPs that precisely apply 

water and DWW to meet evapotranspiration (ET) will therefore accumulate salts in the root zone that 

may restrict plant growth, and water and nutrient uptake. If this reduction in ET is not considered at 

NMP sites, additional leaching and contaminant migration will occur. This point is strongly dependent 

on the salt tolerance of the crop, suggesting that NMPs should use only salt tolerant crops. 

• The leaching timing of excess salts below the root zone is a crucial aspect in NMP design because of 

continuous mineralization of organic N during fallow periods. In order to minimize NO3

-

 leaching, preirrigations should be scheduled at the end of the growing season, when the soil profile is depleted from 

NO3

-

 by plant uptake.

• A comprehensive measurement of N mass balance in the root zone requires information on losses to 

the atmosphere during irrigation. Atmospheric losses may be minimized by applying DWW during times 

that are associated with low potential ET (i.e., early morning), or through drip systems that minimize the 

exposure of DWW to the atmosphere. 

• Differences in the concentration ratios of N, P, and K between DWW and plant uptake may lead to 

accumulation of P and K in the root zone. 

• Improve measurements of water and nutrient requirements by the crop to obtain accurate information 

on the required timing and quantities for application and to minimize drainage (contaminant flux to 

groundwater).

• Increase the water and N use efficiency by irrigating to meet plant uptake requirements using a 

high uniformity application system. Minimize runoff and ponding conditions by matching the water 

application rate to the soil infiltration rate. 

• The transport potential of microorganisms can be significantly reduced by minimizing water leaching 

below the root zone and surface water runoff. This can be achieved by: precise estimation of the ET 

rate; uniform application of wastewater; and selecting water application timing and quantities based on 

considerations of soil permeability and ET. 

• Special caution is warranted for NMPs on coarse textured and structured soils, and during water flow 

transients where enhanced microorganism transport potential has been reported in the literature. 

• Timing of water application should allow for adequate die-off of microorganisms before leaching the root 

zone by irrigation or natural precipitation. 

• Develop a “hydrological sensitivity index” based on the soil and groundwater properties (depth, quality, 

hydraulic properties, and mineralogy of the vadose zone and aquifer). This index should categorize high 

and low potential zones of contamination from agricultural activity. Application of liquid and solid dairy 

wastes in low sensitivity zones would be more flexible than in other zones.
1

1.0

Literature Review

Background

New technological innovations and the 

economic advantage of size have driven a structural shift from small to large concentrated 

animal feeding operations (CAFOs) in the USA. 

It has been estimated that confined livestock 

and poultry animals in the USA generate about 

453 million tonnes of manure annually (Kellogg 

et al., 2000; USEPA, 2003). Water use at 

CAFOs includes drinking water for animals and 

water used in cooling facilities, sanitation and 

wash down of facilities, and animal waste-disposal systems. The United States Geological 

Survey estimated that livestock water use 

accounted for nearly 1% of the total freshwater 

withdrawals (excluding thermoelectric power) 

in the USA (Hutson et al., 2004). Daily use 

of water for farm animals was reported to be 

159 L d-1 for a milking cow, 18 L d-1 for a hog, 

and 18 L d-1 for 100 chickens. Large volumes 

of animal manure-containing wastewater, wash 

water, and storm water runoff can be generated 

at CAFOs (USEPA, 2001a). This manurecontaminated water is typically collected and 

stored in wastewater lagoons on farms (USEPA, 

2001a). 

Transportation, storage, and treatment of 

manure and manure-contaminated water are 

costly (Gleick, 2000). The large volume of 

waste generated, and the lack of disposal area 

at CAFOs, further limit the ability for effective 

manure management. Manure and wastewater are, therefore, usually land applied within 

about 16 km of CAFO facilities. When applied 

to agricultural lands at agronomic rates, 

manure and wastewater can be a valuable 

fertilizer and soil amendment that can improve 

the physical condition of the soil for plant 

growth (Jokela, 1992; Kapkiyai et al., 1999), 

reduce power required for tillage (Sommerfeldt 

and Chang, 1985, 1987), and increase the 

organic content of soil (Sommerfeldt et al., 

1988). The reuse of CAFO wastewater in 

irrigated agriculture also provides a potential 

means to reduce the demand for high quality water that is a scarce natural resource in 

many arid and semi-arid regions (Pimentel 

et al., 2004). Conversely, CAFO manure and 

wastewater have also been reported to pose a 

potential risk to environmental resources and 

to human health (Thorne, 2007; Burkholder et 

al., 2007). Contaminants of potential concern 

in animal wastes include excess amounts of 

nutrients (Jongbloed and Lenis, 1998; Mallin, 

2000), salts (Chang and Entz, 1996; Hao and 

Chang, 2003), organics rich in biochemical 

oxygen-demanding material (Webb and Archer, 

1994), heavy metals (Barker and Zublena, 

1996), microbial pathogens (Gerba and Smith, 

2005; Schets et al., 2005), antibiotics (Shore 

et al., 1988 and 1995; Nichols et al., 1997; 

Peterson et al., 2000), and natural and synthetic hormones (Hanselman et al., 2003; 

Raman et al., 2004). 

This review contains three sections, namely: 

(i) Environmental Contaminants; (ii) Land 

Application; and (iii) Treatments. The 

Environmental Contaminants section reviews 

the nutrients and pathogens that are found in 

CAFO lagoon water and why they may pose a 

risk to the environment and/or human health. 

The Land Application section reviews the 

current regulatory framework for land application of CAFO wastewater that is based upon 

Nutrient Management Plans (NMPs), the 

implicit assumptions and possible weaknesses 

in NMP design and application, illustrative 

nutrient and pathogen loading rates at NMP 

sites, and potential transport pathways for 

nutrients and pathogens. Finally, the section 

on Treatments discusses potential best management practices and lagoon water treatments 

that may be needed before land application of 

CAFO wastewater to minimize risks and dissemination of nutrients and pathogens in the 

environment.
2

Review

Environmental Contaminants

Three classes of potential lagoon water contaminants are considered in this section, 

namely: (i) nutrients and organics, (ii) salts, 

and (iii) pathogens. Tables 1.1 and 1.2 provide illustrative examples of concentrations 

of the various nutrients, salts and indicator 

microorganisms that were measured in CAFO 

lagoon water from several farms. Considerable 

variability is expected in the concentration of 

these contaminants at different farms due to 

differences in animal and waste management 

practices. Hence, the concentration values 

provided in these tables should be viewed as 

only reflecting site specific conditions at the 

indicated farm, and general trends should not 

be ascribed to these data. Wastewater from 

these lagoons, however, is actually used for 

land application at these farms, and therefore 

these data can be used to provide an estimate 

of potential environmental concentrations for 

the various contaminants. 

Table 1.1 provides a summary of measured 

total suspended solids (TSS), electrical conductivity (EC), oxidation-reduction potentials 

(ORP), ammonium (NH4-N), nitrite and nitrate 

(NO2+NO3-N), total Kjeldahl nitrogen (TKN), 

total phosphorus (TP), total organic carbon 

(TOC), and potassium (K) concentrations 

from several swine, poultry, dairy, and beef 

lagoon water samples (Hutchins et al., 2007). 

Concentrations of indicator microorganisms 

in these lagoon water samples are provided 

in Table 1.2. For these data, separate analyses were conducted on the samples collected 

as described previously (Hutchins et al., 

2007). Microbial indicators were analyzed 

on whole lagoon samples using a commercial 

most probable number (MPN) method (IDEXX 

Laboratories, ME). More general information 

on nutrient characteristics and other properties of various types of lagoon water samples is 

also available in the literature (MWPS, 1993; 

NCSU, 1994; USDA, 1996; ASAE, 1999; 

USEPA, 2001a; Bradford et al., 2008).

Table 1.1. A summary of measured total suspended solids (TSS), electrical conductivity (EC), oxidation-reduction potentials (ORP), ammonium (NH4-N), nitrite and nitrate (NO2+NO3-N), total Kjeldahl nitrogen (TKN), 

total phosphorus (TP), total organic carbon (TOC), and potassium (K) concentrations from several swine, 

poultry, dairy, and beef lagoon water samples (mean with standard deviation, three locations for each lagoon). 

CAFO TSS EC ORP NH4-N NO2+NO3-N TKN TP TOC K

Type mg

L-1

dS

m-1 mV mgL-1

mg 

L-1

mg

L-1

mg 

L-1

mg

 L-1

mg

 L-1

Beef 

Feedlot2

212 

± 28

2.6 

± 0.0

73 

 ± 26

33 

± 3

0.2 

± 0.01

63 

 ± 1

14 

± 0

155 

± 6

277 

± 2

Dairy2 718 

± 150

3.1 

± 0.0

-277 

 ± 24

84 

± 12

0.5 

± 0.1

185 

± 11

30 

± 1

576 

± 94

178 

 ± 29

Poultry1 847

 ±15

11.3 

 ± 0.2

-331 

 ± 21

656 

± 34

1.2

± 0.01

802 

± 7

50 

± 1

1050 

± 40

1430 

± 200

Poultry2 865

 ± 79

8.0 

± 0.0

-337 

± 2

289 

± 11

0.5 

± 0.01

407 

± 2

23 

 ± 0

374 

± 15

1490 

± 30

Poultry3 253 

 ± 75

4.4 

± 0.0

207 

 ± 28

58 

 ± 5

10.4 

± 2.3

96 

± 0

30 

± 0

114 

 ± 15

811 

± 14

Swine Sow1 1230 

 ± 30

12.6 

 ± 0.0

-348 

± 16

944 

± 23

2.8 

± 0.04

1290 

± 15

264 

 ± 5

944 

 ± 304

137 

 ± 2

Swine 

Finisher1

5310 

 ±2430

19.4 

 ± 0.0

-368 

 ± 2

1630 

± 20

8.0 

± 0.1

2430 

± 40

324 

 ± 14

4780 

 ± 280

242 

 ± 3

Swine 

Nursery1

4220 

 ±1940

21.5 

± 0.1

-368 

± 5

1370 

± 90

5.7 

± 0.8

2040 

± 60

368 

± 35

1440 

 ±130

4150 

± 210

1

 – Primary lagoon; 2 – Secondary lagoon; 3 – Tertiary lagoon; CAFO – Concentrated Animal Feeding Operation
3

Nutrients and Organics

Table 1.1 provides illustrative concentrations of nutrients (NH4-N, TKN, TP, and K) 

and organics (TOC) that were measured in 

several swine, poultry, dairy, and beef lagoon 

water samples. Ayers and Westcot (1989) 

reported severe restrictions on the use of 

irrigation water that had total nitrogen values 

greater than 30 mg L-1 due to potential problems discussed below. These same authors 

also reported that typical ranges for NH4-N, 

PO4-P, and K+ in irrigation water were 0-5, 

0-2, and 0-2 mg L-1, respectively. The EPA 

has set the Maximum Contaminant Level 

(MCL) in groundwater for nitrate (NO3

-

) and 

nitrite (NO2

-

) at 10 mg L-1 NO3-N and 1 mg L-1

NO2-N, respectively. The EPA-recommended 

guideline for TOC in drinking water is 

4 mg L-1. Hence, Table 1.1 indicates that 

nutrient and organic concentrations are very 

high in these lagoon water samples, relative to 

irrigation and drinking water standards. 

Published literature also indicates that animal 

wastes frequently contain high concentrations 

of nutrients (Chang and Entz, 1996; Hao and 

Chang, 2003) and organics rich in biochemical oxygen-demanding material (Webb and 

Archer, 1994) that can adversely impact 

soil and water quality (Jokela, 1992; Chang 

and Entz, 1996; Craun and Calderon, 1996; 

USEPA, 1997; USEPA, 2000). Potential 

environmental problems from excess amounts 

of nutrients and organics in water include 

algal blooms, reduced biodiversity, objectionable tastes and odors, and growth of toxic 

organisms in surface waters that are used 

for recreation and sources of drinking water 

(Mallin, 2000; Burkholder et al., 2007). 

These degraded conditions, especially the 

associated hypoxia/anoxia and high ammonia, 

have caused major kills of freshwater species 

(Burkholder et al., 2007). High nitrate levels 

in water have been associated with increased 

risk of methemoglobinemia for infants (bluebaby syndrome), as well as diarrhea and 

respiratory disease (Ward et al., 2005). 

Salts

Table 1.1 provides illustrative concentrations 

of EC that were measured in several swine, 

poultry, dairy, and beef lagoon water samples. 

Ayers and Westcot (1989) reported severe 

restrictions on the use of irrigation water that 

had EC values greater than 3 dS m-1. The 

salinity levels that are associated with these 

lagoon waters are very high relative to the 

irrigation quality guidelines. 

Prolonged exposure of agricultural lands to 

animal wastes that have high salinity levels 

may alter many soil physical and chemical properties, and crop yields (Burns et al., 

1985; King et al., 1985; Chang et al., 1991). 

For example, Chang et al. (1991) studied the 

effects of 11 years of cattle manure addition 

on soil chemical properties. Accumulation 

of specific ions (soluble sodium, calcium, 

magnesium, chloride, sulfate, bicarbonate, 

and zinc), nutrients (total N, nitrate, total P, 

available P), and organic matter increased 

with increasing application rates of manure 

and lagoon water. Near the soil surface the 

EC and the sodium adsorption ratio (SAR) 

increased and the pH decreased with increasing addition rates of manure. Changes in these 

soil chemical properties can in turn influence 

soil hydraulic properties. For example, high 

SAR values are frequently associated with 

decreases in the saturated hydraulic conductivity (Ayers and Westcot, 1989) as a result 

of dispersion of the clay (colloidal) fraction 

of soils. High levels of EC as well as specific ions (sodium, chloride, boron, nitrate, 

and bicarbonate) can also severely influence 

crop yields (Maas and Hoffman, 1977; Maas, 

1984) as a result of increases in osmotic 

stress and specific ion toxicities. 

Pathogens

Animal wastes frequently contain pathogenic 

viruses, bacteria, and protozoa that pose a risk 

to human and/or animal health (USDA, 1992; 

USEPA, 1998; Gerba and Smith, 2005). 

Although more than 130 microbial pathogens have been identified from all animal 
4

species that may be transmitted to humans 

by various routes (USDA, 1992; USEPA, 

1998), the most significant manure-borne 

zoonotic pathogens are the protozoan parasites 

Cryptosporidium parvum and Giardia duodenalis, and the bacterial pathogens Salmonella 

spp., Campylobacter spp., Escherichia coli 

O157:H7, and Listeria monocytogenes. 

Viruses of potential concern include: poliovirus, coxsackie virus, echovirus, hepatitis A, 

rotavirus, and Norwalk virus (Gerba and Smith, 

2005). 

The EPA has set drinking water goals for 

pathogens to be no detection, because of 

the low infectious dose for many pathogens (Loge et al., 2002) and variability of 

individual responses to infection (Gerba, 

1996). Regulations to protect public health 

from pathogens, however, are largely based 

on measured concentrations of indicator 

microorganisms such as total or fecal (thermotolerant) coliform (FC). For example, the World 

Health Organization (WHO) recommended 

standard for use of degraded water for irrigating crops eaten raw is <1000 FC per 100 mL 

(WHO, 2006), whereas in the USA the standard for unrestricted urban use varies with 

the state from non-detectable to 200 FC per 

100 mL (USEPA, 2004). Table 1.2 provides 

illustrative concentrations of various indicator microorganisms of fecal contamination 

(Total Coliforms, Fecal Coliforms, and Fecal 

Enterococci) that were measured in several 

swine, poultry, dairy, and beef lagoon water 

samples. The concentration of indicator 

microorganisms in the various lagoon water 

samples is very high, and significantly exceeds 

the recommended US standards for unrestricted irrigation (USEPA, 2004).

Surface and groundwater contamination 

by pathogenic microorganisms is common 

in many areas of the USA (USEPA, 1997). 

Drinking water exposures may occur in vulnerable private wells, whereas recreation 

exposures and illnesses can happen due to 

accidental ingestion of contaminated water 

and dermal contact during swimming. Surveys 

of waterborne disease outbreaks frequently 

demonstrate a farm animal source (Centers 

for Disease Control and Prevention, 1998). 

Pathogenic microorganisms in groundwater have been estimated to cause between 

750,000 and 5 million illnesses per year in 

the USA (Macler and Merkle, 2000). Greater 

risks of serious illness occur for the very 

young, elderly, pregnant women, the immunocompromised, and those predisposed with 

other illnesses (Gerba, 1996). 

Table 1.2. Indicator microbial populations in whole lagoon samples from different CAFOs (mean with standard deviation, three locations for each lagoon).

CAFO Total Coliforms Fecal Coliforms Fecal Enterococci

Type cfu per 100 mL cfu per 100 mL cfu per 100 mL

Beef Feedlot2 3.89E03 ± 0.99E03 1.57E03 ± 0.28E03 2.94E03 ± 1.07E03

Dairy2 1.91E07 ± 0.16E07 1.08E06 ± 0.11E06 1.53E05 ± 0.23E05

Poultry1 4.73E04 ± 2.09E04 2.91E04 ± 1.07E04 1.41E05 ± 0.67E05

Poultry2 2.42E03 ± 0.00E03 2.42E03 ± 0.00E03 8.51E04 ± 2.75E04

Poultry3 5.83E04 ± 0.28E04 2.03E01 ± 0.06E01 2.68E03 ± 1.24E03

Swine Sow1 4.89E05 ± 0.70E05 3.34E05 ± 0.43E05 3.69E05 ± 0.39E05

Swine Finisher1 1.02E06 ± 0.12E06 9.52E05 ± 3.44E05 8.50E05 ± 4.63E05

Swine Nursery1 1.87E05 ± 0.95E05 7.93E04 ± 8.30E04 2.43E05 ± 0.00E05

1

 – Primary lagoon; 2 – Secondary lagoon; 3 – Tertiary lagoon; CAFO – Concentrated Animal Feeding Operation
5

Manure-contaminated water resources have 

also been implicated in food-borne disease 

outbreaks on a variety of fresh produce (Gerba 

and Smith, 2005). The health impacts 

of such outbreaks can be very significant. 

For example, the 2006 outbreak of E.coli 

O157:H7 on spinach from the Salinas Valley, 

California, resulted in 205 illnesses and 3 

deaths (USFDA, 2007). Furthermore, loss of 

confidence in the safety of agricultural produce can have significant economic impacts. 

For example, the 2005 spinach crop from this 

same region was estimated to be worth around 

$188 million. 

Land Application

The Environmental Protection Agency (EPA) 

currently requires that CAFO waste application 

to agricultural lands follow approved Nutrient 

Management Plans (NMPs). The National 

Resources Conservation Service (NRCS) 

defines a NMP as, “Managing the amount, 

source, placement, form and timing of the 

application of nutrients and soil amendments” 

(USDA, 2000). The purpose of a NMP is 

to meet the nutrient needs of the crop to be 

grown, while minimizing the loss of nutrients 

to surface and groundwater (USEPA, 2003). 

Nutrient management plans are developed to 

adhere to state-specific NRCS guidelines. In 

general, a NMP is developed by considering 

all nutrient input sources (such as manure, 

fertilizer, lagoon water, and well water), the 

nutrient content at the soil surface, nutrient volatilization losses to the atmosphere 

(e.g., nitrogen losses as ammonia), nutrient 

mineralization rates, and plant uptake rates 

for nutrients. The nutrient concentrations 

are typically measured directly for the input 

sources, at the soil surface, and in plant 

tissues, whereas nutrient volatilization and 

mineralization rates are commonly estimated 

from literature values. The amount of lagoon 

water or other nutrient source that can be 

used in a given irrigation cycle is determined 

based on nutrient mass-balance considerations for a limiting nutrient (e.g., nitrogen or 

phosphorus). A review of several state NMP 

guidelines shows that they are typically written so as to be protective of surface water 

resources, and only provide limited direction 

on land application site characterization and 

design to protect groundwater from contamination (such as in regions with shallow water 

tables). 

A brief example of NMP implementation is 

given below. In this example we assume 

that nitrogen is the limiting nutrient for plant 

growth, and use the CAFO lagoon water composition information for nutrients presented in 

Table 1.1. Nitrate and nitrite only contributed 

a small fraction to the total N in the lagoon 

water samples (see Table 1.1) and were therefore neglected in this analysis. Table 1.3 lists 

nutrient uptake parameters for selected crops 

(Kellogg et al., 2000) that were used in this 

example. For simplicity we also make the 

following assumptions: N volatilization loss 

of 20%, N mineralization (ammonification) 

of 50% during the growing season, initially 

zero N in the soil profile and in the well water, 

and the only water source to the plants was 

irrigation water. Table 1.4 presents the blending ratios that were calculated to meet the 

nutrient (nitrogen) requirements for winter 

wheat and summer corn for silage assuming final yields of 27 and 56 tonne.

hectare-1, 

respectively, and 90 d of full cover crop. We 

performed this calculation for two climatic 

conditions, inducing different evapotranspiration (ET) fluxes that correspond to semi-arid 

and temperate areas. Table 1.4 indicates that 

the higher ET flux produces a higher blending 

ratio. Lagoon water with a low N concentration (Dairy and Beef) provides insufficient N 

at low ET fluxes and therefore an additional N 

source is required (fertilizer) in order to meet 

crop needs.
6

Table 1.3. Nutrient uptake parameters for selected crops (Kellogg et al., 2000).

Crop

Nitrogen Phosphorus

kg per ton of 

product

kg per ton of 

product

Corn for 

silage 3.6 0.5

Sorghum 

for silage 7.4 1.2

Barley 18.6 3.8

Wheat 20.6 3.6

Alfalfa 25.2 2.4

Grass 

silage 6.8 0.8

Data in Tables 1.1-1.4 can be used to provide 

an illustrative estimate of the environmental 

loading for the various contaminants identified 

in the lagoon water under NMP application 

conditions. As an example, Table 1.5 provides the estimated environmental loading of 

total salts (from EC values given in Table 1.1) 

and indicator microorganisms (sum of Total 

coliform and Enterococcus in a given row of 

Table 1.2) when the various lagoon waters 

(blended with well water) were applied to a 

1 m2 area of agricultural field to meet the 

nitrogen needs of corn during a 90 d summer 

growing season with an evapotranspiration rate 

of 10 mm d-1. In contrast to Tables 1.1-1.2, 

Table 1.5 is dependent on both the contaminant concentration in the lagoon water and on 

the blending ratio given in Table 1.4. 

Table 1.4. Hypothetical blending ratio (well water to lagoon water) for various lagoon waters when winter 

wheat and summer corn are grown using semi-arid and temperate climates. 

CAFO

Type

Plant 

available 

N*

Winter wheat

blending ratio

Summer corn

blending ratio

mg L-1

Semi arid 

climate

ET=5 mm d-1

Temperate 

climate 

ET=2.5 mm d-1

Semi arid 

climate

ET=10 mm d-1

Temperate 

climate 

ET=5 mm d-1

Beef Feedlot2 41 insufficient N insufficient N 0.93 0.46

Dairy2 118 0.05 insufficient N 4.56 2.28

Poultry1 598 3.88 1.43 27.46 12.90

Poultry2 290 1.37 0.18 12.79 5.74

Poultry3 65 insufficient N insufficient N 2.07 0.50

Swine Sow1 928 6.58 2.78 43.22 20.59

Swine Finisher1 1704 12.93 5.94 80.21 38.66

Swine Nursery1 1431 10.70 4.82 67.19 32.30

* Total N minus amount due to volatilization and ammonification; 1

 – Primary lagoon; 2 – Secondary lagoon; 3 – Tertiary 

lagoon; CAFO – Concentrated Animal Feeding Operation; ET – Evapotranspiration
7

Table 1.5. The estimated environmental loading 

of total salts and indicator microorganisms when 

the various lagoon waters were applied to a 1 m2

area of agricultural field to meet the nitrogen needs 

of corn during a 90 d summer growing season. An 

evapotranspiration rate of 10 mm d-1 was assumed. 

CAFO Total

Salts

Total 

Indicator 

Microbes

Type g m-2 number m-2

Beef Feedlot2 1755.0 6.1E+07

Dairy2 458.9 3.8E+10

Poultry1 277.8 6.2E+07

Poultry2 422.2 6.2E+07

Poultry3 1434.8 2.7E+08

Swine Sow1 196.8 1.8E+08

Swine Finisher1 163.3 2.1E+08

Swine Nursery1 216.0 5.8E+07

1

 – Primary lagoon; 2 – Secondary lagoon; 3 – Tertiary 

lagoon; CAFO – Concentration Animal Feeding 

Operation

Nutrient management plans assume that a 

limiting nutrient is the primary environmental concern. In order to minimize surface 

water runoff and leaching to groundwater, 

NMPs also imply that water is applied to the 

crop to meet the potential evapotranspiration demands. The environmental loading 

information presented in Table 1.5 indicates 

that significant amounts of salts and indicator 

microorganisms will be added to agricultural 

lands where a NMP is being implemented. 

An implicit assumption of a NMP is that all 

CAFO contaminants will be taken up, inactivated, retained, or degraded in the root zone, 

so that surface and groundwater are inherently 

protected. The validity of these assumptions 

for all lagoon water contaminants has not yet 

been thoroughly studied. Below we discuss 

possible weaknesses in NMP design and application, as well as potential transport pathways 

and processes for nutrients and pathogens. 

Water Flow

The basic premise of a NMP is that water 

and nutrients are applied to the root zone 

at agronomic rates so that both surface and 

groundwater resources will be protected from 

all contaminants in lagoon water. Potential 

water inputs at a NMP application site include 

irrigation water (well and/or CAFO lagoon 

water), precipitation, surface water, snow 

melt, soil water, and shallow groundwater 

tables. The water application amount per 

irrigation should be based on measurements 

or estimates of potential evapotranspiration 

(PET) and water-balance considerations at 

the application site. If water input at a NMP 

application site equals PET then surface and 

groundwater contamination should theoretically be eliminated because all of the applied 

water to the site is used by the crop. 

When water in excess of PET is applied to a 

NMP site then both surface and groundwater 

contamination problems are possible. This 

situation can arise from a number of different scenarios that may be beyond human 

control. Potential problems may arise due 

to inaccuracies in the estimation of PET, or 

from not accounting for all the sources of 

plant available water (perched water tables, 

precipitation, snow melt, and surface water 

runoff). For example, significant amounts of 

unexpected precipitation (a thunderstorm) 

after application of nutrients may lead to 

surface water runoff and/or leaching of contaminants. Other problems can occur due to 

nonuniform water application practices during 

flood or furrow irrigation or as a result of 

spatial variability of soil hydraulic properties 

(e.g., different amounts of water are applied to 

specific locations in the field) and evapotranspiration. Changes in surface topography that 

produce surface water runoff and lateral flow 

to other locations in the field pose other NMP 

difficulties. 

Nutrient management plans implicitly assume 

that water flow and lagoon water contaminant 

transport are controlled by the soil matrix. 
8

Preferential flow is a potentially important 

mechanism for water and contaminant transport to bypass portions of the soil matrix and 

root zone. Several recent reviews and other 

papers provide detailed discussions of the 

various processes and conditions leading to 

preferential flow (Ritsema et al., 1993; de 

Rooij, 2000; Evans et al., 2001; National 

Research Council, 2001; Bodvarsson et al., 

2003; Šimůnek et al., 2003; Wang et al., 

2004). In summary, preferential water flow 

can occur as a result of funneling of water at 

textural interfaces (capillary barriers), unstable 

flow behavior that is induced by spatial and/or 

temporal variations in capillary characteristics 

(wettability or hysteresis), dynamic capillary 

properties (nonequilibrium capillary pressure 

- water content characteristics), macropores, 

and fractured systems. All of these factors 

can potentially accelerate the movement of 

lagoon water contaminants through the root 

zone to underlying groundwater. Some mechanisms of preferential flow are also anticipated 

to depend on the initial soil water content and 

water application rate. For example, soil water 

repellency is reported to occur below some 

critical water content (Ritsema and Dekker, 

2000), hysteresis is induced when infiltration stops and redistribution occurs (Wang et 

al., 2003), and filling of macropores occurs 

primarily near saturated conditions (Mohanty 

et al., 1997). Improved NMP designs are 

needed to provide guidelines to minimize 

transport processes that bypass the root zone. 

For example, it may be possible to minimize 

preferential flow in macropores by applying 

irrigation water at rates lower than the saturated conductivity of the soil.

Nutrient Transport

It should be mentioned that NMPs are only as 

good as the information on evapotranspiration 

and nutrient mass balance. Errors in water 

and nutrient application rates, or nonuniform 

water application will likely lead to excesses 

and/or shortages in lagoon water application 

in certain locations. Such errors are potentially responsible for some of the reported 

environmental problems associated with 

lagoon water application sites (Evans et al., 

1984; King et al., 1985; Sims et al., 1998; 

Correll, 1998). Another potential problem 

arises when the relative content of nitrogen 

and phosphorus in lagoon water differs from 

that in the crop. In this case, NMPs that are 

designed to meet the nitrogen requirement 

for crops may result in the over-application 

of phosphorus. Conversely, NMPs based 

on crop phosphorus needs may significantly 

reduce the lagoon water application rates, and 

thus require nitrogen fertilizers to meet plant 

needs.

The biogeochemistry of nutrients at NMP 

sites can be quite complex. For example, 

Figure 1.1 shows a schematic of the processes 

and nitrogen species that are involved in the 

nitrogen cycle. The most important inorganic 

forms of nitrogen are ammonium ions (NH4

+), 

ammonia gas (NH3), nitrite (NO2

-

), nitrate 

(NO3

-

), nitrogen gas (N2), nitrous oxide (N2O), 

and nitric oxide (NO). Organic nitrogen compounds in lagoon water occur as urea, amino 

acids, amines, purines, and pyrimidines, as 

well as a fraction of the dry weight of plants, 

microorganisms, detritus, and soils. Organic 

and inorganic forms of nitrogen are continually 

involved in biogeochemical transformations. 

The major processes involved in the nitrogen 

cycle are ammonification (mineralization of 

organic nitrogen to ammonium), nitrification 

(oxidation of ammonia to nitrate), denitrification (reduction of nitrate to nitrogen gas), 

nitrogen fixation (reduction of nitrogen gas to 

ammonia), nitrogen assimilation (conversion of 

inorganic nitrogen to organic forms), and nitrogen volatilization (conversion of ammonium 

ion to ammonia gas). 

The biogeochemistry of phosphorus at NMP 

sites is also very complex. Most phosphorus 

(63 to 92%) in manure and lagoon water 

occurs in inorganic forms (Sharpley and 

Moyer, 2000). Bohn et al. (1985) presents 

a speciation diagram of phosphorus species 

of dissolved orthophosphoric acid (H3PO4) 
9

as a function of pH. The three acid dissociation constants for H3PO4 at 25°C are 

7.5x10-3 (pKa1=2.1), 6.2x10-8 (pKa2=7.2), 

and 5.0x10-13 (pKa3=12.3). The dominant 

phosphate species in the pH range of 2.1-7.2 

is H2PO4

-

, whereas HPO4

-2 is the main species in the pH range of 7.2-12.3. Both of 

these species are available for plant uptake. 

Phosphorus, however, forms complex minerals 

with many elements (Arai and Sparks, 2007). 

For example, phosphorus species frequently 

precipitate out of solution with calcium, iron, 

and aluminum ions (Yeoman et al., 1988; 

Moore and Reddy, 1994). Furthermore, 

phosphorus species can strongly adsorb to 

positively charged surfaces in soils (Torkzaban 

et al., 2006b; Arai and Sparks, 2007), such 

as on metal oxide surfaces and clay edges. 

The solubility of phosphorus in soil solution 

is, therefore, a complex function of biogeochemical variables and processes such as pH, 

oxidation-reduction potential, the concentrations of cations (such as Ca2+, Mg2+, Al3+, and 

Fe3+) and phosphorus species, soil mineralogy 

and organic matter content, and the mineralization rate of organic (manure) compounds 

(Moore and Reddy, 1994; Sharpley, 1995; 

Arai and Sparks, 2007). Measurements 

of total phosphorus in soils may, therefore, 

not accurately assess the potential for plant 

uptake of phosphorus and the potential for 

subsurface transport losses (Brock et al., 

2007). 

The subsurface fate of nutrient species at 

NMP sites depends on a variety of transport 

and mass transfer processes. For example, 

dissolved inorganic nitrogen ions such as 

ammonium, nitrite, and nitrate can be transported with flowing water (advection), as well 

as by diffusive and dispersive solute fluxes. 

Interface mass transfer processes may play 

important roles between dissolved nitrogen 

species and soil/sediment surfaces (e.g., sorption/desorption of ammonium) or the air phase 

(e.g., volatilization of ammonia), and uptake 

of nitrogen species by plants and microorganisms is anticipated. The presence of colloidal 

forms (manure suspension) containing organic 

nitrogen and phosphorus that are mobile 

in the water phase provide another nutrient 

transport pathway. 

Environmental losses of nutrient species 

that are primarily associated with the solid 

Figure 1.1. A schematic of the processes and nitrogen species that are involved in the nitrogen cycle. 

Important inorganic forms of nitrogen are ammonium ions (NH4

+), ammonia gas (NH3 ), nitrite (NO2-

), nitrate 

(NO3

-

), nitrogen gas (N2 ), nitrous oxide (N2O), and nitric oxide (NO). The dashed lines denote secondary 

transport processes.
10

phase (such as ammonium and phosphorus) 

will occur mainly in conjunction with high 

sediment loads during surface water runoff 

(Sims et al., 1998; Correll, 1998). For a well 

designed NMP that is conducted on most 

mineral soils, little leaching and subsurface 

transport of phosphorus is expected. Sims et 

al. (1998), however, review the literature that 

indicates that phosphorus species may also 

be leached through soils under certain environmental conditions (e.g., deep sandy soils, 

high organic matter soils, or soils with high 

soil phosphorus concentrations from longterm over fertilization and/or excessive use of 

organic wastes). Pautler and Sims (2000) 

suggested that the degree of phosphorus saturation, DPS (defined as the ratio of the amount 

of phosphorus sorbed by the soil to the phosphorus sorption capacity), is a better indicator 

for the potential losses to subsurface transport 

than the total phosphorus. Increased addition 

of manure has been reported to nonlinearly 

increase the phosphorus sorption capacity of 

the soil (Brock et al., 2007). When the DPS 

was greater than around 30%, phosphorus 

transport from soils increased dramatically 

(Brock et al., 2007).

Pathogen Transport

The root and vadose zones at NMP application sites play a critical role in protecting 

water supplies from pathogen contamination. 

Effective treatment relies on the retention and 

inactivation of pathogens in unsaturated or 

variably saturated porous media. Inactivation 

of pathogenic microorganisms is commonly 

assumed to occur within 60 d, although many 

viruses and protozoa are known to be viable 

for a longer duration (Schijven et al., 2006). 

Considerable research has been devoted to 

the fate and transport of microorganisms and 

other colloids in porous media (reviews are 

given by Herzig et al., 1970, McDowell-Boyer 

et al., 1986; McCarthy and Zachara, 1989, 

Ryan and Elimelech, 1996; Khilar and Fogler, 

1998; Schijven and Hassanizadeh, 2000; 

Harvey and Harms, 2002; Jin and Flury, 

2002; Ginn et al., 2002; de Jonge et al., 

2004; DeNovio et al., 2004; Rockhold et al., 

2004; Sen and Khilar, 2006; Tufenkji et al., 

2006). Most of the published research pertains to saturated media, less is known about 

microbe transport and retention in unsaturated systems (Wan and Wilson, 1994b; Choi 

and Corapcioglu, 1997; Wan and Tokunaga, 

1997; Schafer et al., 1998ab; Saiers et al., 

2003; Saiers and Lenhart, 2003; de Jonge et 

al., 2004; DeNovio et al., 2004). Below we 

briefly review the literature about processes 

and factors that will potentially influence the 

transport and fate of pathogens and surrogates 

(indicator microorganisms, latex microspheres, 

and other colloids) at NMP sites.

Microorganism retention mechanisms in the 

vadose zone are more complicated than in 

the saturated zone mainly due to the presence of air in the system. In unsaturated 

porous media water flow is restricted by capillary forces to the smaller regions of the pore 

space and flow rates are relatively small. 

Microorganism transport may be influenced 

by increased attachment to the solid-water 

interface (Chu et al., 2001; Lance and Gerba, 

1984; Torkzaban et al. 2006a), attachment 

to the air-water interface (Wan and Wilson, 

1994ab; Schafer et al., 1998b; Torkzaban et 

al., 2006b), straining of microorganisms near 

multiple interfaces in the smallest regions of 

the pore space (McDowell-Boyer et al., 1986; 

Cushing and Lawler, 1998; Bradford et al., 

2006a), film straining in water films enveloping the solid phase (Wan and Tukonaga, 

1997; Saiers and Lenhart, 2003), and retention at the solid-air-water triple point (Crist et 

al., 2004 and 2005; Chen and Flury, 2005; 

Zevi et al., 2005; Steenhuis et al., 2006). 

Transients in water content and composition 

during infiltration and drainage processes can 

also significantly influence these unsaturated 

retention mechanisms (Saiers et al., 2003; 

Saiers and Lenhart, 2003; Torkzaban et al., 

2006b).

Size exclusion affects the mobility of microorganisms in the vadose zone by constraining 
11

them to more conductive flow domains and 

larger pore networks that are physically accessible (Ryan and Elimelech, 1996; Ginn, 

2002). As a result, microbes can be transported faster than a conservative solute tracer 

(Cumbie and McKay, 1999; and Harter et al., 

2000; Bradford et al., 2003). Differences 

in the dispersive flux for microbes and a conservative solute tracer may also occur as a 

result of size exclusion (Sinton et al., 2000; 

Bradford et al., 2002).

Various combinations of well, surface, and 

lagoon water, encompassing a range in solution chemistry, are used for irrigation at NMP 

sites. Many chemical factors (i.e., pH, ionic 

strength, surface charge, etc.) are known to 

influence the transport behavior of pathogens. For example, using colloids as pathogen 

models, colloids are stabilized when the electrical double layers are expanded and when 

the net particle charge does not equal zero 

(Ouyang et al., 1996). Increasing electrolyte 

concentration and ionic strength decreases 

the double layer thickness and thereby promotes aggregation and microorganism-porous 

medium interactions. Aggregation will in 

turn impact the colloid size distribution 

and therefore straining. Aqueous phase pH 

influences the net microorganism and solid 

surface charge by changing pH-dependent 

surface charge sites. McCarthy and Zachara 

(1989) reported that colloids can be produced 

through disaggregation when the ion balance 

is shifted from one dominated by Ca2+ to one 

dominated by Na+. These chemical factors 

(pH, ionic strength, surface charge, and chemical composition) are also known to influence 

soil structure (disaggregation) and pore size 

distribution (shrinking and swelling) when the 

soil contains clays and other colloidal materials (Ayers and Westcot, 1989).

Most transport experiments with pathogens 

have been conducted in the absence of dissolved manure suspensions. Pathogens in 

lagoon water, however, constitute only a 

small portion of the colloidal suspension. 

Large quantities of manure components 

are also present. Several researchers have 

examined the influence of organic matter or 

manure suspensions on microbe transport. 

Some researchers reported that dissolved 

organic matter (DOM) or manure suspensions 

enhanced microbe transport (Johnson and 

Logan, 1996; Pieper et al., 1997; Powelson 

and Mills, 2001; Guber et al., 2005ab; 

Bradford et al., 2006cd). Blocking of favorable attachment sites by organic matter has 

typically been used to explain this enhanced 

transport (Johnson and Logan, 1996; Pieper 

et al., 1997; Guber et al., 2005ab), although 

filling of straining sites provides an alternative explanation (Bradford et al., 2006c). 

Dissolved organic matter has also been 

reported to sorb onto bacterial cell walls and 

alter their electrophoretic mobility (Gerritson 

and Bradley, 1987). Increasing the negative 

charge of the bacterial surface diminishes 

its attachment onto negatively charged 

solid surfaces (Sharma et al., 1985). Other 

researchers have reported that organic matter 

inhibits microbe transport due to hydrophobic interactions between microbe and grain 

surfaces that are coated with organic matter 

(Bales et al., 1993). Adsorption of pathogens 

onto mobile manure colloids could also facilitate their transport potential (Jin et al., 2000; 

de Jonge et al., 2004). 

Many microbiological factors will also play 

important roles in pathogen migration at NMP 

sites. Microbe growth, death, and inactivation are of special importance. The ability of 

many pathogens to survive in manure (Wang 

et al., 1996), lagoon water (Ibekwe et al., 

2003), and soil (Gagliardi and Karns, 2000 

and 2002) has been well documented. The 

survival and subsequent transport of viruses 

have also been reported to be enhanced in the 

presence of manure suspensions (Bradford 

et al., 2006d), likely due to sorption of 

organic components onto metal oxide surfaces 

that would otherwise inactivate the viruses 

(Sagripanti et al., 1993; Pieper et al., 1997; 

Schijven et al., 1999; Chu et al., 2001; and 
12

Ryan et al., 2002). Some microbes can also 

alter their surface characteristics and surrounding environment so as to promote attachment 

or release (detachment) (Ginn et al., 2002). 

Other mobile microbes possess the ability 

to move in the direction of increasing concentration gradient of chemoattractants or a 

decreasing concentration gradient of chemorepellents (Nelson and Ginn, 2001). Many of 

these microbiological processes are expected 

to be strongly coupled with temperature and 

nutrient conditions at NMP sites. 

Treatments

Figure 1.2 presents a flowchart that illustrates 

the potential steps that may be used to assess 

and improve the performance of lagoon water 

application sites. To determine whether the 

reuse of CAFO wastewater on agricultural lands 

poses an acceptable environmental risk, it is 

necessary to monitor the transport and fate 

of relevant contaminants at these sites. At 

present, land application of CAFO lagoon water 

is based upon a nutrient balance. As mentioned earlier, other potential contaminants 

of concern at NMP sites include salts, heavy 

metals, pathogens, antibiotics, and hormones. 

If the risk (measured concentration of a particular contaminant below or in the root zone) 

is deemed to be unacceptable, then management practices for animals will have to be 

modified and/or wastewater treatment will have 

to be implemented before this water can be 

used in NMPs. Based on current technologies and associated costs, it is not practical for 

this level of monitoring to be done for typical 

farm operations. Additional field research is, 

therefore, needed to determine appropriate 

contaminant loadings for effective NMPs, and 

to assess whether additional treatment steps 

are needed. Below we discuss potential tools, 

treatments, and management practices that 

may be needed.

Figure 1.2. A flowchart that illustrates the steps that may be used to assess and improve the performance 

of lagoon water application sites. 
13

Computer models are valuable tools that can 

be used by researchers to study the transport 

and fate of lagoon water contaminants under 

various environmentally relevant scenarios 

that are found at NMP sites. Conventional 

models for contaminant transport are based 

upon solution of mass conservation equations for flowing water (Richards equation) 

and solute transport (advection-dispersion 

equation), subject to a variety of equilibrium 

or kinetic reactions (e.g., Šimůnek et al., 

2009). Improved NMPs could potentially be 

designed with the aid of computer models to 

maximize desirable effects such as nutrient 

uptake by plants, soil retention of pathogens, 

and to minimize the leaching and/or runoff of 

contaminants. 

To efficiently utilize CAFO lagoon water and 

diminish the potential of manure contaminants to be transported to groundwater and 

surface water bodies it is essential to plan and 

implement proper management. Manipulation 

of animal diet and veterinary practices are 

likely to be some of the most simple and 

cost effective ways to minimize the potential 

release of some contaminants into the environment. For example, it has been reported 

that changing cattle diet from grain to forage 

will reduce the numbers of pathogenic E. coli 

O157:H7 in cattle manure (Russell et al., 

2000; Callaway et al., 2003). Selection of 

CAFO locations away from vulnerable water 

resources such as shallow water tables and 

surface water, and on soils with relatively little 

structure and moderate infiltration rates are 

other common sense practices to minimize the 

potential for water contamination. Sufficient 

agricultural land should also be located adjacent to CAFOs so that the manure and lagoon 

water that is generated may be applied to 

these lands at agronomic rates. Actions to 

further avoid water resource contamination 

may include: proper timing of lagoon water 

application (not during rainy seasons); leveling 

the CAFO facilities to a small incline towards a 

draining conduit and channeling runoff water 

to a designated storage reservoir (lagoon); 

and avoiding manure accumulation on CAFO 

grounds, especially during rainy seasons.

Physical separation is commonly used as an 

effective pretreatment method to reduce the 

organic load to CAFO wastewater and therefore 

decrease nutrient and pathogen concentrations in lagoons. Separation methods to 

remove organic solids from the wastewater 

include using stationary inclined screens, 

vibrating screens, rotary screens, a horizontal decanter centrifuge, hydrocyclone, as 

well as roller, belt, and screw presses (Zhang 

and Westerman, 1997). The separated 

manure sludge may in turn have to be treated 

(composted) before it can be used as a soil 

amendment and as a source for nutrients. 

Lagoons for CAFO wastewater storage act in 

a similar fashion as sedimentation basins in 

municipal wastewater treatment. Given sufficient retention time in lagoons, significant 

reductions in total suspended solids are 

expected (35-65%). Most lagoons operate 

anaerobically. Aerated lagoons have received 

less attention because of their higher costs 

and their increased potential of transferring 

reactive nitrogen to the atmosphere through 

volatilization of ammonia. However, the 

potential for decreased odor and increased 

nitrogen reduction might increase their use 

if economical treatment methods emerge 

to mitigate ammonia loss. Typically, CAFO 

wastewater storage in lagoons can reduce the 

amount of nitrogen by 30 to 75% through 

volatilization (depending on whether the 

lagoon is anaerobic or aerobic) (Svoboda, 

1995; USEPA, 2001a). Phosphorus tends 

to be associated with the particulate fraction 

of lagoon water, and nitrogen and potassium are usually in dissolved form. Hence, 

as particles settle out of solution the lagoon 

sludge contains less nitrogen and potassium 

but more phosphorus than the lagoon liquid. 

Up to 80% of the phosphorus in lagoons can 

accumulate in bottom sludge (MWPS, 1993; 

Lander et al., 1998). The bottom of CAFO 

lagoons should be properly sealed to prevent 
14

deep percolation of wastewater towards 

groundwater resources. Most states have 

regulations regarding permissible levels of 

percolation rates from lagoon storage systems. 

For example, regulations in Kansas stipulate 

that percolation rates should be less than 0.3-

0.63 cm d-1. 

Lagoon water from CAFOs may have to be 

adjusted to become a stable, balanced, and 

nutrient-rich product for reuse as irrigation 

water and fertilizer during implementation of 

NMPs. This may require active management 

and treatments that are not currently practiced on farms. Many municipal wastewater 

treatment techniques may be applied to CAFO 

wastewater (Zhang and Lei, 1998; Chastain et 

al., 2001; Vanotti et al., 2005ab; Loughrin et 

al., 2006). For example, chemical treatments 

may be used to promote flocculation of particles by neutralizing the electrostatic forces 

that keep them apart, causing the particles to 

form aggregate flakes (flocs) that rapidly settle 

out of solution. Commonly used flocculants 

include various salts of multivalent cations 

such as aluminum, iron, calcium or magnesium, and/or long-chain polymers (Dobias, 

1993). Other factors such as pH, temperature, aeration, and salinity can be controlled 

to induce flocculation (Lindeburg, 2001). 

Given optimum amounts of chemical additives 

and sufficient settling time, flocs will form 

and settle out of solution to clarify wastewater. Chemical flocculation treatments involve 

equipment, chemicals, and maintenance that 

are not typically found on CAFOs. Vanotti 

and Hunt (1999) and Vanotti et al. (2002) 

estimated it would cost an additional $1.27 

to $2.79 per finished hog to remove 90-95% 

of the suspended solids in swine lagoon 

water using chemical treatments. Additional 

research is needed to assess whether such 

approaches will be economically viable to 

implement. 

Alternatively, it may also be possible to 

adapt a variety of low cost soil treatments 

for wastewater to remove excess amounts 

of organics, nutrients, pathogens, and other 

contaminants in CAFO lagoon water. Potential 

soil treatments for wastewater include: infiltration ponds and galleries, sand filtration, 

and subsurface flow wetlands (Schijven and 

Hassanizadeh, 2000; Tufenkji et al., 2002; 

Ray et al., 2002; Hunt et al., 2002; Weiss 

et al., 2005). Treatment by passage through 

sands can provide a physical treatment for 

particles in wastewater due to size limitations imposed by the pore sizes (Bradford 

et al., 2006a), but also provides solid-water 

and air-water interfaces where chemical and 

microbiological reactions and transformations may occur (Tufenkji et al., 2002). 

Furthermore, proper selection and design 

of the solid surface chemistry of the porous 

media can be used to target the retention and/

or degradation of specific contaminants of 

interest. For example, metal oxide coatings on 

solid surfaces can be created to adsorb and/

or inactivate a variety of microbial pathogens, 

as well as inorganic and organic compounds 

(Joshi and Chaudhuri, 1996; Ahammed and 

Chaudhuri, 1999). Soil treatment forms the 

theoretical basis for many permeable reactive 

barrier technologies that have been developed 

and applied to treat contaminated groundwater 

plumes (Gu et al., 1999; Benner et al., 1999; 

Scherer et al., 2000; Waybrant et al., 2002). 

The collected CAFO wastewater effluent from 

soil treatment could in turn be used in NMPs 

to irrigate agricultural lands with mitigated 

risk to the environment.

Maintaining soil and groundwater quality is 

the key to the long-term successful use of 

NMPs. If leaching is inadequate, then dissolved salts will concentrate and accumulate 

in soil profiles when irrigation water that is 

applied to crops subsequently returns to the 

atmosphere via evapotranspiration. The total 

dissolved solids of CAFO wastewater typi f CAFO wastewater typically range between 1500 to 3500 mg L-1, 

equivalent to electrical conductivity levels of 

2.4-5.5 dS m-1. Salt, sodium, and osmotic 

effects of salinity can restrict crop establishment and growth due to changes in soil 
15

physical structure, tilth, infiltration and 

permeability, sodium toxicity, and salt accumulation (Ayers and Westcot, 1989; Chang et 

al., 1991). Selective use of gypsum, aggregate 

stability imparted by crops, direct seeding of 

crops, and careful irrigation timing of lagoon 

water application under a site-specific management regime are potential methods that 

may be used to ameliorate the negative effects 

of CAFO lagoon waters on soil and groundwater quality (Ayers and Westcot, 1989). Salt 

deposits can also be flushed away by adding 

excess amounts of fresh water, but at a significant cost (Bouwer, 2002). Furthermore, 

desalination of wastewater and removing the 

excess salt to the ocean can cost up to $2 per 

1000 L. Selection of salt-tolerant crops and 

timing of nutrient application are, therefore, 

important considerations for NMPs. The salt 

tolerance characteristics of various crops are 

provided in Ayers and Westcot (1989).
16

2.0

Site Characterization for 

Detailed NMP Studies

Background

Field research was designed to study the 

transport and fate of nutrients and indicator 

microorganisms at a NMP site. To achieve a 

high level of confidence in the water flow and 

solute transport behavior at this site, detailed 

information on the associated soil properties 

was required and is discussed in this chapter. 

The first phase of our approach was site selection. Since the transport and fate of nutrients 

and microorganisms in the vadose zone are 

very complex processes (Bradford et al., 2008; 

Bradford and Torkzaban, 2008) measurements 

of the apparent soil electrical conductivity 

were used to select a highly uniform location 

in a 4 ha field to reduce the level of complexity and uncertainty. The second phase 

of our approach involved an initial site characterization to identify the soil stratigraphy, 

approximate particle size distribution, and 

bulk density information on soil cores and pits. 

Results revealed that a uniform layer of sandy 

loam was situated from the surface down to a 

depth of about −70 cm, followed by a white 

sand layer (−70 to −80 cm). The deeper profile (less than −80 cm) was characterized by a 

sandy loam layer, which incorporated various 

sand and clay lenses at different depths. In 

the third stage, pedotransfer functions and 

interpolation algorithms were used to generate an initial conceptual model about the soil 

profile, including initial estimates of hydraulic 

properties (soft data). This conceptual model 

guided the selection location for undisturbed 

cores that capture the dominant water flow 

behavior under study. 

The hydraulic properties of and conservative 

solute transport in undisturbed core samples 

were subsequently measured in the laboratory (hard data) and used to further refine the 

conceptual model of the site properties. The 

next phase of site characterization was field 

validation of the conceptual model of the soil 

hydraulic and solute transport properties. 

Water flow and solute transport experiments 

were used for this purpose, and to ensure good 

agreement between measured and simulated 

data. A feedback between field experiments 

and computer simulations was used to identify locations in the field where additional 

measurements or computer simulations were 

needed to improve the conceptual model and 

level of understanding at the site. 

Materials and Methods

A mobile remote electromagnetic induction 

sensor system (Geonics EM-38DD) was used 

to measure the apparent soil electrical conductivity (ECa

) across a 4 ha field (Corwin 

and Lesch, 2005a). This system includes two 

EM-38 units synchronized to operate simultaneously, enabling measurements of both 

vertical and horizontal dipole conductivity. 

Data was collected following the protocols 

of Corwin and Lesch (2005b) to generate an 

approximately 5×5 m grid ECa map of our field 

site. The map coordinates were determined 

using a global positioning system (GPS).

Selection of our experimental field plot for 

nitrate and microorganism transport studies was based upon the generated ECa

 map. 

The agricultural field in San Jacinto, CA 

(33°50′22 ̋ North, 117°00′46 ̋ West) was 

chosen for this purpose because it was associated with minimal spatial variability in ECa

readings, which implies relatively low heterogeneity in soil texture. The experimental 

plot is located next to a dry river bed and has 

a shallow perched water table at a depth of 

-220 cm. 
17

Figure 2.1. A schematic of the field site. Squares represent two 6x6 m plots. Circles with letters are vertical culvert pipes (120 cm diameter by 220 cm long) installed with six tensiometers. Circles with roman 

numerals are vertical neutron probe access tubes. Arcs represent the area of water potential sampling. The 

crossed circle in the center of Plot 2 denotes the origin of a radial axis (r). 

The experimental site consisted of two 6x6 m 

plots (Figure 2.1). At the corners of each 

plot a backhoe was used to expose the soil 

profile and to install 120-cm diameter by 

220-cm long culvert pipes vertically into the 

soil (Figure 2.1 - circles marked with letters). 

Soil profiles in each culvert pipe hole were 

photographed and notes on soil stratification 

were taken before installing each culvert pipe. 

Each culvert pipe was instrumented with 6 

tensiometers and 6 solution samplers to measure the soil water pressure and to extract soil 

solution with depth. Tensiometers and solution samplers (90 cm in length) were installed 

radially from the culvert pipe into the undisturbed soil profile (parallel to the soil surface). 

A staggered configuration of the sensors in 

the culvert pipe was selected to minimize the 

potential for preferential flow and interference 

from other sensors, and to maximize the area 

of the profile that was sampled (Figure 2.1). 

The arc in Figure 2.1 represents the area of 

water potential and soil solution sampling. 

Pressure transducers (MPX2100DP, Motorola 

LTD., Denver, Colorado) and a data logger 

(CR7, Campbell Scientific, Inc., Logan, Utah) 

recorded the tensiometer readings every 

15 min. Five neutron access tubes (220 cm 

long) were installed vertically on each plot 

(Figure 2.1 - circle marked with Roman 

numerals). The water content with depth at 

desired times was determined using a neutron probe (503-DRHYDROPROBE®, CPN, 

Martinez, CA) and an established calibration 

curve in this soil profile.

To minimize the sampling time of the solution samplers, it was necessary to increase 

the water flux through the soil solution sampler. This was accomplished by replacing the 

traditional ceramic cup with filter paper (MF0.45 μm, Millipore, Billerica, MA) that had a 

high saturated conductivity (60 cm·h-1) and a 

small thickness (180 μm). 

The instantaneous profile method (Watson, 

1966) was used to evaluate the hydraulic conductivity of the upper 60 cm of the profile at 

the field site. This method uses simultaneous 

measurements of the volumetric water content 

(q) and soil water pressure head (h) in a soil 

profile during the course of drainage to determine the hydraulic conductivity. 
18

An additional set of q. and h data was measured near saturation (h= 0 to -30 cm) during 

redistribution of water immediately after 

ponded infiltration ceased (Hillel, 1998). In 

this case the value of q was acquired using a 

time domain reflectometry (TDR) system (Trase 

system, Soilmoisture Equip. Corp., Santa 

Barbara, CA) as well as gravimetrically, and h

was monitored with a tensiometer (Tensimeter, 

Soil Measurement Systems, Tucson, AZ). 

A step pulse conservative tracer (Potassium 

Bromide, KBr) experiment was conducted 

under steady-state water flow on bare soil at 

the plot, after plowing the upper 20 cm of the 

soil. We employed a 0.2 x 0.2 m staggered 

grid drip irrigation system (Typhoon 630, 

Netafim, Fresno, CA) to uniformly apply small 

water and solute fluxes to the soil surface that 

induced unsaturated flow conditions in the 

upper 120 cm of the soil profile. The surface 

boundary condition was a steady infiltration rate of 0.275±0.025 cm.

h-1. The soil 

surface was covered with a black nylon tarp 

to avoid water evaporation during the tracer 

experiment. 

When a steady-state water flow condition was 

established in the soil profile after 100 h, 

the water application system was switched 

between well water to a step pulse of KBr 

solution for 47 h and then switched back to 

well water for an additional 280 h. The KBr 

solution had 2.4 g·L-1 Br, and was mixed well 

before and during application. Soil solution 

samples were collected over an interval of 

24 h and analyzed for Br concentration with 

a Colorimetric system (O.I. Analytical, Flow 

Solution IV, College Station, Texas). The Br 

data from the field is presented at the median 

time of the sampling interval. 

Additional information was collected to evaluate the transverse dispersivity in the root zone 

(0 to -60 cm) and the effect of non-uniform 

upper boundary condition on the soil water 

and bromide application uniformity. These 

experiments are described in detail in Segal et 

al. (2009). 

Undisturbed cores from the experimental site 

were collected for measurements of bulk density (rb

), hydraulic properties, and conservative 

solute transport. Details on these experiments 

and results are provided in Segal et al. (2008 

and 2009). 

The HYDRUS-2D code (Šimůnek et al., 1999) 

was used to simulate the water flow in the 

upper profile during steady-state infiltration 

and drainage experiments. Other simulations 

were conducted to determine the lateral water 

flux that may occur directly above or through 

the sand layer relative to the vertical surface 

water flux. The two-dimensional water flow 

and bromide transport at the field site during 

the tracer experiment under a non-uniform top 

boundary condition (drip) and when considering a heterogeneous distribution of hydraulic 

properties in the lower profile (depths <-80 

cm) was also simulated using this model. 

Details on the simulation conditions and 

parameters are in Segal et al. (2008, 2009). 

Results and Discussion

Soft Data

In this section we discuss the use of soft data 

to select a location for flow and transport studies and to develop a conceptual model of the 

soil hydraulic properties at this field site.

Electromagnetic induction technology was 

employed to quantify the field spatial variability of ECa

. Figure 2.2 shows a map of the 

vertical component of ECa. The experimental 

study site was selected from this soft ECa data 

to be located in a “relatively uniform” section 

of the field with low ECa values (coarser textured material) as indicated in Figure 2.2. 

Our initial conceptual model of the soil profile stratigraphy at the experimental site was 

developed from photographic information 

(Segal et al., 2008). The particle size distribution (PSD) and bulk density data (Segal et 

al., 2008) were used in conjunction with the 

ROSETTA program to estimate values of saturated hydraulic conductivity (Ks

) at sampled 
19

locations. The nearest neighbor algorithm was 

then used to interpolate values of Ks at other 

locations that were not sampled. Figure 2.3 

presents the estimated values of Ks on northsouth and east-west transects of the plot. The 

dark uniform layer between the soil surface 

and -70 cm was the cultivated layer. The lightcolored thin layer underneath the cultivated 

layer was the sandy layer (-70 to -80 cm). 

The lower soil profile (-80 cm to -210 cm) 

was scattered with multiple lenses of different 

texture. The data can be further analyzed in 

terms of one-dimensional vertical conductivity 

(Segal et al., 2008). 

Hard Data

The soft data discussed above provided valuable information that was used to guide our 

strategy for collecting additional hard data 

in the field and the laboratory. This hard 

data was used to further refine our conceptual model and to improve our quantification 

of key soil hydraulic properties at the field 

site. Furthermore, comparison of field and 

laboratory data was used to identify potential 

limitations of the various types of hard data, 

and to assess the predicted hydraulic properties that were estimated from the soft data. 

Undisturbed cores were collected for hydraulic property analysis at several locations in 

the root zone (0 to -70 cm), at the capillary 

barrier (sand layer), and in the lower profile 

(below -80 cm). These sampling locations 

were chosen to improve our understanding 

of water flow in areas of the site where distinct differences in estimated soil hydraulic 

properties were identified (soft data shown 

in Figure 2.3). The fitted hydraulic model 

parameters to the measured soil core data are 

provided in Table 2.1. 

Field data collected during steady-state infiltration and redistribution were analyzed using 

the instantaneous profile method to determine 

hydraulic properties at depths of -30 and 

-60 cm. Analysis of the entire profile with this 

method was not possible due to the layering at 

the site, which violated an assumption of this 

approach (no lateral flow). 

Figure 2.2. A 4 ha map of the vertical component of the apparent soil electrical conductivity in the field as 

measured using a mobile remote electromagnetic induction sensor system on a 5x5 m grid.
20

Fitted field-scale hydraulic properties parameters also are presented in Table 2.1. Field 

and laboratory values of h(Se) matched reasonably well. In contrast, field and laboratory 

values of h showed considerable dissimilarity 

and revealed limitations of using only lab 

information to described field-scale hydraulic 

properties. A potential explanation for this 

difference could be entrapped air in the field.

Figure 2.3. North-south and east-west transects of estimated values of Ks on plot 1. These transects were 

generated from a three dimensional map of Ks that was estimated using particle size distribution and bulk 

density data from locations I-V using ROSETTA, and the nearest neighbor interpolation algorithm. Circles 

represent the undisturbed core sampling locations.

Table 2.1. The hydraulic properties (qs is the saturated water content; qr is the residual water content; a is 

the reciprocal of the air entry pressure; and n is the pore size distribution parameter of the van Genuchten 

model) of the three major layers in the upper soil profile of the field plot. Soft data was estimated from particle size distribution and bulk density data using the ROSETTA program. Hard data was measured on undisturbed core samples using the multistep outflow, pressure plate, and constant head permeameter techniques 

or from TDR readings in the field site. 

Hydraulic 

property

Layer 1

Sandy loam 

0 to -70cm

Layer 2

Sand

-70 to -80cm

Layer 3

Silt loam

-80 to -90cm

Soft data Hard data Soft data Hard data Soft data Hard data

Ks (cm.h-1) 1.95 5 9.25 71.2 1.4 1.6

qs 0.37 0.43 0.35 0.33 0.41 0.45

qr 0.033 0.03 0.04 0.01 0.056 0.04

a (cm-1) 0.018 0.0085 0.042 0.016 0.0047 0.007

n 1.45 1.6 2.41 1.96 1.70 1.57
21

Validation and Simulations

In this section we compare soft and hard 

estimates of hydraulic properties from several 

locations in the field. Numerical simulations 

were subsequently conducted to independently “validate” our field scale hydraulic 

properties where possible, and to further 

improve our understanding of water flow 

at specific locations in the field. Moreover, 

the reliability of using soft and hard data 

as input for simulations of water flow was 

demonstrated. 

Table 2.1 presents a comparison between the 

hydraulic properties generated from soft and 

hard data. This information was obtained for 

three layers in the upper soil profile of the 

field site (0 to -70 cm; -70 to -80 cm; and 

-80 to -90 cm). In general, soft and hard 

estimates of the hydraulic properties for the 

silt loam layer were very consistent. In contrast, soft estimates of the hydraulic properties 

were poorer for the coarser textured sandy 

loam layer and especially the sand layer. In 

general, the soft data provides a reasonable 

prediction of soil textural characteristics, but 

that the specific magnitudes of the hydraulic 

properties are only approximately predicted. 

Hence, soft data may likely be used to infer 

trends in spatial variability in soil textural 

properties, but not to deterministically simulate water flow at the site. Observed and 

simulated steady-state water infiltration and 

redistribution in the relatively uniform upper 

soil profile (0 to -70 cm) support this statement (Segal et al., 2008). In this case, 

simulations which employed measured 

hydraulic properties as model input provided 

a better agreement of observed water flow and 

drainage. Nevertheless, simulations which 

employed the soft-data-derived hydraulic 

properties did capture the general water flow 

behavior and trends. Numerical modeling in 

conjunction with measured hydraulic properties also proved to be an effective tool to gain 

additional insight on field scale water flow. 

For example, we used numerical simulations 

to quantify the influence of water application 

rate on the lateral water flux at a location of 

pronounced soil textural discontinuity in the 

field at a depth of -70 to -80 cm (Segal et al., 

2008). 

Bromide Travel Times

The mean and standard deviation of the bromide travel time for each BTC is presented in 

Table 2.2, as well as average and variance of 

these values at each depth. Minor changes 

in the variance of the mean travel time were 

measured in the upper profile (-32 and 

-61 cm), due to high uniformity in soil texture 

and water content (Segal et al. 2008). In 

contrast, the variance significantly increased 

immediately below the sand layer (-95 cm). 

This high variability in travel time can be 

attributed to three reasons, namely: (i) the 

sand layer was associated with a lower water 

content and consequently a higher pore water 

velocity; (ii) low permeability lenses just below 

the sand layer caused lateral flow of Br; and 

(iii) variability in the soil permeability directly 

below the sand layer generated variability in 

water and bromide mass inputs. 

The average standard deviation in travel time 

(Table 2.2) tended to increase with depth as 

expected due to dispersion. However, similar 

values occurred at -95 and -126 cm, likely 

due to greater variability in travel times at 

-95 cm. The ratio between the variance and 

the average values was much larger in the 

mean travel time than in the standard deviations of the travel times. This observation 

suggests that local scale effects on the BTCs 

were much less significant than plot scale 

factors. 

Averaging of Bromide Transport 

The influence of soil heterogeneity, water 

content, and location of the solution samplers 

on the tracer data may be overcome by averaging the 4 collected samples at the same 

depth and time on a plot, and then analyzing 

the depth averaged bromide transport behavior. Bromide transport in the root zone and in 

the lower profile will be separately discussed 

in detail below due to the previously noted 
22

differences in soil and water flow characteristics in these regions. 

Observed field measurements of the depth 

averaged bromide transport data is presented 

in Figure 2.4. The average relative bromide 

concentration at a given time is denoted 

using filled circles for the -32 cm depth and 

with empty circles for the -61 cm depth. 

Integration of the area under the breakthrough 

curve provided very good mass balance at -32 

and -61 cm (99.5-102%) which is presented 

in the figure. The vertical bars represent 

standard deviations and the horizontal bars 

represent the sampling time range. Standard 

deviations account for the inherent soil spatial variability, and the variable location of 

each solution sampler. The modeled breakthrough curves, based on the mobile-immobile 

(MIM) transport parameters evaluated in the 

laboratory, are also depicted in Figure 2.4. 

Additional information is provided in Segal 

et al. (2009). The modeled curves provide a 

reasonable prediction of the average measured 

value of bromide at -32 and -61 cm and total 

Br recovery. In general, the attempt to independently predict the bromide transport in 

the root zone by using only laboratory derived 

model parameters was fairly successful at 

this scale. In addition, both one- and twodimensional simulations (average value of a 

cross section at a specific depth and time) 

provided nearly an identical description of the 

data, indicating that the water flow and solute 

transport in the upper 60 cm of the soil profile 

could be described as a uniform one-dimensional process when using our drip irrigation 

setup and measured values of dispersivity. 

The water flow and solute transport pattern 

in the lower profile (-70 to -200 cm) is much 

more complex than in the root zone (0 to 

-70 cm). In particular, the coarse sand layer 

at about -70 cm generates high water velocities and a lateral component to water flow 

(Segal et al. 2008). The presence of interconnected layers and fine textured lenses of 

variable thickness below -80 cm produce a 

Table 2.2. Mean and standard deviation of bromide travel time (units in hours, h) at each sampling depth 

and culvert pipe location, and associated average and variance values for each depth.

Depth -32 cm -61 cm -95 cm -126 cm -155 cm

Location Estimated mean bromide travel time (h)

A 82 120 280 190 250

B 64 110 160 265 250

C 75 130 200 210 300

D NA 110 145 250 270

Average mean travel time (h) 73.6 117.5 196.2 228.7 267.5

Variance of mean travel time (h2) 82.3 91.6 3656.2 1206.2 558.3

Depth -32 cm -61 cm -95 cm -126 cm -155 cm

Location Estimated standard deviation of 

bromide travel time (h)

A 52 50 69 60 80

B 37 46 61 62 63

C 38 56 66 68 79

D NA 46 62 66 76

Average standard deviation of travel time (h) 42 50 65 64 75

Variance of standard deviation of travel time (h2) 70 22 13 13 62
23

location specific pattern of solute transport 

through the lower soil profile of the field plot. 

Two-dimensional solute transport simulations 

were conducted to study the relative importance of nonuniform boundary conditions and 

soil heterogeneity on solute transport in the 

lower profile. Results indicate that soil heterogeneity is the major contributor to solute 

transport behavior in the lower soil profile. 

Specifically, variations in bromide travel time 

and total mass suggested that the sand layer 

in conjunction with fine textured lenses slow 

down the bromide propagation and generates 

bypassing of water and solute through lower 

resistance pathways. Yet, the lower water 

velocities below this region (due to the raised 

water table) decrease the variability of the 

mean travel time (Table 2.2) and generate a 

more uniform Br front. This finding coupled 

with the fact that the exact soil type that is 

associated with a particular soil solution sampler is unknown, hampers the application of a 

completely deterministic approach for describing solute transport in the lower profile. 

Considering the above information, the 

bromide transport data in the lower profile 

is presented below in terms of the onedimensional average concentration (4 sample 

locations) at a specific depth as a function 

of time. Figure 2.5 presents measured and 

simulated bromide breakthrough curves in the 

lower profile at -95, -126, -155 cm and the Br 

mass recovery under the BTCs. The horizontal 

bars are standard deviations of the concentration measurements. It should be mentioned 

that the one-dimensional modeling data that 

are presented in this figure were generated 

from a two-dimensional simulation that was 

averaged over a specific depth at a given time. 

A transect of spatially heterogeneous hydraulic 

properties was generated from measurements 

taken from the field plot that was used as 

input for this simulation (Figure 2.3). Good 

Figure 2.4. Field measurements of the relative bromide concentration over time for the bromide tracer 

experiment. Data is presented at two depths in the field plot, -32 and -61 cm (circles). Each data point is 

an average of the four locations at the same time and depth. The vertical bars are standard deviations and 

the horizontal bars represent the sampling time range. The corresponding 1D and 2D (depth averaged) MIM 

simulation results are also presented in this figure based on transport parameters derived from the undisturbed core. 
24

agreement between measured and modeled 

data was found (Correlation coefficient above 

84%). In general, travel time and mass balance of the average bromide concentration 

over depth is conserved (>88.4% bromide 

mass recovery). However, the observed and 

simulated standard deviations have the same 

order of magnitude as the average value, suggesting high variability of bromide at a specific 

depth and time. 

Figure 2.5. Field measurements (circle) and 

2D-MIM simulation (lines and triangle) of relative bromide concentration over time at -95, -126 

and -155 cm. Measured data is an average value 

from four sampling locations at the same time and 

depth. Simulated data is presented as an average 

value at these same depths. The horizontal bars are 

standard deviations. 
25

3.0

NMP Results - Nutrients

Background

NMPs are designed to meet the water and 

nutrient needs of crops, while minimizing the 

loss of nutrients to surface water and groundwater (USEPA, 2003). However, researchers 

with the USEPA have observed significant 

migration of pollutants (e.g., nitrate) towards 

surface water and groundwater bodies at NMP 

sites (Hutchins and Bradford, 2005 - personal communication). These observations 

suggest that implementing a NMP based on 

current agronomic practices may not always 

protect the environment. The literature review 

(Chapter 1) indicates that potential problems 

with NMP implementation include: (i) inaccurate quantification of water and nutrient 

mass balances due to inadequate information 

on soil properties, climatic data, wastewater constituents or crop water and nutrient 

uptake rates; (ii) inherent spatial and temporal variability in soil properties, manure and 

nutrient distribution, irrigation uniformity, 

and crop growth; and (iii) NMP management 

constraints, such as quantifying the amount 

of water and wastewater application and 

managing the timing of the application. The 

objective of this study was to measure the 

fate of nitrogen, phosphorus, potassium and 

salts from land application of dairy wastewater 

under a well-designed and implemented NMP 

in a semi-arid environment. We also present key management practices that minimize 

the potential leaching of nutrients and salts 

toward groundwater.

Materials and Methods

Traditional NMP studies have been conducted 

from an agronomic perspective to determine 

the impact of specific NMP factors on a given 

field. The statistical design for such experiments include blocks and random repetitions 

because the spatial variability of the field and 

application systems is typically unknown. In 

contrast, our experiment was a process-based 

(mechanistic) study of flow and transport 

processes under NMP conditions. Accurate 

implementation of a process-based NMP 

requires the determination of water, salt and 

nutrient mass balances in the root zone, and 

the ability to quantify flow and transport processes. To collect this type of information 

requires the use of measurement tools such as 

weighing lysimeters, weather station, tensiometers and solution samplers with depth, and 

irrigation systems with a high level of uniformity and precision. The traditional agronomic 

design cannot achieve such high precision in 

measuring and implementing a NMP on each 

repetition due to economic constraints, and 

this further increases the variability of traditional NMP studies. Below we highlight the 

design of our process-based NMP that was 

needed to overcome the limitations of the 

traditional agronomic approach. 

Field

Our field site was located in San Jacinto, CA 

(33°50′22 ̋ North, 117°00′46 ̋ West) and was 

chosen to be in a relatively homogenous part 

of the field in order to minimize variability in 

soil hydraulic properties. This objective was 

achieved by acquiring preliminary information on the field soil spatial variability using 

a remote electromagnetic induction system 

(Segal et al., 2008). This procedure eliminated the need of repetitions scattered across 

the field in traditional agronomic NMP studies, and allowed us to focus on within plot 

variability. The experiment site was also 

chosen to have no recent history of CAFO 

wastewater application. The field was cultivated only during winter (triticale, TRICAL®

Resource Seeds, Inc.) and manure was 

applied twice (the last manure application was 

three years prior to this experiment) during the 

last ten years prior to this experiment. 
26

The experimental site consists of two 

6 m × 6 m plots (Figure 3.1, more detailed 

description of the site is given in Segal et al., 

2008 and 2009). Within plot variability was 

overcome by taking multiple measurements 

over depth at several locations within the 

plots. Briefly, one culvert pipe was installed at 

each corner of both plots (8 pipes total). Each 

pipe was instrumented with 6 tensiometers 

and 6 soil solution samplers installed over 

depth at approximately 30 cm intervals, which 

were installed 90 cm horizontally from the culvert pipe into the undisturbed soil profile. The 

staggered configuration of the sensors (represented by the arc in Figure 3.1), was selected 

to maximize the area of the profile that was 

sampled. Each plot was also equipped with 

five neutron probe (503-DRHYDROPROBE®, 

CPN, Martinez, CA) access tubes for measuring the water content with depth.

An intensive study on water flow and soil 

hydraulic properties was conducted on the 

two plots at our experimental site (Segal et 

al., 2008) and no significant difference was 

found between the plots. The soil texture of 

the root zone (0-65 cm) was a sandy loam 

(Grangeville fine sandy loam, a coarse-loamy, 

mixed, superactive, thermic fluvaquentic 

haploxeroll) with average contents of 55% 

sand, 40% silt and 5% clay. The average bulk 

density was 1.35 g·cm-3 and the saturated and 

residual water contents were 0.43 and 0.03, 

respectively. The average saturated hydraulic 

conductivity was 2.35 cm·h-1, the longitudinal 

dispersivity was 0.56 cm, the immobile water 

content was 0.0978, and the mass transfer 

coefficient between mobile and immobile 

regions was 0.0035 h-1 (Segal et al., 2008 

and 2009).

Figure 3.1. Schematic of the field site. Squares represent two six-by-six meter plots. Circles with letters 

(A-H) are 220 cm in length vertical culvert pipes installed with tensiometers and solution samplers. Arcs 

represent the area of water potential sampling. The controlled mixing and application system is illustrated at 

the top of the figure.
27

Raw DWW was treated prior to land application with a stationary inclined screen separator 

(Zhang and Westerman, 1997), a sedimentation tank (Sukias et al., 2001), and a sand 

filter (Rodgers et al., 2005) packed with 

crushed silica (0.45-0.5 mm in diameter, 

AGF, Netafim, Fresno, CA). In contrast to traditional agronomic designs, our process-based 

NMP minimized variability through uniform 

application of water and nutrient with a high 

level of precision. Specifically, well water and/

or DWW were uniformly applied to each plot 

using a pump and nine emitters (R184CT, 

Raindrip, Fresno, CA) in 3 m spacing. The 

water application rates varied between 0.6 to 

0.95 cm·h-1 (25 to 40% of the soil saturated 

hydraulic conductivity) with a Christiansen 

uniformity coefficient of 94% under low wind 

conditions. The water and wastewater application system consisted of a mixing tank, a 

controller, solenoid valves, pumps and two 

electrical water meters with a resolution of 

3.78 L ± 1.5% (JSJ075, Carlon meter, Grand 

Haven, MI) to achieve desired blending ratios 

for treated DWW and well water (Figure 3.1). 

Dairy wastewater was applied using cyclic 

and blending strategies. The blending strategy employed a selected mixture of treated 

DWW and well water to meet the needs of the 

crop for N and water. Conversely, the cyclic 

strategy applied separate irrigations of treated 

DWW and well water to meet the crop needs 

for N and water. 

A NMP was implemented on winter and 

summer crops during 2007, and a perennial 

crop in 2008. Triticale (TRICAL® Resource 

Seeds, Inc.) served as the 2007 winter crop 

and was planted on February 10th, seedlings 

emerged and established a full stand on 

February 25th, and the crop was harvested 

on May 8th. The 2007 summer crop was 

NK 300 hybrid forage sorghum (Syngenta 

Global) and was planted on May 30th, seedlings emerged and established a full stand 

on June 5th, and the crop was harvested on 

August 13th. The 2008 perennial crop was 

alfalfa (Grandslam cv. Western Farm Service, 

CA). Alfalfa is an important crop for the dairy 

industry due to its high yield, feeding value 

and efficiency in N removal. Alfalfa has three 

major differences relative to the crops used 

in 2007, namely: i) it is a perennial crop with 

multi-cuts; ii) it has a deeper root system; 

and iii) it may assimilate N from the atmosphere through nodules (symbiotic nitrogen 

fixation). Alfalfa was planted on the blending 

field plot at agronomical rate of 2.75 Kg·ha-1

on April 1st, emerged on April 10th, and 

established full cover 30 days later. Five 

consecutive growing cycles (five cuttings with 

harvest interval between 37 to 45 days) were 

achieved during 2008. The NMP was implemented by dividing the growing season into 

multi-cut segments that represent the growing 

cycle. Each growing cycle was considered a 

separate period with a new initial condition. 

The first growing cycle started on May 10th

and lasted until June 17th. However, extensive weed growth interfered with the normal 

development of the alfalfa during this period. 

Therefore, the plot was treated with herbicide 

(Pursuit, BASF, NC) at the rate of 150 ml·ha-1

on June 25th. A fallow season (time between 

harvest and planting that is associated with 

minimal ET and nutrient uptake) occurred 

between successive growing seasons for the 

crops described above. 

Blumenthal and Russelle (1996) reported that 

atmospheric N fixation through nodules will be 

less active during periods of high NI

soil . The 

alfalfa NMP therefore attempted to maintain 

high N concentrations in the root zone during 

the growing season in order to minimize the 

amount of N fixation and to maximize the 

amount of DWW addition. No DWW was 

applied between the last two alfalfa harvests 

in order to deplete the soil profile of plant 

available N. 

Water and Nitrogen Mass Balances in the Root 

Zone 

Plot scale water balance information in the 

root zone over a given time interval was used 

to determine the amount of applied irrigation 
28

water, I (ML-3DT-1) to meet crop ET (ML-3DT-1) 

at the end of this interval as:

w I ET D W P = + + ∆ − [3.1]

where D (ML-3DT-1) is water loss due to drainage, Pw (ML-3DT-1) is the water input due to 

precipitation, and DW (ML-3DT-1) is the change 

in soil water storage (final – initial). 

Water balance parameters in Equation [3.1] 

were measured as described below. Potential 

ET (ET

p

), with a resolution of 0.1 mm, was 

estimated using data from a weather station 

(Penman, 1948) located between the plots. 

Temperature, relative humidity, solar radiation, wind speed and rain were recorded every 

15 min. Actual ET (ETactual) was estimated 

from weighing lysimeters, 20 cm in diameter 

and 100 cm length, installed at the perimeter 

of each plot (Figure 3.1). The top of the lysimeter was at the soil surface connected to a 

load cell that measured the total weight continuously (resolution of 50 g). A suction cup 

(filter paper; MF-0.45 μm, Millipore, Billerica, 

MA) was connected to the bottom of the lysimeter, where vacuum was applied to collect the 

drainage. The crop coefficient (Kc) was calculated as the ratio of ETactual to ET

p

 during a 

given time period. The value of Pw (resolution 

of 0.25 mm) was measured using a rain gauge 

(CS700, Campbell Scientific, Logan, Utah). 

The values of D and DW were determined from 

neutron probe and tensiometer readings in the 

root zone and measured soil hydraulic properties. The value of I was verified from flow 

meter readings.

In this study the following inorganic and 

organic N mass balance equations for the root 

zone were employed: 

I I I I I N E N N N N application OI plant drainage atmosphere soil + = + + + ∆

[3.2]

O O N N E application soil OI = ∆ + [3.3]

where NI

application is the inorganic N applied to 

the soil surface (ML-3DT-1), EOI (ML-3DT-1) is 

the amount of N converted from/to organic 

to/from inorganic forms, NI

plant (ML-3DT-1) is 

the inorganic N uptake by the plant, NI

drainage 

(ML-3DT-1) is the inorganic N drained below 

the root zone, NI

atmosphere (ML-3DT-1) is the 

inorganic N lost to the atmosphere, DNI

soil is 

the difference in inorganic N storage in the 

root zone (final-initial), NO

application (ML-3DT-1) is 

the organic N applied to the soil surface and 

DNO

soil (ML-3DT-1) is the difference in organic 

N storage in the root zone (final-initial). The 

total N mass balance is equal to the sum of 

Equations [3.2] and [3.3]. Equation [3.3] 

assumes that losses of organic N are only due 

to mineralization (volatilization and drainage 

of organic N are assumed to be negligible). 

The N mass balance was calculated over the 

upper 30 cm for the triticale, 60 cm for the 

sorghum and 90 cm for the alfalfa, where 

roots are most active in water and nutrient 

uptake under irrigated conditions (Kätterer 

et al., 1993; Merrill and Rawlins, 1979; 

Abdul-Jabbar et al., 1982). Nitrogen balance parameters were quantified as described 

below. Total N and C in the solid phase of 

the DWW, soil, and plant tissues were measured using the combustion method (Flash 

EA 1112, Thermo-Finnigan, Waltham, MA). 

Measurement of nitrogen from ammonium 

(N-NH4) (Keeney and Nelson, 1982) and 

nitrogen from combined nitrite and nitrate 

(N-(NO2+NO3)) concentrations in soil solution 

and DWW were performed using a colorimetric system (O.I. Analytical, Flow Solution IV, 

College Station, Texas) after filtering the 

sample through a 0.22 μm filter. Values of 

NI

application and NOapplication were directly measured in the DWW before each application. 

The value of DNI

soil was determined from 

sequential measurements of soil inorganic N 

concentrations in the root zone before DWW 

application events. The value of NI

drainage was 

determined from measured inorganic N concentrations in soil solution below the root zone 

and from information about the water fluxes. 

NI

atmosphere accounts for volatilization of ammonia (NH3) during application and from the 
29

soil surface. We assumed denitrification was 

negligible under the low nitrate and unsaturated conditions of the root zone (Luo et al., 

1999). The loss of NH3 during irrigation was 

measured using the concentration ratio of 

N-NH4 in the irrigation water at the emitter 

outlet and at the soil surface. Volatilization 

of NH3 from the soil surface was measured 

following DWW wastewater application for a 

period of one week during the 2007 winter 

crop season using a standard chamber and 

acid-trap technique to capture NH3 emissions 

(Black et al. 1985). The loss of N-NH4 during 

irrigation and the ET

p

 are presented for two 

application events in Table 3.1. The N-NH4

losses during irrigation varied between 9 and 

32%, and higher rates were associated with 

higher N-NH4 concentrations in the irrigation 

water and higher ET

p

. The atmospheric loss 

of N-NH4 during irrigation was measured and 

taken into account in the N balance before 

each application. The loss of N-NH4 from 

the soil after irrigation was measured to be 

three orders of magnitude smaller than the 

N-NH4 loss to the atmosphere during irrigation (Table 3.1). These findings are consistent 

with other data presented in the literature 

(Cameron et al., 1995; Sharpe and Harper, 

1997; Cameron et al., 2002; Hawke and 

Summers, 2006). 

NI

plant was determined from measurements of 

dry phytomass and its N content. Before each 

water application, a 1 m long row of plants of 

the triticale and sorghum (0.2 to 0.4 m2) or 

0.3 by 0.3 m of the alfalfa was collected for 

N analysis from the middle of the plot, where 

minimal effects from the measuring devices 

are expected. Since the root system was not 

removed during the harvesting, it was not considered as an N sink.

For triticale and sorghum during 2007 the 

value of EOI accounts for the net exchange 

due to mineralization. In contrast, for alfalfa 

(a legume) during 2008 the value of EOI

also accounts for N fixation from the atmosphere. The value of EOI was determined from 

Equation [3.2], while all other parameters 

were measured, and this information was used 

in conjunction with Equation [3.3] to determine the changes in NO

soil using measured 

average values of the initial NO

soil at the field 

site. The exchange rate was subsequently 

calculated as EOI divided by the initial NO

soil for 

a given time period. Final total N (dominated 

by NO

soil ) and its spatial variability in the plots 

were measured on March, 2008. Ten soil 

cores, 30 cm long by 1.25 cm in diameter, 

were sampled from each plot in random locations. Three samples were taken from each 

Table 3.1. Ammonia volatilization from the sprinkler irrigation system and soil surface.

N-NH3 volatilization from sprinkler irrigation system

Late 

winter 

DAE

Application 

Strategy

N-NH4 in Irrigation water

mg·L-1

Volatilization Loss

% 

ETp 

mm·h-1

35 Blending 11.1 15 0.446

35 Cyclic 39.45 9 0.142

57 Blending 92.57 32 0.68

57 Cyclic 157 22 0.24

N-NH3 volatilization from soil surface

Late 

winter

DAE

Application 

Strategy

N-NH4 in Irrigation water

mg·m-2

N-NH3 volatilization

mg·m-2

Volatilization Loss

%

29-36 Cyclic 2407.82 1.614 6.7·10-6
30

core at depths of 5, 15 and 25 cm (total of 

30 samples, 10 at each of these depths). Due 

to the small soil volume used in the combustion method, each sample was divided into 

three subsamples (total of 90 subsamples) 

that were analyzed for their total N and C 

content.

In practice, NI

application was calculated from 

Equation [3.2] to meet the projected NI

plant 

and EOI during the subsequent time interval. The projected plant uptake for each 

time interval was determined from potential 

N uptake curves for crops under optimum 

growth conditions (Bélanger and Richards, 

2000;Gibson et al., 2007; Rahman et al., 

2001), and the projected mineralization rate 

was estimated from literature values (Stenger 

et al., 2001) or from the previous time step. 

The blending ratio before each application 

was determined by matching simultaneously 

I

application and NIapplication , where Iapplication=IDWW + 

I

well and NIapplication = NIwell Iwell + NIDWW IDWW and 

the subscripts DWW and well denote the water 

source. 

Suspended sediments concentration (SSC) 

of the DWW were measured by centrifuging a known volume at 2040 times gravity 

for 20 min, decanting the liquid phase and 

measuring the remaining solid after drying at 

60°C for 48 h (ASTM D 3977-97 - Method A). 

The TDS was assumed to be correlated to the 

EC (1 dS·m-1 = 10 meq·L-1) and was measured with an EC meter (M33.1, Agricultural 

Electronics, Montclair, CA). The concentrations of plant available K (Olsen bicarbonate 

method) and P (ammonium acetate method) 

in the soil profile were determined at the 

beginning and the end of the 2007 growing 

season, whereas salt concentrations (EC and 

TDS) in the root zone were measured before 

each irrigation event.

The T-Statistic (T-Test) was used to evaluate 

significant differences (P < 0.05) between 

cyclic and blending application strategies 

during 2007 (Sigmaplot 11, Systat Software 

Inc., CA).

Results and Discussion

Results from triticale (winter 2007) and sorghum (summer 2007) data are discussed 

below in sections entitled Management 

Considerations for Salinity, Management 

Considerations for Organic Nitrogen, and Plant 

Available N, P and K. These two cereal crops 

do not form nodules to fix atmospheric N, and 

EOI therefore reflects exchange due to mineralization. In contrast, alfalfa (summer 2008) 

is a legume that forms nodules and may fix 

atmospheric N. The additional N source from 

fixation poses additional challenges for efficient NMP implementation that is discussed 

below in a separate section entitled Nitrogen 

Fixation – Alfalfa 2008.

Management Considerations for Salinity 

Water balance information for 2007 winter (A) 

and summer (B) growing seasons are presented in Table 3.2. Rainfall occurred during 

winter (total of 28.75 mm) but was absent 

during summer. The value of ET

p

 was lower 

during winter than during summer. The final 

water application amounts were adjusted to 

include a leaching factor of 0.2 for the first 

30 days and 0.1 for later times in order to 

leach excess salts from the root zone and 

to minimize downward migration of NO3

-

. A 

system malfunction, however, delivered an 

extra 55.4 mm of well water to the cyclic plot 

on day 58 of the summer growing season. The 

total drainage flux (average of the blending 

and cyclic strategies) below the root zone was 

23.9 mm and 56.4 mm throughout the winter 

and summer growing seasons, respectively. 

Figure 3.2 presents the absolute value of soil 

water pressure head (|h|) in the soil profile 

as a function of day after emergence (DAE) 

for the blending and cyclic strategies during 

the 2007 summer growing season. Changes 

in |h| were restricted only to the upper 60 cm 

of the soil profile due to the accurate water 

mass balance on both plots. In general, |h|

followed the water application events: decreasing after irrigation (soil becomes wetter) and 

increasing with time between irrigations (soil 
31

becomes drier). Figure 3.2 shows that aerobic 

conditions were maintained during the majority of the season. The soil water pressure head 

below the root zone (i.e. <-90 cm) was generally steady throughout the growing season. 

The system malfunction on the cyclic plot 

at day 58, however, caused a decrease in |h| 

below the root zone (<-60 cm). 

The increase in the EC of soil solution (ECw) 

of the root zone (-30 and -60 cm) over time 

due to the use of irrigation water with high 

TDS, a low leaching factor, and concentration of salts by ET is presented in Figure 3.3. 

The ECw of the root zone increased from 

1 to 3 dS·m-1 for the blending strategy and 

from 1 to 2.5 dS·m-1 for the cyclic strategy (no 

significant difference in the level of P < 0.05 

were found between the final values of ECw of 

each treatment). The extra water application 

on the cyclic plot produced greater leaching 

and hence a lower final value of ECw. Only 

minor changes in the electrical conductivity of 

the soil solution (ECw) were detected below the 

root zone during the growing seasons (data is 

not presented), due to the low leaching factor 

Table 3.2. Potential and actual evapotranspiration (ET), crop coefficient, rainfall and water application 

during the growing season of triticale during winter 2007 (A) and sorghum during summer 2007 (B), DAE is 

day after emergence.

A

DAE

potential ET 

mm

Crop coefficient and 

leaching factor†

Actual ET

mm

Rainfall

mm

Water application 

mm

15-29 100.2 0.5 50.1 7.25 42.9

30-36 30.1 0.5 15.05 0.00 15.0

37-50 84.4 0.9 75.96 4.25 71.7

51-58 36.7 1.1 40.37 4.00 36.3

59-65 39.8 1.2 47.76 13.25 34.6

66-72 40.1 1.2 48.12 0.00 48.0

B

DAE

potential ET 

mm

Crop coefficient and 

leaching factor†

Actual ET

mm

Rainfall

mm

Water application 

mm

5-28 258.6 0.5 129.3 0 135.1

29-35 86.7 0.75 65.02 0 62.4

36-44 94.0 0.9 84.6 0 90.5

45-49 47.1 1.0 47.1 0 48.5

50-58 86.0 1.0 86.0 0 86.6

59-62 33.5 1.05 35.17 0 35.3

63-70 59.8 1.1 65.78 0 66.6

 †Crop coefficient was measured based on water mass balance in the root zone by using weighing lysimeters. The 

leaching factor was 0.2 during the first 30 days and 0.1 through the rest of the growing season. 

Figure 3.2. The absolute value of the soil water 

pressure head (|h|) in the soil profile as a function 

of day after emergence (DAE) for the cyclic and 

blending water application strategies during the 

sorghum 2007 growing season.
32

that was implemented at these sites. The 

measured values represent the salt load under 

a conservative NMP approach that applied 

only a fraction of the total N that was required 

by the plant with DWW. If 100% of the plant 

N had been applied by DWW, then the accompanying salts would increase the ECw in the 

root zone to higher levels. High salt levels 

in the root zone may restrict plant growth, 

and accordingly water and nutrient uptake. 

If this reduction in ET is not considered at a 

NMP site, additional leaching and contaminant migration will occur. An optimum point 

likely exists between the benefits of nutrient 

application and the detrimental effects of 

salt accumulation on crop yield. This point 

is strongly dependent on the salt tolerance of 

the crop, suggesting that NMP should use only 

salt tolerant crops.

Minimizing the potential adverse effects of 

salts on plant growth is commonly achieved by 

leaching excess salts below the root zone. The 

timing of salt leaching may be a crucial management decision in NMPs because organic 

soil N continues to be converted to inorganic 

N forms (NH4

+, NO2-

 and NO3

- ) during the 

fallow season. A pre-irrigation at the beginning 

of a new growing season, or seasonal rains 

during the fallow season may result in migration of inorganic N, especially NO3

-

, below the 

root zone towards groundwater (Feng et al., 

2005; Woodard et al., 2002).

Figure 3.4 demonstrates this scenario by 

presenting the concentrations of N-(NO2+NO3) 

and N-NH4 in the soil profile at the end (finalafter harvesting) and beginning (initial- after 

seasonal rains and pre-irrigation) of consecutive growing seasons. The graphs of triticale 

2007/sorghum 2007 and the sorghum 2007/

alfalfa 2008 fallow seasons show that the soil 

profile at both strategies was depleted from 

inorganic N at the beginning of the fallow 

seasons. Conversely, high concentrations of 

N-(NO2+NO3) were found along the soil profiles at the end of the fallow seasons. No 

significant difference (P < 0.05) between strategies was found in the initial and final values 

of N-NH4 and N-(NO2+NO3) in the soil profile. 

A mass balance of the inorganic N in the profile revealed that 9.67 and 17.95 g of N·m-2

was mineralized for the blending strategy and 

3.40 and 19.5 g of N·m-2 for the cyclic strategy during the triticale 2007/sorghum 2007 

and the sorghum 2007/alfalfa 2008 fallow 

periods, respectively. These values are equivalent to mineralization rate of 2.37E-04 day-1 

and 4.25E-04 day-1 for the blending strategy 

and 2.93E-04 day-1 and 4.72E-04 day-1 for 

the cyclic strategy. Leaching excess salts is 

therefore recommended right after harvesting, 

Figure 3.3. Electrical conductivity of the soil solution (ECw ) over depth and total dissolved solids (TDS) 

load during 2007 for the blending and cyclic water application strategies.
33

Figure 3.4. Nitrogen from ammonium (N-NH4) and nitrogen from combined nitrite and nitrate 

(N- (NO2+NO3 )) concentration in the soil profile at the end (final) and beginning (initial) of two consecutive 

growing seasons (triticale 2007/sorghum 2007 and sorghum 2007/alfalfa 2008).

when the inorganic N, and especially NO3

-

levels are low in the root zone. 

Management Considerations for Organic 

Nitrogen 

The determination of NO

soil is hampered due to 

the inaccuracy of currently available methodologies (i.e. Kjeldahl and Combustion) and the 

inherent spatial variability of soils and water 

application systems (Strong et al., 1999; 

Stenger et al., 2001; Valenzuela-Solano and 

Crohn, 2006; Watts et al., 2007). Significant 

variability in NO

soil was observed at the beginning of the 2008 winter growing season. The 

average total N (dominated by organic N) was 

0.051% for the blending and 0.058% for the 

cyclic strategies, yet the values varied between 

0.03 to 0.12%. This corresponds to total N 

levels of 121 to 486 g of N·m-2 for the upper 

30 cm. The calculated coefficient of variance for the combustion method was 6.95% 

and from the spatial variability was 19.5%. 

Hence, when the NO

soil reservoir plays a 

dominant role in NMP management, the high 

uncertainty may lead to an inaccurate application of N. Furthermore, Figure 3.4 suggests 

that a large NO

soil reservoir can maximize the 

potential migration of NO3

-

 below the root zone 

at the end of the fallow season. 

Figure 3.5 shows the effect of the high 

uncertainty and amount of NO

soil on NMP 

implementation (in the absence of N fixation); 

by presenting three conceptual scenarios 

representing different ratios of inorganic to 

organic N sources in the root zone. The values 

of NO

soil were assumed to be 333, 1666, and 

3333 g of N·m-2 in the top, middle, and lower 
34

figures. Cumulative amounts of potential 

NI

plant , EOI, and the sum of EOI and NIapplication

as a function of day of the year (DOY) are 

presented for each NO

soil level. The year is 

divided into two growing seasons (winter 

and summer) and two fallow periods (fall 

and spring). For winter and summer crops 

the cumulative amounts of potential NI

plant 

were assumed to be 16 and 22 g of N·m-2

and mineralization rates were assumed to be 

2E-04 and 3E-04 day-1, respectively. The 

error bars reflect the assumed variance (40%) 

in the mineralization rate. Differences in the 

predicted cumulative amounts of EOI are due 

to differences in the initial organic reservoir. 

NI

application is determined from the difference 

between potential NI

plant and EOI, while considering the uncertainty. 

For the case of a low NO

soil reservoir, matching between potential NI

plant and NIapplication is 

practical with low deviations due to the minor 

amounts of NO

soil and NOapplication . This well 

controlled NMP condition is similar to fertigation. The second scenario, intermediate NO

soil

reservoir, is representative of our field study 

during 2007 and requires the consideration of 

EOI , NI

soil and NIapplication . This second scenario 

will under apply NI

plant when uncertainty in 

the mineralization rate is considered and the 

NO

soil reservoir will deplete over time and shift 

the system to the well controlled NMP condition shown in the upper graph. In the third 

scenario, the entire NI

plant is dependent on 

the NO

soil pool and mineralization rates. When 

considering the high uncertainty in EOI, significant under-application of N is likely with 

corresponding yield reductions. Consequently, 

the NO

soil reservoir will be depleted over time. 

If uncertainty in the mineralization rate is not 

considered with scenarios 2 and 3, then yield 

reduction can be minimized but the risk for N 

migration below the root zone toward groundwater increases.

Figure 3.5 indicates that the potential 

migration of N below the root zone can be 

minimized when NO

soil is low. The majority of 

the SSC from the DWW was therefore removed 

as part of the NMP implementation with an 

inclined screen separator, a sedimentation 

tank and a sand filter. In addition to this 

NMP consideration, lower SSC in the DWW 

Figure 3.5. Three conceptual approaches for 

nitrogen NMPs based on low (top figure-333 g 

of N·m-2), intermediate (middle figure-1666 g of 

N·m-2), and high (lower figure-3333 g of N·m-2) 

soil organic N reservoirs. Cumulative amounts of 

potential plant N uptake (16 and 22 g of N·m-2 for 

winter and summer crops), predicted mineralized 

N, and the sum of mineralized N and applied inorganic N are presented as a function of the day of 

the year (DOY). Mineralization rates were 2E-04 

and 3E-04 day-1 for winter and summer seasons. 

The error bars reflect the assumed variance (40%) 

in the mineralization rate.
35

allowed us to use a water application system 

with a high uniformity (micro-sprinkler). The 

inorganic and organic N species and their 

distribution in applied DWW during the 2007 

winter growing season (triticale) are presented 

in Figure 3.6. The organic N was relatively 

constant throughout the growing season (6.12-

7.78 mg L-1), and the inorganic N fraction 

increased from 90% to 97% of the total N. 

Figure 3.6 also indicates that N-NH4 was the 

dominant N form in the DWW (CampbellMathews et al., 2001; Cameron et al., 2002; 

Wang et al., 2004). Temporal variability 

in the fraction and amount of N-NH4 and 

N-(NO2+NO3) in the DWW is due to NH3

volatilization and nitrification during storage 

(Bussink and Oenema, 1998). The DWW 

treatment was found to also have an effect 

on many chemical properties (Table 3.3). A 

small reduction in EC was measured and was 

attributed to adsorption (Rodgers et al., 2005) 

and removal of suspended solids. 

Figure 3.6. The composition, distribution, and 

amount of organic and inorganic N species (mg L-1) 

in dairy wastewater during the triticale 2007 growing season.

Plant Available N, P, and K 

NMPs need to be designed to maintain soil 

fertility and to minimize the risk of N migration below the root zone. A conservative NMP 

approach was implemented at our field site 

for triticale and sorghum during 2007 in an 

attempt to transition the soil organic reservoir to an optimum value of NO

soil . During 

this transition phase, the NO

soil reservoir was 

depleted over time in order to minimize the 

migration of NO3

-

 below the root zone. Once 

an optimal value of NO

soil is achieved then it 

can be maintained by matching rates of dominant N sources (DWW addition) and sinks 

(plant uptake).

NMP results for triticale and sorghum during 

2007 are summarized in Figure 3.7. Here 

NI

soil , NOsoil and cumulative values of NIplant  , 

EOI, NI

drainage , NIapplication - NIatmosphere in the 

root zone are presented over time (no significant difference at P<0.05 level were found 

between cyclic and blending treatments). 

Initial low NI

soil and minor changes in NIdrainage 

over time were measured for both cyclic 

and blending strategies and crops. Average 

NI

application - NIatmosphere for the triticale and sorghum crops were 48% and 28% of the total

NI

plant , hence the dominant N source was EOI

(mineralization). An additional factor influencing NI

application was the low ET values during 

winter. Implementation of the cyclic strategy 

during these low ET conditions could not add 

sufficient N to match plant requirements for 

Table 3.3. Salts and macro-nutrients of a raw and 

treated dairy wastewater (DWW). DWW was sequentially treated by solid separator, sedimentation tank and 

sand filter.

Category Component Raw 

DWW

Treated 

DWW

General

EC (dS·m-1) 3.7 3.2

SSC (mg·L-1) 1611.4 199.1

pH 7.57 8.16

Salts

 (mg·L-1)

Na 182.5 149.9

Ca 378.3 299.3

Mg 243.1 231.4

Cl 174.2 175.8

S-SO4 109.1 70.4

HCO3 2163.1 1829.0

MacroNutrients

(mg·L-1)

N-(NH4+NO2+NO3) 157.4 145.2

Organic N 55.5 6.9

K 404.9 380.5

Total P 39.0 27.9
36

several weeks, without adding DWW in excess 

of ET.

Decreases in NO

soil over time are shown in 

Figure 3.7 for both triticale and sorghum 

plots as a result of mineralization. Average 

mineralization rates were 1.23E-03 day-1 

during the triticale growing season and 

2.54E-03 day-1 during the sorghum growing 

season. The elevated rates during summer 

were associated with higher soil temperatures 

(the average soil temperatures for the winter 

and summer growing seasons at -15 cm were 

15.6°C and 24.5°C, respectively) that accelerated the mineralization process (Watts et al., 

2007). These mineralization rates are lower 

than earlier studies using DWW (Feng et al., 

2005; Stenger et al., 2001). This is likely 

due to the low organic content of the applied 

DWW that was treated, and the fact that most 

of the organic N in the soil was plant residuals 

(low C to N ratio). Mineralization continues 

during fallow periods (Figure 3.4) and this 

lead to differences in NO

soil between growing 

seasons, especially for the long fallow period 

(241 days) between the harvest of sorghum 

and emergence of alfalfa. A comparison 

between measured and calculated (N mass 

balance) values of NO

soil at the beginning of 

winter 2008 revealed no significant difference. The final calculated and measured NO

soil

reservoir at the beginning of winter 2008 was 

found to be 267.9±45.1 and 220.2±44.6 g 

for the blending strategy and 276.6±39.8 and 

236.3±38.9 g of organic N·m-2 for the cyclic 

strategy. 

The typical concentration ratios of N, P and 

K in the applied DWW were 10:1.93:26.2 

(Table 3.3). Direct measurements of P and 

K contents in plant tissues were not made 

during the course of the growing season. 

However, plant uptake rates of N, P, and K by 

wheat and sorghum have been reported to be 

10:1.9:11.5 and 10:1.4:8.75, respectively 

(Bar-Tal et al., 2004; Vanderlip and Reeves, 

1972). If these nutrient uptake ratios are 

assumed, then P and K will be applied in 

excess and accumulated in the root zone 

when a NMP based on N is implemented to 

meet crop demand. Excess application of 

P becomes an environmental concern when 

surface water runoff and shallow water tables 

can mobilize the P into surface water bodies 

(Wang et al., 2004). Yet, arid and semi-arid 

environments are mostly associated with 

deep water tables, a high capacity of mineral 

soils for P adsorption and limited runoff to 

nearby surface water due to efficient water 

application. 

Figure 3.8 presents concentrations of plant 

available K and P (phosphorus from phosphate, P-PO4) in the soil profile at the 

beginning of the 2007 winter growing season 

(initial) and the end of the 2007 summer 

crop season (final) for both blended and cyclic 

dairy wastewater application strategies. The 

distributions of K and P exhibited changes 

primarily in the upper 50 cm of the soil profile, where roots were most active in water and 

nutrient uptake. The actual deficit NI

application 

(Figure 3.7) corresponded to 5.05 g of P·m-2, 

relative to an estimated 8.65 g of P·m-2 taken 

up by the crop. Therefore, the final soil P content with both strategies is less than the initial 

content. However, if NI

application is matched to 

NI

plant as the main N sink, P will be applied in 

excess and accumulate in the soil. Similarly, 

excess amounts of K in the DWW and the large 

pool of K in the soil profile causes accumulation of K in the root zone for both cyclic and 

blending strategies. 

Nitrogen Fixation – Alfalfa 2008

Since no significant differences in cyclic and 

blended treatments were observed during 

2007, only the blended treatment was implemented on alfalfa during 2008. In contrast to 

triticale (winter 2007) and sorghum (summer 

2007) crops discussed above, alfalfa (summer 

2008) is a legume and may also obtain N 

through fixation. The implications of N fixation on efficient NMP implementation will be 

discussed below. 
37

Figure 3.7: Soil inorganic and organic N reservoirs ( NI

soil and NOsoil ) and cumulative values of N 

uptake by plant ( NI

plant ), exchange to/from organic and inorganic N forms (EOI ), N loss to drainage 

( NI

drainage ), supplied organic N ( NOapplication ), and supplied inorganic N minus loss to the atmosphere 

( NI

application - NIatmosphere ) in the root zone are presented over time. Data is presented as g of N·m-2 for triticale 

(30 cm root zone), sorghum (60 cm root zone) and the alfalfa (90 cm root zone). Error bars represent 

measured or calculated standard deviations. The value of EOI was obtained by closure of the mass balance 

(Eqs. 3.2 and 3.3), and reflects changes due to mineralization for triticale and sorghum, and mineralization 

and N fixation for alfalfa.
38

Similar to triticale and sorghum data shown 

in Table 3.2 and Figure 3.2, an efficient 

water mass balance was implemented for 

alfalfa during 2008. Dry phytomass, cumulative water application and cumulative ETactual

throughout the five growing cycles of alfalfa 

in 2008 are presented in Figure 3.9. The 

error bars represent an estimation of the 

measuring error associated with the sampling 

procedure of alfalfa (10%). Dry phytomass 

ranged between 325 g·m-2 at the last growing cycle, to 750 g·m-2 at the second growing 

cycle (ending July 24th). The relatively slow 

growth rate during the last cycle was due to 

seasonal variation in climate; i.e., the average maximum and minimum air temperatures 

and solar radiation during this period were 

relatively low with values of 23.1°C, 3.07°C 

and 265.3 W·m-2, respectively. In contrast, 

the low dry phytomass of the first cycle was 

related to the extensive weed growth that 

interfered with the normal development of the 

alfalfa during this period. The ratio between 

the total water applications (1626.7mm) to 

the total ETactual (1455.5 mm) corresponded to 

a leaching fraction of 11.7%. The calculated 

crop coefficients, Kc, were consistent with 

published data (Allen et al., 1998) and varied 

between 0.4 after harvesting to 1.2 when full 

foliage cover was reached. Similar to 2007 

data shown in Figure 3.2, changes in |h| were 

restricted only to the upper 60 cm of the soil 

profile due to the accurate water balance. 

Time (month)

May Jul Sep Nov Jan 

Cumumulative water application or 

actual Evapotranspiration (mm)

0

500

1000

1500

Alfalfa dry phytomass (g.

m-2)

0

200

400

600

800

1000

Actual evapotranspiration

Water application

Phytomass

Figure 3.9. Dry phytomass, cumulative water application and actual evapotranspiration throughout 

the five growing cycles of alfalfa in 2008.

The alfalfa 2008 growing season started after 

a long fallow period (Nov. 2007-April 2008). 

A total of 25.7 cm of rainfall occurred during 

the fallow period that leached salts further 

into the soil profile. Similar to the 2007 data 

presented in Figure 3.3, the TDS increased 

over the growing season. The TDS in the 

soil profile (0 to -170 cm) at the beginning 

Figure 3.8. Plant available phosphorus (P-PO4) and potassium (K) in the soil profile at the beginning (initial) of the triticale and end (final) of the sorghum growing seasons in 2007 for the blending and cyclic water 

application strategies.
39

of the growing season (May 2008) was 

937.7±338 g·m-2 and at the end (Dec. 2008) 

was 1391.7± 265 g·m-2. This increase was 

pronounced at all depths and especially the 

upper 30 cm (98.8 g·m-2 versus 254.3 g·m-2). 

Figure 3.7 shows the various components of 

the N balance that were measured for alfalfa 

during 2008. Since alfalfa can fix atmospheric N through nodules, the value of EOI

now accounts for the net exchange between 

organic and inorganic N forms due to both 

mineralization and atmospheric N fixation. 

Due to this additional complication, only 

initial and final values of measured N O

soil are 

shown in Figure 3.7 for alfalfa. Recall that a 

very conservative NMP approach was implemented for triticale and sorghum crops in 

2007 that depleted the soil organic N. In 

contrast, a less conservative NMP strategy was 

implemented for alfalfa during 2008 due to a 

greater value of N I

plant , a deeper root system, 

and the desire to suppress atmospheric N 

fixation. Specifically, the cumulative value of 

N I

plant was 104 g of N·m-2 for alfalfa in comparison to 60 g of N·m-2 for both triticale and 

sorghum. The depth of the root zone, where 

roots are most active in water and nutrient 

uptake under irrigated conditions, for alfalfa 

was approximately 90 cm in comparison to 30 

and 60 cm for triticale and sorghum, respectively. A larger root zone allows nutrients to 

be extracted over a larger area and to implement a more flexible schedule for lagoon water 

application. In addition, by maintaining a 

high N concentration in the root zone, we can 

minimize the need of the plant to seek for 

alternative N sources and theoretically apply 

more DWW. The value of NI

application - NIatmosphere 

for the alfalfa was selected to be 76% of the 

total N I

plant . This implies that the remaining 

24% of N I

plant comes from mineralization or 

atmospheric N fixation.

Several implications for the less conservative NMP strategy that was implemented on 

alfalfa during 2008 are discussed below. 

First, an increase in measured NO

soil is shown 

in Figure 3.7 for alfalfa. This observation 

can be attributed to three factors, namely: 

i) intensive root growth of the alfalfa that 

increased the total NO

soil ; ii) the less conservative NMP approach yielded a higher ratio 

of NI

application - NIatmosphere to the total NIplant ; 

and iii) the mechanism of N fixation through 

nodules reduced the need for mineralized 

NO

soil , and induced immobilization of inorganic N. Amounts of N in excess of plant 

uptake requirements were apparently generated due to these factors. Consequently, 15% 

of the supplied inorganic N was measured 

to be steadily drained below the root zone 

throughout the growing season under the 

applied leaching factor of 11.7%. Additional 

research is therefore needed to optimize NMP 

performance with leguminous crops.
40

4.0

NMP Results – 

Indicator Microorganisms

Background 

NMPs implicitly assume that pathogenic 

microorganisms in CAFO lagoon water will 

be retained and inactivated/degraded in the 

root zone, so that food and water supplies are 

protected. This assumption has not yet been 

thoroughly tested. The objective of this work 

was to test the hypothesis that fecal indicator 

microorganisms (Enterococcus, total E. coli, 

fecal coliforms, and somatic coliphage) will 

be retained and die-off in the root zone of a 

NMP application site. To this end, the fate 

of indicator microorganisms at a field NMP 

site was monitored during winter and summer 

growing seasons. Additional information 

was obtained in well controlled laboratory 

experiments in order to: (i) identify mechanisms that controlled the fate of the indicator 

microorganisms at the NMP site, and (ii) to 

develop recommendations for improved NMP 

performance. Specifically, batch experiments 

were conducted to quantify the survival characteristics of the indicator microorganisms. 

Small-scale packed column experiments were 

conducted to study the influence of the soil 

matrix on transport and retention behavior. 

Finally, a large undisturbed column experiment was conducted to assess the influence 

of soil structure on indicator microorganism 

transport and survival under a worst case scenario of ponded infiltration and redistribution. 

Materials and Methods

NMP Field Site

The experimental field site is located 

in San Jacinto, CA (33°50′22 ̋ N, 

117°00′46 ̋ W), next to a dry river bed and 

has a shallow perched water table at a depth 

of -220 cm. The experiments were conducted 

on a 6x6 m plot that was described in detail 

in Chapters 2 and 3. Soil cores were periodically collected from the NMP plot boundaries 

for microbial analysis as described below.

Before initiating the NMP, extensive field and 

laboratory studies were conducted to characterize the soil hydraulic properties, the water 

flow behavior, and the transport of a conservative solute tracer on these plots. Detailed 

information is provided in Segal et al. (2008 

and 2009) and in Chapter 2. 

For accurate NMP implementation it was 

necessary to conduct a treatment on the 

dairy wastewater using an inclined screen, a 

sedimentation tank, and sand filtration. This 

treatment significantly decreased the organic 

load of the wastewater and thus increased 

the proportion of plant available inorganic N 

relative to organic N, which was necessary for 

efficient management of the NMP (Segal et al. 

2010). The indicator microbial composition 

of the dairy wastewater before and after this 

treatment was measured as part of this work. 

Detailed information on the NMP design and 

implementation is provided in Segal et al. 

(2010) and in Chapter 3. 

Analysis and Sampling for Indicator 

Microorganisms

Representative viral (somatic coliphage) and 

bacterial (total E. coli, fecal coliforms, and 

Enterococcus) indicator microorganisms were 

monitored at the NMP application site and 

in laboratory experiments discussed below. 

These microbes are commonly associated 

with fecal contamination and are typically 

found in high concentrations in animal wastes 

(Havelaar, 1986; Bradford et al., 2008). 

Somatic coliphage, such as fX174, are very 

persistent bacterial viruses that resemble 

pathogenic viruses in their fate and behavior 
41

(Schijven and Hassanizadeh, 2000), but 

are harmless to humans and can be prepared and quantified easily. Total E. coli and 

Enterococcus are frequently used as indicators 

of bacterial pathogens that are found in the 

intestinal tract of humans and warm-blooded 

animals.

The concentration of indigenous somatic 

coliphage in aqueous samples was determined using the double agar overlay Method 

1601 (USEPA, 2001b) with bacterial host 

Escherichia coli CN-13 (ATCC 700609). In 

brief, 1 mL of log phase host culture and 

1 mL sample was added to borosilicate test 

tubes (Thermo Fisher Scientific, Waltham, 

MA) containing 4 mL of trypticase soy agar 

(TSA) (BD Diagnostic Systems, Sparks, MD) 

supplemented with 1% nalidixic acid (NA) 

(Sigma-Aldrich, St. Louis, MO). The mixture 

was poured onto sterilized 100 × 15 mm 

plastic petri plates (Thermo Fisher Scientific, 

Waltham, MA) and allowed to solidify for 

15 min, after which, they were inverted and 

incubated for 16 h at 36°C ± 1.0°C. The 

number of plaque forming units (PFU) in 

the plates was determined by counting the 

plaque density under a darkfield colony 

counter (Leica, Buffalo, NY). All coliphage 

assays were run in duplicate and diluted as 

necessary.

Aqueous concentrations of total E. coli, fecal 

coliforms, and Enterococcus were determined 

using the conventional spread plating method 

(Clesceri et al., 1989). A 100 μl sample 

was plated on Chromagar ECC (CHROMagar 

Microbiology, Paris, France) plates for total 

E.Coli, on mFC agar (BD Diagnostic Systems, 

Sparks, MD) plates for fecal coliforms, and on 

KF agar (EM Science, Gibbstown, NJ) plates 

for Enterococcus. The plates were inverted 

and incubated at 37°C for total E. Coli (24 h) 

and Enterococcus (24-48 h), and at 44.5°C 

for fecal coliform (24 h). The bacterial colony 

forming units (CFU) were then counted. All 

bacterial assays were run in duplicate and 

diluted as necessary.

Dairy wastewater samples were analyzed 

directly according to the procedures outlined 

above. Soil samples were collected and 

weighed under field and oven dried conditions. For microbial analysis, a 10 g sample 

of the field soil from each depth increment 

was placed in a 50 mL sterile polypropylene 

centrifuge tube (Thermo Fisher Scientific, 

Waltham, NJ), 20 mL of phosphate buffered 

solution or de-ionized water was added, and 

the solution was gently mixed on a Eberbach 

shaker (Eberbach corporation, Ann Arbor, 

Michigan) for 30 min, and then the soil solution was allowed to settle for 5 min. This 

solution was subsequently analyzed for 

microbial concentrations using the outlined 

procedures, and the concentrations were corrected for the amount of soil and solution in 

each depth increment. Results from column 

transport and batch survival experiments 

discussed below demonstrate that reasonable mass balance and microbe recovery was 

achieved when using this protocol with soil 

from the field plot and the considered indicator microorganisms. 

For laboratory transport experiments that 

utilized spiked concentrations of indicator microorganisms the following protocols 

were followed. Stock bacteria, E. coli (ATCC 

11775); Enterococcus faecalis (ATCC 19433), 

and somatic coliphage, fX174 (ATCC 13706-

B1) were grown 24 h prior to the laboratory 

experiments. E. coli was propagated in trypticase soy broth (TSB) (BD Diagnostic Systems, 

Sparks, MD) and Enterococcus faecalis was 

grown in LB broth (FisherBiotech, Fair Lawn, 

NJ). Both bacterial strains were incubated 

overnight at 37°C ± 1.0°C. The somatic 

coliphage host, E. coli (ATCC 13706) was 

incubated in TSB for 18 h at 37°C ± 1.0°C. 

Bacteriophage fX174 was then added into 

the broth culture of host bacteria and allowed 

to propagate overnight. The host, E. coli, 

was removed from the suspension, containing bacteriophage fX174, by centrifugation 

at 17329 × g for 10 min. The other bacteria were harvested from the growth broth, 
42

and washed twice in PBS and centrifuged at 

17329 × g before using them in the transport 

experiments. 

Laboratory Experiments

Batch – Batch experiments were conducted 

to assess the survival characteristics of the 

indicator microorganisms. Experiments were 

conducted by placing 7.5 g of field soil into 

a 20 ml glass scintillation vial. Two conditions were studied to determine the influence 

of biotic factors on the survival of the indicator microorganisms. One set of experiments 

employed native soil and wastewater, and the 

other used sterilized (autoclaved) soil and 

wastewater spiked with indicator microorganisms. Lagoon water (0.43 ml) and deionized 

water were added to the vials to achieve a 

water saturation of 80%. The porosity of the 

soil in the vials was estimated from the soil 

volume and mass to be 0.43. The deionized 

water and wastewater were then thoroughly 

mixed with the soil using a metal spatula, and 

the vial was capped (a pin hole was used for 

aeration) and wrapped in aluminum foil. The 

vials were subsequently stored at 25°C and 

analyzed for indicator microorganism concentrations at desired times. Separate vials were 

used for each time (0, 6, 10, 24, 48, 80, 

144, and 168 h). Hence, a total of 8 vials 

were independently analyzed for native and 

sterilized batch experiments. 

Microorganism death/inactivation is typically assumed to follow a simple first-order 

decay model (Yates et al., 1987; Schijven 

and Hassanizadeh, 2000). However, virus 

inactivation and bacterial growth in soils have 

been observed to exhibit more complex survival behavior than the simple first-order decay 

model. The following model was proposed by 

Sim and Chrysikopoulos (1996) to account for 

observed time dependent inactivation:

exp( ) T

o T

dC t C

dt

= −λ −α [4.1]

where CT [N L-3 ; where N is the number 

of microorganisms, and L denotes units of 

length], is the total microorganism concentration, t [T, denotes units of time] is time, 

lo [T-1] is the initial rate of die-off/inactivation, and a [T-1] is the resistivity coefficient to 

die-off/inactivation. The solution to Equation 

[4.1] is given as (Sim and Chrysikopoulos, 

1996):

exp exp 1 ( ) T o

Ti

C t

C

 λ  =  −α −      α  [4.2]

where CTi [N L-3] is the initial total microorganism concentration. The parameters lo

 and a

will be fitted herein to measured batch survival 

data using a nonlinear least squares optimization routine. 

Packed Column - Packed soil column experiments were conducted using top soil from the 

field site to assess the transport potential of 

Enterococcus, somatic coliphage, and E. coli

under well controlled conditions. Sterilized 

field soil and wastewater that was spiked with 

indicator microorganisms were used for this 

purpose. 

Kontes Chromaflex chromatography columns 

(Kimble / Kontes, Vineland, NJ) made of 

borosilicate glass (15 cm long and 4.8 cm 

inside diameter, equipped with an adjustable 

adapter at the top) were used in the transport 

studies. The columns were packed dry with 

oven-dried soil from the surface of the NMP 

site. Well water from the field was slowly 

pumped upward through the vertically oriented 

columns at a steady flow rate for several pore 

volumes (PV) to saturate the column. A peristaltic pump (Cole-Parmer, Vernon Hills, IL) 

was used for this purpose. Microorganism 

transport experiments were subsequently 

conducted by pumping tracer solution at a 

steady rate through the column for several 

PV, after which a three-way valve was used to 

switch to the well water for several additional 

PV. Effluent samples were continuously collected in 16 × 150 mm borosilicate test tubes 

(Thermo Fisher Scientific, Waltham, NJ) using 

an auto sampler (Isco, Inc., Lincoln, NE). 
43

Following completion of the transport 

experiments, the concentration of indicator 

microorganisms in the effluent and retained in 

the soil was determined using the previously 

outlined protocols. 

Undisturbed Column - To study the influence 

of soil structure on the transport and survival 

of indigenous indicator microorganisms in 

wastewater a larger undisturbed soil column 

(length was 65 cm and the internal diameter 

was 24 cm) was taken from the field site just 

outside of the plot. This column was also used 

in water flow and bromide transport experiments, and details on the collection, handling, 

instrumentation, hydraulic properties, and 

solute transport characteristics of this column 

are provided in the literature (Segal et al. 

2009). In brief, the soil core was encased 

in an acrylic cylinder that was pushed into 

the soil with a hydraulic piston and then dug 

out. The bottom of the column was placed 

on a ceramic plate with a bubbling pressure 

of 100 kPa and a hanging water column was 

used to control the water pressure and to 

collect effluent samples. The soil surface 

boundary condition was selected to mimic a 

worst case transport scenario of ponded infiltration in which saturated conditions enable 

water flow and pathogen transport through 

macropores and soil structure. In this case, 

wastewater was instantaneously added to the 

soil surface to a depth of 7 cm and allowed to 

infiltrate into the profile. Following application and infiltration of the wastewater, 2 cm 

diameter and 65 cm long soil cores were 

collected vertically from the column surface 

at selected times and analyzed for concentrations of indicator microbes. The sample 

locations were subsequently plugged with a 

similar sized PVC tube that was capped. This 

experiment was conducted at 25°C in a temperature controlled room. 

Results and Discussion

NMP Field Site

The sequential treatment (inclined screen 

separator, sedimentation tank and sand filter) 

of the dairy wastewater at the field site had 

a large effect on many microbial properties. 

Table 4.1 provides representative information 

on the concentration of indicator microorganisms before and after this treatment. The 

treatment produced almost a 2 log decrease 

in the concentration of the various indicator 

microorganisms. Concentrations of indicator microorganisms in the treated wastewater, 

however, still significantly exceeded the recommended U.S. standards for unrestricted 

irrigation (USEPA, 2004). If pathogenic 

microorganisms behave similarly to indicators, then care must be taken in using dairy 

wastewater as a source for irrigation water. To 

protect surface and groundwater supplies it is 

therefore essential that pathogenic microorganisms in the dairy wastewater be removed 

by treatment, or be retained and inactivated/

degraded in the root zone of NMP sites.

Table 4.1. Representative concentrations of 

indicator microorganisms in raw and treated dairy 

wastewater, and the percent removal by treatment. 

Treatment included solid separator, sedimentation 

tank and sand filter.

Indicator Raw Treated % 

Removal

Enterococcus

(cfu ml-1) 1.56E+06 6.10E+04 96.1

fecal 

coliform 

(cfu ml-1)

2.47E+05 6.00E+03 97.6

somatic 

coliphage 

(pfu ml-1)

7.50E+03 5.00+02 93.3

total E. coli

(cfu ml-1) 9.30E+04 3.00E+03 96.8

Water mass balance information for the winter 

triticale and the summer sorghum growing seasons are presented in Table 4.2 as a 

function of day after emergence (DAE). The 

accurate water application quantities (well 

water + lagoon water) relative to the actual 
44

plant evapotranspiration (ET) restricted 

changes in water pressure to the upper 60 cm 

of the soil profile (Segal et al., 2010). Hence, 

the amounts of drainage below the root zone 

(Table 4.2) were very small throughout the 

winter and summer growing seasons and 

most of this drainage occurred with well 

water application (57.6 and 86.9% during 

winter and summer, respectively). This finding indicates that there exists little potential 

for advective transport of the indicator microorganisms below the root zone under these 

NMP management conditions. If the indicator microorganisms survive in the root zone, 

however, they may still be transported below 

the root zone when water in excess of ET is 

applied to this NMP site such as during the 

fallow period with high precipitation events or 

during pre-irrigations to leach salts. Hence, 

pathogen survival in the root zone may pose a 

risk to food and water resources at NMP sites.

Figure 4.1a-d present plots of the concentration of Enterococcus, fecal coliform, somatic 

coliphage, and total E. coli in soil (S, N g-1) 

as a function of depth at several different DAE during the winter triticale growing 

season, respectively. Table 4.2 also includes 

the amount of well water and dairy wastewater that was applied to the NMP study site. 

Irrigation water was applied at the end of the 

intervals provided in Table 4.2, except for 

the first interval in which the irrigation water 

was applied periodically throughout the interval. Low concentrations of total E. coli, fecal 

coliform, and somatic coliphage were initially 

present in the surface soil of the NMP site. 

The Enterococcus concentrations, however, 

were initially more abundant. After wastewater addition on DAE 36, the concentrations of 

the indicator microorganisms in the soil core 

increased dramatically. The highest concentrations occurred near the soil surface (top 

Table 4.2. Actual evapotranspiration (ET), rainfall, well water application, wastewater application, and 

drainage amounts during the winter (triticale) and summer (sorghum) growing seasons as a function of day 

after emergence (DAE).

Winter

DAE

Actual ET

mm

Rainfall

mm

Well Water 

mm

Waste Water 

mm

Drainage1

mm

15-29 40.1 7.2 42.9 0.0 10.0

30-36 13.5 0.0 0.0 15.0 1.5

37-50 68.4 4.2 0.0 71.7 7.6

51-58 36.5 4.1 36.3 0.0 4.1

59-65 43.0 13.2 0.0 34.5 4.8

66-72 43.2 0.0 48.0 0.0 4.8

Summer

DAE

Actual ET

mm

Rainfall

mm

Well Water

mm

Waste Water 

mm

Drainage1 

mm

5-28 104.0 0.0 135.0 0.0 26.0

29-35 58.5 0.0 62.0 0.0 6.5

36-44 76.2 0.0 0.0 91.51 8.5

45-49 42.4 0.0 48.6 0.0 4.7

50-58 77.4 0.0 86.6 0.0 8.6

59-62 31.7 0.0 35.3 0.0 3.5

63-70 59.1 0.0 66.7 0.0 6.6

1

- Value estimated from water balance
45

4-6 cm) and then rapidly decreased with soil 

depth. Over time concentrations of the indicator microorganisms at the soil surface tended 

to decrease, and by DAE 71 approached the 

low levels that were found before wastewater 

application (DAE 36). 

Similar NMP experiments were conducted 

during the summer on sorghum. Soil cores 

were periodically collected from the field 

site during the summer season and analyzed 

for indicator microorganism concentrations. 

Similar to the winter crop, only low levels of 

Enterococcus were found in the surface soil at 

the beginning and end of the summer season. 

A more detailed NMP experiment was initiated the following summer growing season 

(alfalfa, Medicago sativa, was the crop) to 

better understand the transport and fate of the 

indicator microorganisms. In this case, the 

sampling frequency (initial, and times=0, 14, 

62, 134, and 206 h after wastewater application) and number of soil cores (composite 

of 3 cores) were much higher after a single 

wastewater application event. In addition, 

the gravimetric water content with depth was 

measured at selected times after wastewater 

application. Initial concentrations in soil were 

low for the somatic coliphage and total E. 

Figure 4.1. Plots of the concentration of Enterococcus (Figure 4.1a), fecal coliform (Figure 4.1b), somatic 

coliphage (Figure 4.1c), and total E. coli (Figure 4.1d) in soil (S, N g-1) as a function of depth at several different days after emergence (DAE) during the winter triticale growing season at the NMP field site.
46

coli (CT was 0 CFU cm-3). Conversely, initial 

concentrations for Enterococcus (CT was 5E4 

CFU cm-3) and fecal coliform (CT was 1.3E4 

CFU cm-3) in soil were higher. Approximately 

10.5 cm of wastewater was applied to the 

field site at an average rate of 0.95 cm h-1. 

Changes in the water content occurred over 

depth and time (Figure 4.2a) as a result of 

infiltration, redistribution, and then evapotranspiration in the root zone (top 60 cm of 

soil). Figure 4.2b-e present plots of the normalized concentration of Enterococcus, fecal 

coliform, somatic coliphage, and total E. coli

in soil (S/CTi ; cm3 g-1) as a function of depth 

at selected sampling times after wastewater 

application. Here the concentrations of a 

given microorganism in soil were normalized 

by CT for the entire soil core taken at time=0 

h, i.e., denoted as CTi . The concentration 

of indicator microorganisms in the soil was 

high at time = 0 h, and reflected initial conditions, transport during wastewater infiltration, 

and retention of the indicator microorganisms in the soil. At later sampling times 

(greater than or equal to 14 h) infiltration has 

ceased; redistribution and evapotranspiration 

of water depletes water from the root zone 

(Figure 4.2a). In general, the concentration 

of the indicator microorganisms in the soil 

cores is controlled at earlier sampling times 

(t=14 and 62 h) by initial conditions and 

microbe retention, and at later times (t=134 

and 206) by microbe survival and/or inactivation. Microbe retention tends to be highest 

at the soil surface and to rapidly decrease 

with soil depth. Survival of fecal coliforms, 

somatic coliphage, and total E. coli was apparently short lived under these NMP conditions, 

with concentrations in soil approaching low 

background levels within 134 h after wastewater application. This result is at least partly 

due to the high summer soil temperature 

which averaged 22.6°C at a depth of 7 cm 

during this experiment. Cooler temperatures 

that were measured during the winter growing season (20.3°C at a depth of 7 cm) are 

expected to enhance microorganism survival 

(Reddy et al., 1981; Yates et al., 1987) shown 

in Figure 4.1. Conversely, final concentrations 

of Enterococcus in soil were more pronounced 

than the other indicator microbes. This may 

occur as a result of differences in survival or 

due to differences in the initial concentration in these microbes in the soil (CT

 was 5E4 

and 1.3E4 CFU cm-3 for Enterococcus and 

fecal coliform, respectively) and wastewater 

(Ci in the wastewater was 8.9E04 and 1.7E3 

CFU ml-1 for Enterococcus and fecal coliform, 

respectively). 

A more quantitative determination of the 

transport and survival of the indicator microorganisms at the NMP site was not attempted 

due to the relatively low initial concentration 

levels of these microbes in the irrigation water 

and our analytical detection limits, potential 

spatial variability in microbe concentrations in 

the field, incomplete control of the lower water 

flow boundary conditions, and transients in 

water content that were induced by infiltration, 

redistribution, and ET. Laboratory experiments 

that are described below were initiated to overcome many of these limitations. 

Laboratory Experiments

Figure 4.3 presents results from the batch 

survival experiments conducted under sterile 

(Figure 4.3a) and native (Figure 4.3b) conditions at 80% water saturation for the various 

indicator microorganisms. A semi-log plot 

of CT /CTi as a function of time is shown in 

this figure. The considered bacterial indicator microorganisms had much greater die-off 

under native than sterile conditions. This 

indicates that biotic factors such as predation 

and competition in native soil and wastewater 

had a significant influence on the survival of 

the considered bacterial indicator microorganisms. Other researchers have reported 

similar observations and this literature has 

been reviewed by Van Veen et al. (1997). 

Conversely, the somatic coliphage exhibited 

similar survival/inactivation under both sterile 

and native conditions, suggesting that abiotic 

factors controlled die-off/inactivation for this 

coliphage. The native condition is expected 
47

Figure 4.2. Plots of the gravimetric water content ( qg ) (Figure 4.2a), and the normalized concentration of 

Enterococcus (Figure 4.2b), fecal coliform (Figure 4.2c), somatic coliphage (Figure 4.2d), and total E. coli 

(Figure 4.2e) in soil (S/CTi ) as a function of depth at selected sampling times (initial, and times=0, 14, 62, 

134, and 206 h after wastewater application) during the summer alfalfa growing season at the NMP field 

site. Microbial concentrations were determined from a composite of 3 soil cores at the indicated depth and 

time, and normalized by the total concentration of a given microorganism measured in the entire core at 

time=0 (CTi ). 
48

Figure 4.3. A semi-log plot of the relative total concentration (CT /CTi) as a function of time in batch survival 

experiments under sterile (Figure 4.3a) and native (Figure 4.3b) conditions at 80% water saturation for the 

various indicator microorganisms.

to be most representative of field conditions 

at the NMP site. Table 4.3 therefore provides 

a summary of the fitted die-off/inactivation 

rates (Equation 4.2) and the standard error 

coefficient on these parameters for the various indicator microorganisms under native 

conditions at 80% water saturation. The P 

values for statistical significance of the correlation coefficients were less than 0.00002. 

It is interesting to note that fecal coliforms 

had a lower die-off rate than Enterococcus

under native conditions (Figure 4.3b and 

Table 4.3). This finding implies that the 

observed differences in final concentrations 

of these microbes in soil shown in Figure 4.2 

was likely due to differences in the initial 

concentrations of these organisms in soil and 

wastewater. Packed soil column experiments 

were conducted using sterilized soil (sandy 

loam) from the root zone of the NMP field 

site to better understand the transport of the 

indicator microorganisms. The tracer solution 

consisted of sterilized (autoclaved) wastewater 

spiked with a known concentration of specific 

microorganisms, and sterilized well water was 

employed as the resident and eluant solutions. 

Figure 4.4a presents breakthrough curves for 

Enterococcus, total E. coli, and fX174 in the 

packed soil column. Relevant experimental 

conditions are provided in the figure caption. 

The coliphage fX174 exhibited much greater 

transport potential (87.1%) than the bacteria Enterococcus (0.6%) or E. coli (0.3%). 

This is likely due to differences in the size 

and surface chemistry of these microorganisms. In particular, fX174 is much smaller 

than Enterococcus or E. coli (0.5-2 microns). 

Straining is therefore expected to play a more 

dominant role in retention of the bacterial 

Table 4.3. Fitted parameters (lo and a) from the 

time dependent decay model (Equation 4.2) for 

the various indicator microorganisms under native 

conditions at 80% water saturation. The goodness 

of model fit is quantified by the coefficient of linear 

regression (R2) on natural log transformed data, and 

standard error coefficient of the fitted model parameters is provided in brackets.

Indicator

lo

(h-1)

a

(h-1)

R2

Enterococcus 3.52E-02 

(1.31E-03)

3.54E-11 

(4.32E-04) 0.992

fecal coliform 1.90E-01 

(4.03E-02)

7.81E-02 

(1.86E-02) 0.933

somatic 

coliphage

1.61E-01 

(2.05E-02)

2.95E-02 

(4.65E-03) 0.973

total E. coli 7.29E-02 

(1.22E-02)

1.25E-02 

(3.55E-03) 0.963
49

cells than the coliphage based on size considerations (Bradford et al., 2003). It is 

interesting to note that the breakthrough curve 

for both Enterococcus and E. coli exhibited a 

similar shape and magnitude in the relative 

concentrations, with initially low effluent concentrations that slowly increased with time. 

This observation suggests the potential for 

low but persistent amounts of bacteria transport that were likely due to slow release of 

cells from the solid phase, i.e., detachment, 

hydrodynamic shearing, or diffusion from low 

velocity regions (Bradford and Torkzaban, 

2008). 

After recovery of the breakthrough curve, 

the soil in the column was excavated to 

recover the remaining cells in the column. 

Figure 4.4b presents the microorganism 

retention profiles for Enterococcus, total 

E. coli, and fX174 in the packed soil column. 

Consistent with the breakthrough curve 

information the total amounts of microbes 

recovered in the soil were 7.8, 83, and 109% 

for fX174, E. coli, and Enterococcus, respectively. Reasonable mass balance was achieved 

in this packed column experiment (83 to 

109%). Batch results shown in Figure 4.3a 

indicate that little death or growth of the 

indicator microorganisms was likely to occur 

during the column experiments (<6.25 h). 

Hence, the mass balance information supports 

our methodology for determining microorganism concentrations in the field. It should also 

be mentioned that the collected retention 

profiles for the microorganisms were not log 

– linear with depth; i.e., the rate of retention 

was not first-order. The observed nonmonotonic shape of the retention profiles for the 

bacteria cells likely reflected the release and 

continued slow migration of cells through 

the soil. Other researchers have reported 

nonmonotonic profiles for bacteria in porous 

media and this shape has been reported to 

be sensitive to the soil grain size, the system 

hydrodynamics, and the sampling time (Tong 

et al., 2005; Bradford et al., 2006b). In 

contrast, the retention profile for fX174 shows 

enhanced retention near the column inlet that 

decreased with distance. In the literature this 

type of depth dependent retention has been 

attributed to straining (Bradford et al., 2006a) 

as well as chemical heterogeneity of the 

microorganisms (Li et al., 2004; Tufenkji and 

Elimelech, 2005). 

Collectively, Figures 4.4a and 4.4b demonstrate the complexity of microorganism 

Figure 4.4. Breakthrough curves (Figure 4.4a) and retention profiles (Figure 4.4b) for Enterococcus, 

total E. coli, and fX174 in a column experiment packed with sterilized field soil. In Figure 4.4a log relative 

concentrations (C/Ci; where C is the aqueous concentration and Ci is the initial concentration in the influent 

suspension) are plotted as a function of pore volumes. In Figure 4.4b log relative concentrations in soil, 

(S/Ci), is plotted as a function of distance away from the inlet. Relevant experimental conditions include 

the following parameters: porosity=0.50, column length=14.4 cm, tracer pulse duration=112.5 min, and the 

Darcy water velocity=3.36 cm h-1. 
50

transport and retention behavior in soil from 

the NMP site. In this work we do not attempt 

to quantify and simulate the exact retention 

mechanisms, as this would require additional 

experiments and model development that 

are beyond the scope of this manuscript. In 

addition, application of laboratory retention information collected under saturated 

conditions to predict transport of the microorganisms at the NMP site is complicated by the 

sensitivity of microorganism retention to water 

content, transients, and fluid velocity. The 

interested reader is referred to the following 

literature that discusses current challenges in 

modeling microorganism retention in porous 

media (Bradford and Toride, 2007; Bradford 

and Torkzaban, 2008).

Wastewater application under ponded infiltration conditions represents a worst case 

scenario for microorganism transport, 

because more conductive pore spaces and 

soil structure are water filled under saturated conditions. An additional transport 

experiment was therefore conducted on an 

undisturbed column from the NMP site (core 

length was 65 cm and the internal diameter 

was 24 cm), with wastewater instantaneously 

added to the soil surface to a depth of 7 cm. 

Figure 4.5 presents plots of the normalized 

concentration of the indicator microorganisms 

in soil as a function of depth at a sampling 

time of 24 h. In this case, the infiltrate 

front has already passed through the column 

and water is slowing redistributing (data not 

shown). High concentrations of the indicator 

microorganisms occur near the soil surface 

and then tend to rapidly decrease with depth. 

This mainly reflects microbe retention behavior. Figure 4.5 also indicates the presence 

of isolated low concentrations of bacterial 

indicators at several deeper depths (27.94 

and 53.34 cm). The concentrations at these 

locations likely reflect the influence of soil 

structure or low levels of bacteria release 

and migration as previously identified in 

Figure 4.4. 

Figure 4.5. Plots of the normalized concentration of the indicator microorganisms in soil (S/CTi) 

as a function of depth at a sampling time of 24 h 

after ponded infiltration of wastewater ceased on 

the undisturbed soil core from the NMP site. Here 

the concentration in soil is normalized by the total 

concentration of a given microorganism measured 

in the entire core at time = 0 (CTi).

It should be mentioned that analysis of the 

column effluent and soil at the column outlet 

from the undisturbed core did not reveal any 

indicator microorganisms for all core sampling times (24, 48, 96, and 176 h). Soil 

cores taken at later sampling times primarily 

reflected the influence of indicator microorganism survival and may be analyzed in a 

similar fashion to the batch survival experiments by plotting the log of CT /CTi for the 

entire core as a function of time in Figure 4.6. 

Survival information obtained from batch 

experiments under native conditions and 80% 

water saturation were generally consistent with 

results shown in Figure 4.6. After 176 h the 

batch and undisturbed column values of CT /CTi

were 0.003 and 0.006 for Enterococcus, 

and 0.003 and 0.005 for somatic coliphage, 

respectively. The total concentrations of fecal 

coliform and total E. coli in the undisturbed 

column could only be detected up to 96 and 

48 h, respectively, but were similar in magnitude to native batch experiments at these 

times (compare with Figure 4.3b). 
51

Figure 4.6. A semi-log plot of the relative total 

concentration (CT /CTi) of the various indicator microorganisms as a function of time in the ponded 

infiltration undisturbed soil column experiment. 

Here the total concentration in soil is normalized 

by the total concentration of a given microorganism 

measured in the entire core at time = 0 (CTi). 
52

5.0

Laboratory Studies Investigating 

Mechanisms of Colloid Retention 

Background

Recent experimental and theoretical research 

has indicated that the pore structure and 

the solid-water-air triple point can play an 

important role in colloid and microorganism 

retention under unfavorable attachment conditions (Cushing and Lawler, 1998; Bradford et 

al., 2002, 2003, 2004, 2005, 2006abcd; Li 

et al., 2004, 2006ab; Tufenkji et al., 2004; 

Bradford and Bettahar, 2005 and 2006; 

Foppen et al., 2005). Pore spaces occurring 

at grain contacts and triple points provide 

optimum locations for colloids that are weakly 

associated with the solid water interface (SWI) 

or the air-water interface (AWI) to be retained 

because of reduced hydrodynamic forces, size 

limitations, and adhesive interactions from 

multiple interfaces (Hoek and Agarwal, 2006). 

Enhanced retention of colloids in the smallest 

regions of the soil pore space formed adjacent 

to grain-grain contacts and the triple point 

has been referred to as straining (Cushing 

and Lawler, 1998; Bradford and Torkzaban, 

2008). 

Most published research on straining has 

focused on the role of physical factors such as 

the relative size of the colloid to the median 

grain diameter, and little attention has been 

given to the potential interrelated influence 

of solution chemistry and hydrodynamics on 

straining. Attachment under unfavorable 

conditions is known to be highly dependent 

on solution chemistry (e.g., Li et al., 2004; 

Tufenkji and Elimelech, 2004 and 2005a), 

and system hydrodynamics (Wang et al., 

1981; Tan et al., 1994; Kretzschmar et al., 

1997; Compere et al., 2001; Li et al., 2005). 

Under unfavorable attachment conditions, 

hydrodynamic forces may be sufficient to overcome weak adhesive interactions (Bergandahl 

and Grasso, 1999; Torkzaban et al., 2007). 

In this case, attached colloids may be lifted 

from the solid surface and detach, or they 

may roll, skip or slide down gradient on the 

SWI and/or AWI to locations where the hydrodynamic shear is less significant. It is logical 

to anticipate that some of these mobilized 

colloids will be retained in small pore spaces 

formed at grain-grain contacts and the triple 

point. One can therefore expect that solution 

chemistry and hydrodynamic forces will play 

an important interrelated role in straining. 

The objective of this research is to investigate the role of solution chemistry and 

system hydrodynamics on colloid transport 

and straining. Negatively charged latex 

microspheres and quartz sands were used in 

batch experiments and packed column studies that encompassed a range of solution 

ionic strength, Darcy water velocity, grain 

sizes, and water saturation. All experiments 

were conducted using electrolyte solution 

buffered to a pH of 10 to ensure highly unfavorable attachment conditions. Data analysis 

and interpretation was aided through interaction energy calculations, mass balance 

computation, mathematical modeling, and 

experimental determination of breakthrough 

curves and deposition profiles. Details on the 

saturated colloid transport experiments and 

results are given in Bradford et al. (2007), 

whereas Torkzaban et al. (2008a) discusses 

findings from the unsaturated colloid transport 

experiments. Only an abbreviated discussion 

is provided below.

Materials and Methods

Colloids

Yellow-green fluorescent latex microspheres 

(Molecular Probes, Eugene, OR) were used as 

model colloid particles in the experimental 

studies (excitation at 505 nm, and emission at 515 nm). Two sizes of microspheres 

were used in the transport experiments, 
53

1.1 and 3.0 μm. The uniformity of the colloid size was verified using a Horiba LA 930 

(Horiba Instruments Inc., Irvine, CA 92614) 

laser scattering particle size and distribution 

analyzer and by inspections of suspensions 

under an epi-fluorescent microscope. The 

microspheres had carboxyl surface functional 

groups, a density of 1.055 g cm-3, and are 

reported as hydrophilic by the manufacturer. 

The initial influent concentration (Ci) for the 

1.1 and 3.0 μm colloids for the experiments 

was 2.7×1010 and 1.3×109 Nc L-1 (where Nc

denotes number of colloids), respectively. 

Several experiments with the 1.1 μm colloids 

were also conducted at a lower initial concentration of Ci =6.8×108 Nc

 L-1. 

Sand

Aquifer material used for the column experiments consisted of various sieve sizes of 

Ottawa sand (U.S. Silica, Ottawa, IL). The 

porous media were selected to encompass a 

range in grain sizes, and are designated by 

their median grain size (d50 ) as: 360, 240, 

and 150 μm. Specific properties of the 360, 

240, and 150 μm sands include: the coefficient of uniformity (d60 /d10 ; here 10 and 

60% of the sand mass is finer than d10 and 

d60 , respectively) of 1.88, 3.06, and 2.25; 

and intrinsic permeabilities of 6.37×10-11, 

1.12×10-11, and 4.68×10-12 m2, respectively. 

Ottawa sands typically consist of 99.8% SiO2

(quartz) and trace amounts of metal oxides, 

have spheroidal shapes, and contain relatively 

rough surfaces. An estimate of the pore-size 

distribution for these sands can be obtained 

by using Laplace’s equation of capillarity 

and measured capillary pressure - saturation 

curves presented by Bradford and Abriola 

(2001). 

Electrolyte Solution Chemistry

The background electrolyte solutions utilized in the column studies consisted of 

deionized water with a buffered pH of 10 

achieved with 1.7 mM NaHCO3 and 1.7 mM 

Na2CO3 (Cherrey et al., 2003). This solution 

chemistry was chosen to create a stabilized 

mono-dispersed suspension with the selected 

colloids. In particular, at a pH of 10 quartz 

and iron oxides possess a net negative charge 

(Tipping, 1981; Redman et al., 2004), 

and any attractive electrostatic interactions 

between the colloids and porous media is 

expected to be minimized at this pH. The 

ionic strength (IS) of the eluant, resident, and 

tracer solutions in the transport experiments 

was varied between 6-106 mM using this 

same pH 10 solution by changing the amounts 

of added NaCl, KCl, or NaBr. Unless specifically indicated, the concentrations of resident, 

tracer, and eluant solutions were maintained 

over the course of an experiment.

DLVO Calculations 

Derjaguin, Landau, Verwey and Overbeek 

(DLVO) theory (Derjaguin and Landau, 1941; 

Verwey and Overbeek, 1948) was used to 

calculate the total interaction energy (sum 

of London-van der Waals attraction and electrostatic double-layer repulsion) for our 1.1 

and 3 μm colloids upon close approach to 

quartz surfaces (assuming sphere-plate interactions) for the various solution chemistries 

(pH=10, and IS=6-106 mM). In these calculations, constant-potential electrostatic 

double layer interactions were quantified using 

the expression of Hogg et al. (1966) and 

zeta potentials in place of surface potentials. 

Retarded London-van der Waals attractive 

interaction force was determined from the 

expression of Gregory (1981) utilizing a value 

of 4.04×10-21 J for the Hamaker constant 

(Bergendahl and Grasso, 1999) to represent 

our polystyrene latex-water-quartz system. 

The zeta potential of the quartz at pH 10 and 

for IS ranging from 6 to 106 mM was estimated using results presented in Elimelech et 

al. (2000) and Redman et al. (2004). Zeta 

potentials for our 1.1 and 3 μm colloids in 

the various solution chemistries that were 

used in the DLVO calculations were calculated 

from experimentally measured electrophoretic mobilities using a ZetaPals instrument 

(Brookhaven Instruments Corporation, 

Holtsville, NY). 
54

Batch Experiments 

Batch experiments were conducted by placing 10 g of sand and 10 ml of a known initial 

concentration of colloid suspension into a 

polypropylene centrifuge tubes with the temperature kept at approximately 20° C. Three 

different ionic strength solutions (6, 30, 

60 mM) buffered at pH 10 were used for 

making the colloid suspension. The suspension and sand were allowed to equilibrate for 

2 h by gently rotating the tubes end over end 

(15 rpm) on a tube rotator (Fisher Scientific, 

San Diego, CA). The 2-h equilibration time 

was chosen to mimic the duration of the 

column experiments. A control experiment 

without colloids was also run for measuring the background concentration of colloids 

introduced from the sand. The initial and final 

concentrations of colloids in the suspension 

were determining using a spectrophotometer 

(Perkin Elmer LC95 UV/VIS spectrometer, 

Irvine, CA) after setting the tube to rest for a 

few minutes. All experiments were performed 

in duplicate. 

Column Experiments

Procedures and protocols for the saturated 

packed column glass (15 cm long and 4.8 cm 

inside diameter, equipped with an adjustable 

adapter at the top) experiments are reported 

in detail by Bradford et al. (2002 and 2007), 

whereas details on the unsaturated column 

(10 cm long and 5 cm inside diameter) 

experiments are provided in Torkzaban et 

al. (2008a). The columns were wet packed 

(water level kept above the sand surface) with 

the various sands. For saturated systems, 

the colloid suspension was pumped upward 

through the vertically oriented columns at a 

steady flow rate, after which a three-way valve 

was used to switch to the background solution. 

The unsaturated column experiment was 

designed to accurately establish steady state 

flow and uniform saturation conditions. In 

this case, the sand in the column was drained 

to the desired water saturation level by reducing the inflow water rate to the hydraulic 

conductivity corresponding to that saturation. 

Simultaneously, the pressure head at the 

bottom of the column was gradually reduced 

until unit hydraulic gradient conditions were 

achieved (the only driving force for water flow 

was gravity), which implies a constant capillary pressure and saturation along the column. 

After establishing a specified saturation and 

water chemistry, colloid transport experiments were initiated by introducing a colloid 

suspension and then rinsing with background 

electrolyte. 

Effluent samples from the column experiments were collected and analyzed for colloid 

concentration using a Turner Quantech 

Fluorometer (Barnstead/Thermolyne, Dubuque, 

IA) or a spectrometer (Perkin Elmer LC95 

UV/VIS spectrometer, Irvine, CA). The average 

of three measurements were used to determine 

each colloid concentration (reproducibility was 

typically within 1% of Ci). 

Following completion of the colloid transport experiments, the spatial distribution of 

retained colloids in each packed column was 

determined. The sand was carefully excavated 

into tubes containing excess eluant solution 

of the same IS and pH that was used in the 

transport experiment. The tubes were slowly 

shaken to liberate reversibly retained colloids. 

The concentration of the colloids in the excess 

aqueous solution was then measured with the 

fluorometer. The volume of solution and mass 

of sand in each tube was determined from 

mass balance.

A colloid mass balance was conducted at the 

end of each column experiment using effluent 

concentration data and the final spatial distribution of retained colloids in the sand. The 

calculated number of colloids in the effluent 

and retained in the sand was normalized by 

the total number of injected particles into a 

column. 

Colloid Transport Model for Saturated and 

Unsaturated Systems

The HYDRUS-1D code (Šimůnek et al., 2009) 

was used to simulate colloid transport and 

retention in the column experiments. Details 
55

are given by Torkzaban et al. (2008a). In 

brief, the model includes provisions for transport by advection and dispersion, and two-site 

kinetic colloid retention/release with various 

time and depth dependent blocking functions. 

Colloid retention and release parameters for 

given conditions (e.g., ionic strength, water 

velocity and saturation, porous medium size, 

and colloid size) were obtained by nonlinear 

least squares optimization to the experimental 

data. 

Results and Discussion

Details on the experimental results are 

presented in Bradford et al. (2007) and 

Torkzaban et al. (2008a). In this section we 

focus our discussion on the interpretation and 

implications of these findings. 

Experimental evidence in this study demonstrated that attachment to the SWI was not 

the dominant mechanism responsible for 

colloid retention. The first piece of indirect 

evidence was from the batch experiments 

in which no significant colloid attachment 

to the sand was observed. Additionally, the 

experimental protocol for determining the 

deposition profiles was based upon the rapid 

release of colloids into solutions, and high 

recoveries were not consistent with the low 

detachment rates that were observed in the 

column experiments. If the colloids were 

irreversibly attached to the SWI, they should 

have remained on the grain surface even after 

suspending the sand in the same solution that 

was used for the transport experiments. The 

next piece of evidence is the fact that colloid retention occurred in the column, even 

under conditions when DLVO calculations 

indicate the presence of a substantial energy 

barrier to colloid – SWI interaction. Finally, 

the colloid deposition profiles resulting from 

the column experiments were not consistent 

with first-order attachment-detachment model 

predictions (e.g., non-exponential). 

Non-exponential deposition profiles in 

saturated conditions have been attributed 

to charge variability of the porous media 

(Johnson and Elimelech, 1995), heterogeneity in colloid surface charge characteristics 

(Bolster et al., 1999; Li et al., 2004), and 

deposition of colloids in a secondary energy 

minimum (Redman et al., 2004; Tufenkji and 

Elimelech, 2005b). These factors may have 

been involved; however, these mechanisms 

cannot fully explain the experimental data. 

The experiments were conducted in a solution 

at a pH 10, which should minimize any effect 

of charge heterogeneity on the sand surface 

because the isoelectric points of most metal 

oxides fall below this pH (Elimelech et al., 

2000). The results of our batch experiments 

and DLVO calculations do not support the 

existence of any significant heterogeneity on 

the grain or colloid surfaces. Furthermore, the 

colloids may be held in the secondary minima; 

however, this cannot explain the observed 

depth and sand size dependent deposition. 

Therefore, another retention mechanism must 

also be involved. 

Retention of colloids under unsaturated conditions has also been attributed to partitioning 

at the AWI and/or film straining (e.g. Wan 

and Wilson 1994a; Wan and Tokunaga 1997; 

Saiers and Lenhart, 2003a; Lenhart and 

Saiers, 2002). However, attachment to the 

AWI is not believed to play a significant role in 

the reported unsaturated experiments, as both 

the AWI and colloids were negatively charged 

even in the highest ionic strength solution. It 

has been well documented that hydrophilicnegatively charged colloids are unlikely to 

attach to the negatively charged AWI (e.g. Wan 

and Tokunaga 2002; Crist et al. 2004, 2005; 

Torkzaban et al. 2006a, 2006b). 

It is also unlikely that film straining is playing 

a major role in the reported colloid retention as the 1.1 μm latex colloids are too 

large to be bound within a thin–film. These 

thin water films have calculated thicknesses 

around 50 nm during steady-state water flow 

– over an order of magnitude thinner than the 

diameter of the colloids used in this current 

study. Moreover, film straining is theoretically independent of solution ionic strength 
56

and flow velocity (Wan and Tokunaga, 1997). 

In contrast, notably more colloid retention is 

observed in higher ionic strength solutions and 

for lower water velocities. 

Straining of colloids provides a plausible 

explanation and mechanism for the observed 

colloid deposition behavior. Colloids can 

become trapped in small pores where the 

flow velocity is reduced. These nearly immobile regions include the small pores formed 

by narrow wedges that are defined by grainto-grain contacts, dead-end pores, and the 

three-phase contact line of the solid-water-air 

interface (in case of unsaturated conditions). 

It is proposed that colloids are weakly associated with the SWI interface via the secondary 

energy minimum; hence, they are subject 

to fluid drag forces that can translate and/or 

funnel the colloids to the low velocity regions 

of the pore structure or “straining sites”. 

Earlier studies have observed similar trends by 

fluorescent microscopy and x-ray microtomography that colloids accumulate in the narrow 

region of the pore spaces near the contacts of 

irregularly shaped sand grains under unfavorable attachment conditions (Bradford et al., 

2005, Li et al., 2006a, and Bradford et al., 

2006b). In these studies, pore-space constrictions apparently served as locations for 

colloid retention by straining, whereas few 

colloids appeared to be immobilized far from 

the grain-to-grain contacts. Colloid retention 

in the pore network is further supported by

Hoek and Agarwal (2006) who reported that 

DLVO forces act on colloids up to 5 times 

more in small pores than with a single flat surface. Straining, as the mechanism of colloid 

retention, is also supported by mass balance 

calculations and the fact that once the sand 

is resuspended in the solution and the pore 

structure eliminated, the colloids return to 

solution.

As noted previously, colloids that are weakly 

associated with the SWI via the secondary 

energy minima experience significant hydrodynamic forces (e.g., fluid drag, lift forces) 

due to fluid flow. The shear force acting 

on the colloid surface held in the secondary energy minimum is different than that 

acting on the colloid surface in the bulk fluid. 

Consequently, at close range to the SWI the 

colloids experience a combination of forces 

(hydrodynamic forces, electrical doublelayer repulsion, and London-van der Waals) 

which creates a torque and hence rotation on 

the surface (Bergendahl and Grasso 2000; 

Cushing and Lawler 1998). It is therefore 

hypothesized that colloids associated with 

secondary energy minima can be translated 

to regions of low velocity where there are no 

significant hydrodynamic forces present and 

they can remain attached even by a weak 

attraction force. Based on this argument, 

more colloids will be retained in a secondary 

energy minimum as ionic strength is increased 

and these colloids will subsequently be funneled to the straining sites. It is believed that 

colloids retained in straining sites can aggregate together depending upon the solution 

chemical conditions as it has been observed 

by Bradford et al. (2005), Li et al. (2006), 

and Bradford et al. (2006b). This hypothesis 

can also explain the observed decrease in colloid retention with increasing fluid velocity. In 

this case, enhanced hydrodynamic forces may 

overcome the electrostatic and van der Waals 

interactions and detach colloids from the grain 

surface. It is also possible that the extent 

of stagnation regions decreases as the water 

velocity increases. This is consistent with our 

observations and modeling results presented 

by Cushing and Lawler (1998). 

It was anticipated that the magnitude of 

straining would be enhanced with decreasing sand size and water saturation level due 

to a greater amount of low velocity regions. 

Both of these expected trends were verified 

by our experimental observations. The extent 

of retention of colloids increased with ionic 

strength and soil grain size for all of the various saturation levels tested (Figure 5.1). With 

decreasing soil grain size, the peak effluent 

concentration was lower, indicating a greater 
57

removal of colloids. This is especially true 

for the higher ionic strength conditions when 

very little breakthrough of colloids occurred. 

Comparison of the breakthrough curves for 

all of the ionic strength conditions reveals 

a decrease in peak effluent concentrations 

with water content. All these results provide 

substantial direct and indirect evidence that 

straining is an important removal mechanism 

for the CML colloids in the Ottawa sands 

investigated.

The comparison of experimental and modeled results also suggests that straining is the 

underlying mechanism in colloid deposition in 

both the saturated and unsaturated systems 

tested. The concentration of retained colloids 

enumerated after the column experiments 

was found to exhibit hyper-exponential spatial 

distribution through the length of the column. 

This indicates the deposition coefficient was 

depth-dependent, a trend that has previously 

been associated with straining (e.g., Bradford 

et al., 2002, 2003, and 2006a). 

All the observations discussed above provide 

convincing evidence that straining was the 

primary mechanism of colloid retention in 

our column experiments. The straining rate 

in a given porous medium is apparently a 

complicated mechanism, coupled with such 

parameters as pore size distribution, hydrodynamics, solution chemistry and water content. 

Specific findings are highlighted below. 

• Straining increases in magnitude with 

increasing ionic strength due to an increased 

force (secondary energy minimum) and 

number of colloids that are funneled to and 

retained in small pores formed adjacent to 

grain-grain junctions. 

• Straining increases in magnitude with water 

content due to an increase in the extent 

of stagnant regions of flow within the pore 

structure in lower water content.

• Increasing the flow rate of a system tends to 

decrease the amount of straining as a result 

of the increased fluid drag force that acts on 

weakly-attached colloids on the SWI and also 

decreased flow stagnation regions. 

• The shape of the colloid deposition profile is 

highly sensitive to the physical (grain size, 

water content, and flow rate) and chemical 

(solution IS and pH) properties of a system 

due to the interrelation of these parameters 

on colloid straining.

Additional research is required to better 

understand and quantify the coupling of physical and chemical processes that influence 

colloid straining in saturated and unsaturated 

porous media. This information is believed to 

be essential for predicting colloid transport 

and fate in many natural environments. 

Figure 5.1. Observed and simulated breakthrough 

curves of colloids for various saturation levels in 

360 μm sand at an ionic strength of 6 mM (Figure 5.1a) and 30 mM (Figure 5.1b) 
58

6.0

Mathematical Models to Simulate 

Pathogen and Nutrient Fate

Background

Most models for microorganism retention in 

porous media have been based on filtration 

theory (Yao et al., 1971, Rajagopalan and 

Tien, 1976). These models have typically only 

considered the average pore-water velocity in 

the porous medium and a single first-order 

attachment rate coefficient which predicts that 

the profile of retained colloids in porous media 

will decrease exponentially with distance. 

Under unfavorable attachment conditions, 

these models have commonly been found to 

be inadequate to predict the amount of retention, the shape of the deposition profile, and 

the dependence of colloid retention on velocity, solution chemistry, and grain and colloid 

size (Bradford et al., 2003, 2006a, 2007; 

Tufenkji and Elimelech, 2005ab; Tong et al., 

2005; Li and Johnson, 2005; Li et al., 2005; 

Johnson et al., 2007; Torkzaban et al., 2007 

and 2008a). 

Recent research has demonstrated that colloid 

and microorganism retention in porous media 

does not solely depend on the strength of the 

adhesive interaction (Bradford et al., 2007 

and 2009; Bradford and Torkzaban, 2008b; 

Johnson et al., 2007; Torkzaban et al., 2007 

and 2008; Duffadar and Davis, 2008; Shen et 

al., 2008; Kim et al., 2009ab). Findings suggest that colloid retention in porous media is a 

strongly coupled process that depends on the 

chemistry of the aqueous and solid phases, 

as well as the pore structure and surface 

roughness, the colloid size and concentration, and water velocity. In particular, it has 

been reported that weakly associated colloids 

with the solid-water interface via the secondary minimum or nanoscale heterogeneity may 

experience significant hydrodynamic forces 

due to fluid flow that may result in rolling, 

sliding, skipping, or detachment of colloids 

on/from the collector surface (Bradford and 

Torkzaban, 2008b; Torkzaban et al., 2007 and 

2008b; Duffadar and Davis, 2008). Some 

of these weakly associated colloids can be 

translated and/or funneled by fluid drag force 

to low velocity regions in small pore spaces 

and “eddy zones” which occur near some 

grain-grain contacts and surface roughness 

locations where they can be retained (Bradford 

and Torkzaban, 2008b; Torkzaban et al., 2007 

and 2008b). Indeed, recent experimental 

evidence by Kuznar and Elimelech (2007) 

demonstrates that weakly interacting colloids 

can be translated along the collector surface 

via hydrodynamic forces and be retained in 

regions near the rear stagnation point.

The above literature indicates that, under 

unfavorable attachment conditions, colloid 

retention will be enhanced in the low velocity 

regions of the porous medium. It is therefore 

not surprising that models that consider only 

the average pore water velocity and a single 

attachment coefficient have been found to be 

inadequate to predict colloid retention behavior in many instances. Below, we discuss 

alternative model formulations that can be 

used to account for different colloid retention 

mechanisms in the various regions of the pore 

space. Specific model formations that will be 

discussed include: a combined physical-chemical nonequilibrium model (Leij and Bradford, 

2009), the dual permeability model (Bradford 

et al., 2009), and the stochastic stream tube 

model (Bradford and Toride, 2007). A brief 

description of the applications, implications, 

and limitations of these models to characterize 

colloid/microorganism transport and retention 

will be discussed. Although specific examples 

are provided below for colloid transport in 

homogeneous porous media, these same 
59

models are well designed to simulate colloid 

transport in the field. 

Physical Chemical Nonequilibrium (PCNE) 

Model

The two governing equations for transport of 

a colloid in a porous medium with mobile and 

immobile regions of water are defined as:

2

2

m im m m

m im b m m m m

C C S C C D v

t t t z z

∂ ∂ ∂ ∂ ∂ θ + θ + ρ = θ − θ ∂ ∂ ∂ ∂ ∂

[6.1]

( ) im im

im b m im

C S C C

t t

∂ ∂ θ + ρ = α − ∂ ∂

[6.2]

where θ is the volumetric water content, the 

subscripts m and im refer to the mobile and 

immobile region, C is colloid concentration in 

aqueous phase [N/L3], S is solid phase concentration of colloid from either the mobile or 

immobile region per mass of dry soil [N/M], 

ρb is dry soil bulk density [M/L3], D is dispersion coefficient [L2/T], v is pore-water velocity 

[L/T], α is a physical nonequilibrium (PNE) 

coefficient for mass transfer between mobile 

and immobile region [1/T], z is depth [L], and 

t is time [T]. 

The aqueous phase consists of mobile and 

immobile regions. The solid phase of the soil 

is either in contact with the mobile or immobile aqueous region, colloid may be retained 

or released from the solid phase. Without 

considering the actual mechanism for colloid 

retention, the following equalities hold:

,

,

m im m m

m m D D S S S m im

θ + θ = θ θ ν = θν

θ = θ + =

 [6.3a,b,c,d]

The chemical nonequilibrium (CNE) component is introduced by further distinguishing 

between equilibrium (type 1) and kinetic 

(type 2) retention:

, , 1 2 , , 1 2 , S S S S S S m m m = + im im im = + [6.4a,b]

Colloid retention on type-1 sites is an equilibrium process described by:

S K C S K C m m m im im im , , 1 1 = , , , 1 1 = [6.5a,b]

with K as a “distribution” constant expressed 

as volume of aqueous phase (either the 

“mobile” or “immobile” region) per mass of 

dry soil [L3/M]. Retention on type-2 sites is 

governed by a first-order rate equation:

2

2 2

2

2 2

( )

( )

,

, ,

,

, ,

, m

m m m m

im

im im im im

S K C S

t

S K C S

t

∂ = β − ∂

∂ = β − ∂

[6.6a,b]

The retention rate is proportional to the difference in (eventual) equilibrium and actual 

solid phase concentration with β as the proportionality constant [1/T]. These coefficients 

are likely to be different for retention from the 

mobile and immobile regions. For both regions 

a ratio of “equilibrium” to “total” sites may be 

defined. At equilibrium these follow from the 

solid phase concentrations or the distribution 

coefficients according to:

1 1

1 1

, ,

, ,

f ,

f

m m

m

m m

im im

im

im im

S K

S K

S K

S K

= =

= =

[6.7a,b]

The total concentration, which is given as 

mass per volume of bulk soil, is defined as:

( ) C C C S S T m m im im b m im = θ + θ + ρ + [6.8]

At equilibrium, the total concentration is given 

by:

with 1 

and 

/

T m

b

m im

C RC

R K

K K K

= θ

= + ρ θ

= +

[6.9]

where R is a retardation factor.

The system of mathematical equations that 

needs to be solved is as follows:

2 2

2

2

, , f f m im m m im im

m im b m m im im

m m

m m m m

C C C S C S K K

t t t t t t

C C D v z z

∂ ∂  ∂ ∂ ∂ ∂  θ + θ + ρ + + +   ∂ ∂  ∂ ∂ ∂ ∂ 

∂ ∂ = θ − θ ∂ ∂

[6.10a]

 

( ) im im

im b m im

C S C C

t t

∂ ∂ θ + ρ = α − ∂ ∂

[6.10b]
60

2

2

2

2

1

1

[( ) ]

[( ) ]

,

,

,

,

f ,

f

m

m m m m m

im

im im im im im

S K C S

t

S K C S

t

∂ = β − − ∂

∂ = β − − ∂

 

[6.10c,d]

The problem will be rewritten with only Cm as 

dependent variable. The selected mathematical conditions involve a zero initial condition 

and a time-dependent input Co(t):

( ) ,0 0 C z m = [6.11]

 ( ) ( ) ( ) 0, , , 0 C t C t C t m = o m ∞ = [6.12a,b]

The advection-dispersion equation was 

adapted to separately account for physical and 

chemical nonequilibrium during transport of 

solutes and colloids in porous media. In the 

resulting physical-chemical nonequilibrium 

(PCNE) model the aqueous phase is partitioned into an immobile and a mobile aqueous 

region. Based on equilibrium or nonequilibrium interaction of colloids with the solid 

phase, four types of solid domains may be 

distinguished. 

An analytic solution for the PCNE model 

described above was obtained by Leij and 

Bradford (2009). Expressions for the first 

three time moments of the solutions are presented in Table 6.1. These may be used to 

elucidate the impact of transport parameters 

on the mean, variance, and skewness of breakthrough curves. There are several reasons 

to have analytical tools available to quantify 

PNCE transport. Analytical methods are 

useful to verify numerical methods, elucidate 

the role of different model parameters, and to 

approximately quantify transport such as for 

longer time or spatial scales. The derived analytical tools for the PCNE model also offer the 

flexibility to independently quantify the impact 

of a “chemical” and “physical” nonequilibrium process on colloid transport.

The sensitivity of the breakthrough curves to 

model parameters was illustrated by Leij and 

Bradford (2009) for different types of nonequilibrium using the analytical solution for 

the PCNE model. The simplest cases involve 

the dependency of the curve on the PNE 

parameter θm /θ and the CNE parameters fm

and fim in the presence of physical nonequilibrium. The curves exhibit the characteristic 

features of earlier breakthrough and more tailing with increased nonequilibrium. However, 

the shape of the curves is not very sensitive 

to the parameter values. On the other hand, 

the shape of the curve will change with different combinations of the PCNE parameters α

and β. The additional parameters in the PCNE 

allow greater flexibility to generate different 

types of breakthrough curves. The moment 

results of Table 6.1 were used to predict 

contours of mean, variance, and skewness 

of the colloid breakthrough curve as a function of either log α and log kim,a or θim and log 

kim,a. This information illustrates the utility of 

having a model with independent physical and 

chemical nonequilibrium terms. 

Colloid transport will be affected by physical 

nonequilibrium because pores are not (readily) 

accessible and by chemical nonequilibrium 

due to (different) attachment and detachment 

rates. These nonequilibrium phenomena are 

intertwined because attachment/detachment 

rates depend on the flow regime. The PCNE 

model, with its ability to independently model 

physical and chemical nonequilibrium, was 

therefore applied to four colloid breakthrough 

experiments by Bradford et al. (2002). It 

should be noted that independently quantifying physical and chemical nonequilibrium 

processes will become more useful for 

transport in natural porous media where nonequilibrium phenomena may no longer be 

ignored.

Dual-Permeability Model

Dual-permeability models have commonly 

been used to study preferential and nonequilibrium flow and solute transport in 
61

structured soils and fractured rocks (Šimůnek 

et al., 2003; Gerke, 2006; Šimůnek and 

van Genuchten, 2008). In this case, the 

dual-permeability model partitions the pore 

space into two regions that have fast (fracture) and slow (matrix) rates of advective and 

dispersive transport of solutes. In contrast to 

previous work, Bradford et al. (2009) used the 

dual-permeability model described herein to 

simulate the different colloid retention mechanisms that occur in fast (larger pore spaces 

- region 1) and slow (small pore spaces, dead 

end pores, and grain-grain contact points – 

region 2) velocity regions of homogeneous 

porous media. The dual-permeability model 

has not been previously used to gain insight 

into enhanced colloid retention processes in 

low velocity regions of homogeneous porous 

media. The approach is somewhat analogous 

to multiphase flow and transport models that 

partition the pore space to regions accessible 

for the wetting (small pore spaces) and nonwetting (large pore spaces) phases. The small 

pore spaces in the dual-permeability model 

are assumed to maintain continuity by slow 

flow adjacent to the solid phase, in crevice 

sites near grain-grain contacts, and in small 

pores in the same way as the wetting phase in 

multiphase systems. 

Table 6.1. Expressions for the second- and third-order moments for breakthrough after a Dirac delta input 

according to physical nonequilibrium (PNE), chemical nonequilibrium (CNE), and PCNE models. The firstorder moment is equal to Rz / v for all the models.

Model Moment Expression

PNE M2

2 2 2

3

2 ( ) 2 R im im R z D z  θ 

+ ν +    ν αθν 

CNE M2

2

2

3

2 ( ) 2 R R z D z  

+ ν +    ν βν 

PCNE M2

2

3

2 2

2 2

2

2

( )

im im im im m m , ,

im m

R z D z

R R R

v



 + ν +

 ν

θ θ θ   + +  θ α β β  

PNE M3

3 2 2

2 2 2

5 2

6 (12 6 ) ( ) 2 R im im R R im im R z D D z z D z  θ  θ   + ν + ν +  + ν +  ν αθν ν α   

CNE M3

3

2 2 2 2

5 2

6 1 (12 6 ) ( ) 2 R R R z D D z z D z     + ν + ν +  + ν +  ν βν  ν β  

PCNE M3

3 2 2

2 2 2

5 2

2 2

2 2

6 12 6 2

1 2 1 2 2

( ) ( )

( ) ( ) ,

im im im im

im im im im m m

im im m m

R R R R z D D z z D z

R R R R R D z D z

 θ  θ   + ν + ν +   + ν + + ν θν α ν α   

θ  θ   θ 

+ ν + + + + ν +      β ν β α β ν   β 
62

As indicated in the Background section, colloids that collide with solid surfaces in fast 

regions of the pore space experience different hydrodynamic forces than colloids in slow 

regions. The higher hydrodynamic forces in 

region 1 act to remove colloids from the solid 

surface, thus causing region 1 to be associated with lower rates of colloid retention. 

Colloid exchange in the aqueous phase may 

occur to and from “slow” water (region 2). In 

addition, in this work we also consider the 

potential for colloid exchange on the solid 

phase from fast to slow regions due to either 

rolling or sliding of colloids on the solid surface. The governing equations for water flow 

in the dual-permeability model are well known 

and are available in the literature (Gerke and 

van Genuchten, 1993a,b; Šimůnek and van 

Genuchten, 2008). The corresponding onedimensional dual-permeability equations for 

local scale colloid transport and retention are 

as follows:

( 1 1 ) 1

1 1 1 1 1 1 1 det

w s

w b

C J k C k s

t z w

∂ θ ∂ Γ = − + − θ + ρ ∂ ∂ −

[6.13] ( 2 2 ) 2

2 2 2 2 2 2 det

w s

w b

C J k C k s

t z w

∂ θ ∂ Γ = − − − θ + ρ ∂ ∂

[6.14]

 

( 1 1 ) 1 12 1

1 1 1 1 1 1 1 det

b b

w b

s k s k C k s

t w

∂ ρ ρ = θ − ρ − ∂ − [6.15]

 

( 2 2 ) 1 12 1

2 2 2 2 2 2 det

b b

w b

s k s k C k s

t w

∂ ρ ρ = θ − ρ + ∂ [6.16]

Γ = ω − θ − s (1 w C C ) w1 2 1 ( ) [6.17]

where z [L] is distance, t [T] is time, C1 and 

C2 [Nc L-3; Nc denotes the number of colloids] 

are the liquid phase concentrations of colloids in regions 1 and 2, s1 and s2 [Nc

 M -1] are 

the solid phase concentrations of colloids in 

regions 1 and 2, qw1 and qw2 are the volumetric 

water contents in regions 1 and 2 [-], ρb1 and 

ρb2 are the bulk densities in regions 1 and 2 

[M L-3], k1 and k2 [T-1] are the first order colloid 

retention rate coefficients in regions 1 and 2, 

kdet1 and kdet2 [T-1] are the first order detachment coefficients in regions 1 and 2, J1 and J2

[Nc L-2 T-1] are the total solute fluxes (sum of 

the advective and dispersive flux) for colloids 

in regions 1 and 2, w [T-1] is a coefficient for 

colloid exchange between liquids in regions 1 

to 2, k12 [T-1] is a coefficient for transfer of colloids from solid phase region 1 to 2, and w is 

the ratio of the volume of region 2 to the total 

volume (volume of region 1 + volume of region 

2). The term Gs [N L-3 T-1] accounts for aqueous phase mass exchange of colloids between 

regions 1 and 2. 

Equations [6.13]-[6.17] were written in terms 

of local scale mass balances of regions 1 and 

2. To formulate the equations in terms of the 

total pore space, the mass balance equations 

for regions 1 and 2 need to be multiplied by 

(1-w) and w, respectively. The relationship 

between several variables at the local scale 

and total pore space is provided below. Under 

steady-state flow conditions, the total water 

flux (qt; LT-1) is defined as:

( ) 1 2 1 q w q wq t = − + [6.18]

where q1 (LT-1) and q2 (LT-1) are the local 

scale water fluxes in regions 1 and 2, respectively. Expressions for the total water content 

and bulk density are written in an analogous 

manner as Equation [6.18]. The total flux 

concentration of colloids (Ct, Nc L-3) is given as 

(Šimůnek and van Genuchten, 2008):

2 2 1 1

2 1

1

1

( )

( ) t

wq C w q C C

wq w q

+ − = + −

[6.19]

and the total solid phase colloid concentration 

(st, Nc M-1) as:

2 2 1 1

2 1

1

1

( )

( )

b b

t

b b

w s w s

s

w w

ρ + − ρ = ρ + − ρ

[6.20]

The dual-permeability model outlined above 

has been implemented into the HYDRUS-1D 

computer model (Šimůnek et al., 2007; and 

Šimůnek and van Genuchten, 2008). The 
63

code employs the Galerkin-type linear finite 

element method for spatial discretization of 

the governing differential equations, and a 

finite difference method to approximate temporal derivatives. A Crank-Nicholson finite 

difference scheme was used to solve the 

outlined transport equations sequentially (with 

equations for region 1 solved first). Complete 

details about the numerical techniques are 

provided in the HYDRUS-1D technical manual 

(Šimůnek et al., 2007). For the simulations 

discussed below, a third-type boundary condition was used at the inlet, and a concentration 

gradient of zero was fixed at z equal to the 

outlet depth. The initial concentration of the 

simulation domain was zero. 

Bradford et al. (2009) performed a sensitivity analysis with the dual-permeability model 

parameters. Simulation results indicated 

that low amounts of advective transport to 

low velocity regions of the pore space had a 

dramatic effect on the shape of the retention 

profile, especially near the inlet boundary 

(Figure 6.1). The total water flow rate was 

also shown to have a significant influence on 

the shape of the colloid retention profile near 

the inlet boundary (Figure 6.2). In this case, 

higher water velocities were found to produce 

less colloid retention near the inlet boundary 

because greater amounts of colloids bypassed 

the low velocity regions. Both of these predictions are consistent with experimental 

observations that have been reported in the literature. Away from the inlet boundary, colloid 

retention was controlled by the deposition rate 

in the higher velocity region, and the aqueous and solid phase exchange rates. Example 

figures are shown below.

Published colloid transport and retention 

data that were obtained for unfavorable 

attachment conditions for a range of colloid 

and sand sizes, water velocities, and solution chemistries were described using the 

dual-permeability model. Fitted model parameters exhibited systematic trends with grain 

size, velocity, and solution chemistry. The 

dual-permeability model provided a plausible 

interpretation for the experimental observations, and a reasonable approximation of the 

pore-scale physics controlling colloid retention 

under unfavorable attachment conditions.

Stochastic Stream Tube Model

The CXTFIT program (Toride et al., 1995) 

served as the foundation for our stochastic 

modeling effort. This code includes the analytical solution for the one-dimensional advective 

dispersion equation with one-site kinetic 

chemical nonequilibrium deposition subject to 

various initial and boundary conditions. This 

model formulation is equivalent to the wellknown first-order attachment and detachment 

model that is commonly employed to describe 

colloid transport and deposition (e.g., Harvey 

and Garabedian, 1991; Corapcioglu and Choi, 

Figure 6.1. Plots of simulated breakthrough 

curves (Figure 6.1a) and retention profiles (Figure 

6.1b) when qt was equal to 0.0918 cm min-1 and 

the value of wq2 was 0, 0.001, 0.002, 0.004, and 

0.008 cm min-1. 
64

1996; Bolster et al., 1999; Schijven and 

Hassanizadeh, 2000). This analytical solution 

was used in conjunction with the stochastic 

stream tube model in CXTFIT to explore colloid transport and deposition. Details are 

provided in Bradford and Toride (2007). 

The value of first-order deposition coefficient 

(kd) is typically assumed to be constant in 

colloid transport models. In the stochastic modeling approach, parameters may be 

defined by probability density functions. If kd

is considered to be stochastic, we assume a 

log-normal probability density function (PDF) 

that is defined as:

( ) 2 1

2 2 exp d

d

d d

Y F k

k

  =   − σ π   [6.21]

where σd is the standard deviation of the 

log-normal probability density function, and 

Yd is the normalized log-transformed variable 

defined as:

ln( ) d d

d

d

k Y − μ = − σ

[6.22]

Here μd is the mean value of the log-normal 

probability density function defined as 

μd = ln( < kd > )-0.5σd

2, where < kd

 > is the 

ensemble average of kd. The subscript d is 

used on σd, Yd, and μd to identify parameters 

associated with the deposition coefficient. 

Subscripts r and v are used in a similar fashion to identify parameters associated with the 

release coefficient and the pore water velocity, 

respectively.

If both kd and kr are assumed to be log-normal 

stochastic parameters that are correlated, then 

a joint probability density function is defined 

as:

( ) 2 2

2 2

1 2

2 1 2 1( ) , exp r dr r d d

d r

dr d r d r dr

Y Y Y Y F k k

k k

 − ρ +  = − 

πσ σ − ρ  − ρ 

[6.23]

The parameter ρdr is the correlation coefficient 

between Yd and Yr and is defined as:

( ) 0 0

, dr d r Y Y Y Y F k k dk dk r d d r d r

∞ ∞

ρ = = ∫∫ [6.24]

When Yd and Yr are perfectly correlated then 

ρdr = 1, when they are uncorrelated ρdr = 0, 

and when they are perfectly inversely correlated ρdr = -1.

The mean aqueous and solid phase colloid 

concentrations at a given depth and time can 

also be determined for two log-normally distributed parameters kd

 and kr as:

( ) ( ) ( ) 0 0

, , ; , , C z t C z t k k F k k dk dk d r d r d r

∞ ∞

= ∫∫ [6.25]

and

( ) ( ) ( ) 0 0

, , ; , , S z t S z t k k F k k dk dk d r d r d r

∞ ∞

= ∫∫ [6.26]

where C(z,t; kd, kr) and S(z,t; kd, kr) are 

the aqueous and solid phase colloid 

Figure 6.2. Plots of the simulated breakthrough 

curves (Figure 6.2a) and retention profiles (Figure 6.2b) when wq2 was 0.001 cm min-1and qt

 was 

equal to 0.024, 0.046, 0.091, 0.181, and 0.361 

cm min-1. Other model parameters are provided in 

the manuscript text.
65

concentrations determined from the analytical 

solution of the advective dispersion equation 

with one-site kinetic chemical nonequilibrium. 

The variance in solid phase colloid concentrations is given as < S(z,t)S(z,t) >-< S(z,t) 

> 2 when using the two parameter stochastic 

model. Alternatively, if kd and v are stochastic 

and kr is constant, Equation [6.23–6.26] can 

be rewritten by replacing kr and v. 

Simulation results presented in Bradford 

and Toride (2007) indicate that variations 

in the deposition coefficient and the average 

pore water velocity can both produce hyperexponential deposition profiles. Bimodal 

formulations for the PDF were also able to 

produce hyper-exponential profiles, but with 

much lower variances in the deposition coefficient. The shape of the deposition profile was 

found to be very sensitive to the correlation 

of deposition and release coefficients, and 

to the correlation of pore water velocity and 

deposition coefficient (example simulations 

are shown below in Figure 6.3). Application of 

the developed stochastic model to a particular 

set of colloid transport and deposition data 

indicated that chemical heterogeneity of the 

colloid population could not fully explain the 

observed behavior. Alternative interpretations 

were therefore proposed based on variability of the pore size and the water velocity 

distributions. Figure 6.3. (a) Plot of the relative flux concentration, < vC > /( < v > Ci ), at a depth of 10 cm as 

a function of time when v and kd are both stochastic parameters and values of ρvd = -1, -0.5, 

0, 0.5, and 1. (b, c) Corresponding normalized 

solid phase colloid concentration, < S > /Nic , and 

associated variance after 250 min with depth, 

respectively. Model parameters that were employed 

in these simulations were D = 0.0313 cm2 min-1, 

< v > = 0.313 cm min-1, < kd > = 0.03 min-1, 

kr = 0.001 min-1, σv = 1, and σd = 1.
66

7.0

Summary and Conclusions

Concentrated animal feeding operations generate large volumes of manure-contaminated 

water that are typically stored in lagoons 

before land application. In Chapter 1 we 

review the current level of understanding on 

the environmental impact and sustainability of 

concentrated animal feeding operation (CAFO) 

wastewater reuse in agriculture. Specifically, 

we address the source, composition, application practices, environmental issues, transport 

pathways, and potential treatments that are 

associated with nutrients and pathogens in 

CAFO wastewater. 

When applied to land at agronomic rates, 

CAFO lagoon water has the potential to be a 

valuable fertilizer and soil amendment that 

can improve the physical condition of the 

soil for plant growth and reduce the demand 

for high quality water resources. However, 

excess amounts of nutrients, organics with 

high biochemical oxygen demand, salts, heavy 

metals, pathogenic microorganisms, and pharmaceutically active compounds (antibiotics 

and hormones) in CAFO lagoon water can all 

adversely impact soil and water quality, as has 

been well documented in the literature. The 

current regulatory framework is based on nutrient management plans (NMPs) to ensure that 

lagoon water is applied to agricultural lands at 

agronomic rates to meet the nutrient demands 

of crops. Other lagoon water contaminants are 

implicitly assumed to be retained, inactivated, 

or degraded in the root zone. Additional 

studies are needed to thoroughly test these 

hypotheses. 

Potential problems and weaknesses associated 

with the implementation of NMPs were identified in Chapter 1. One problem for proper 

NMP implementation is due to differences 

in the nutrient composition of lagoon water 

and the nutrient uptake rates by plants. A 

second more critical issue concerns the accurate estimation and delivery of water to meet 

plant water demands. Potential errors in water 

balance may occur as a result of inaccuracies 

in estimates of potential evapotranspiration 

(PET), due to not accounting for all sources 

of plant available water, spatial variability 

of soil hydraulic properties and evapotranspiration (ET), nonuniform irrigation water 

application, and preferential water flow that 

bypasses the root zone. If water in excess of 

PET is applied (due to irrigation inefficiency or 

unforeseen natural causes) to or preferential 

water flow occurs at specific locations on a 

NMP site, lagoon water contaminants may also 

be transported below the root zone towards 

groundwater resources. Of special concern are 

contaminants that have limited ability to sorb 

to solid surfaces such as nitrate, or contaminants that can be associated with the mobile 

colloidal fraction of soil solution. Water in 

excess of PET will also lead to potential surface water contamination issues, especially for 

contaminants that are associated with sediments in runoff water such as phosphorus. If 

water application (from all water sources) is 

less than or equal to PET and preferential flow 

does not occur, then all lagoon water contaminants should remain in the root zone. In 

this case, potential environmental problems 

may occur due to accumulation of salts and/or 

survival of pathogens. 

Mitigation of the environmental risks associated with the reuse of CAFO lagoon water 

on agricultural lands may require planning 

to prevent contamination, as well as various 

lagoon water treatments before use in NMPs. 

Prevention measures may include the wise 

choice of locations for CAFOs, proper manure 

management and modification of animal 

diets to minimize specific contaminants (e.g., 

pathogens). Potential treatments to CAFO 
67

lagoon water include: use of solid-liquid separators; optimization of the design of lagoon 

water storage basins to remove sediment loads 

and nutrients (aerobic compared to anaerobic); and the adaptation of various municipal 

wastewater treatments such as chemical flocculants and/or soil treatments. 

A nutrient management plan (NMP) was 

implemented in a semi-arid environment 

on a triticale and sorghum rotation during 

2007 and on alfalfa during 2008, where 

nitrogen (N) was assumed to be the primary 

environmental concern. Chapter 2 discusses 

extensive research that was conducted to 

characterize flow and transport properties at 

this site. Chapters 3 and 4 discuss results 

pertaining to nutrients and indicator microorganisms, respectively. The NMP field site 

was designed to accurately measure the water 

and N mass balances in the root zone and 

to determine the fate of N, phosphorus (P), 

potassium (K) and other salts. Cyclic and 

blending dairy wastewater (DWW) application strategies, varying in nutrient application 

timing, were investigated during 2007. Only 

minor differences were found between these 

two strategies, therefore the key findings discussed below were valid for both conditions. 

This NMP study was initiated under the conditions of high levels of soil organic N, which 

may be representative of historic manure and 

DWW application at many sites. Such conditions require that the soil organic N reservoir 

be taken into account in the N mass balance 

and this may induce difficulties in conservative NMP implementation that is protective 

of the environment due to: i) difficulties in 

estimation of mineralization rates and its 

spatial variability; ii) delayed availability of 

the organic N for plant uptake; iii) DWW can 

only be applied to meet a fraction of plant 

N uptake; and iv) continuous mineralization 

and potential nutrient leaching during fallow 

periods. 

Based on these insights we implemented a 

conservative NMP in 2007 that depleted the 

soil organic reservoir. This was achieved by 

applying only a fraction of the plant N uptake 

with DWW and using DWW that was treated 

to remove most of the suspended solids content (SSC). One advantage of lowering the 

SSC of DWW is the ability to irrigate using 

water application systems with high water use 

efficiency (sprinkler or drip). The outcome 

of this transition was an accurate application 

of plant available inorganic N that minimized 

the migration of nutrients below the root zone. 

After the soil organic reservoir is depleted, the 

total amount of DWW that can be applied per 

crop is expected to increase. 

In contrast, a more aggressive NMP (higher 

N application rates) implemented in 2008 

demonstrated potential problems associated 

with the use of treated DWW to grow alfalfa. 

Specifically, the soil organic N reservoir 

increased and leaching of nitrate below the 

root zone (15%) increased during the multicut alfalfa growing season, in spite of the 

much larger plant uptake of N by alfalfa than 

the cereal rotation in 2007. These observations were attributed to fixation of atmospheric 

N, increased root density, and applying a 

higher fraction of plant N uptake with DWW. 

The precise water application in conjunction 

with the high content of salts in the DWW 

caused salt accumulation in the root zone over 

time. The measured values represent the salt 

load under a conservative NMP approach that 

applied only a fraction of the total N that was 

required by the plant with DWW. If 100% of 

the plant N had been applied by DWW, then 

the accompanied salts would increase the 

ECw in the root zone to higher levels. High 

salt levels in the root zone may restrict plant 

growth, and accordingly water and nutrient 

uptake. If this reduction is not considered at 

NMP sites, additional leaching and contaminant migration may occur. An optimum point 

likely exists between the benefits of nutrient 

application and the detrimental effects of 

salt accumulation on crop yield. This point is 

strongly dependent on the salt tolerance of the 
68

crop, suggesting that NMPs should use only 

salt tolerant crops. 

The leaching timing of excess of salts below 

the root zone is a crucial aspect in NMP 

design. In this study, the soil profile was found 

to be depleted from inorganic N at the end of 

the 2007 growing seasons, yet mineralization 

of organic N was observed to continue during 

fallow periods, and high levels of N-(NO2+NO3) 

(nitrogen from the sum of nitrite and nitrate) 

were found in the soil profile at the beginning of the sorghum 2007 and alfalfa 2008 

seasons. This fallow period poses a greater 

contamination risk to ground water due to 

planned (pre-irrigation) or natural (precipitation) water application in excess of ET. In 

order to minimize nutrient leaching, pre-irrigations should be scheduled at the end of the 

growing season. 

A comprehensive measurement of N mass 

balance in the root zone requires information 

on both organic N (soil reservoir) and inorganic N (DWW) sources. During the growing 

seasons, the major N sink was plant uptake 

and losses to the atmosphere during irrigation. Atmospheric losses may be minimized 

by applying DWW during times that are associated with low potential ET (i.e. early morning), 

or through drip systems that minimize the 

exposure of DWW to the atmosphere. 

Differences in the concentration ratios of N, 

P, and K between DWW and plant uptake may 

lead to accumulation of P and K in the root 

zone. However, the conservative NMP implemented in this study applied only a fraction 

of the plant N uptake with DWW because it 

accounted for the organic N reservoir in the 

soil. Similarly, in 2007 the P in DWW was 

applied in deficit to plant uptake, and this 

resulted in a depletion of soil P. In contrast, 

K was applied in excess and needed to be 

leached with the salts.

Additional longer term research is needed 

on both cereal and legume crops to optimize 

NMPs, the soil organic reservoir, and irrigation methods to simultaneously: (i) Prevent 

excessive nitrate leaching even after multiple 

years of implementation and through crop 

rotations; (ii) Maximize the plant uptake of N 

in DWW per unit land; (iii) Provide economic 

returns on the forage harvest; and (iv) Prevent 

excessive salt build up.

NMPs implicitly assume that pathogenic 

microorganisms in the lagoon water will be 

retained and die-off in the root zone. To test 

this assumption with regard to groundwater 

protection, the transport and fate of indicator 

microorganisms were monitored during winter 

and summer growing seasons at the NMP field 

site. These results are discussed in detail in 

Chapter 4.

In order to efficiently implement the NMP the 

dairy wastewater was treated (screen, sedimentation tank, and sand filter) to minimize 

the organic load in the wastewater. This treatment also produced close to a 2-log reduction 

in concentrations for Enterococcus, fecal 

coliform, total E. coli, and somatic coliphage. 

When well-water and treated wastewater were 

applied to the field site to meet ET and plant 

nutrient requirements, little advective transport of the indicator microorganisms occurred 

below the root zone (60 cm). The remaining 

concentrations of these indicator microorganisms in the root zone died-off during the 

winter and summer growing seasons. These 

observations support the hypothesis that a 

well-designed and implemented NMP at this 

site will protect groundwater supplies from 

microorganism contamination. 

Additional experiments were conducted in the 

laboratory to better quantify microorganism 

transport and survival in the field soil. Batch 

survival experiments revealed much more 

rapid die-off rates for the bacterial indicator 

microorganisms in native than in sterilized 

soil, suggesting that the biotic factors played a 

dominant role in survival behavior. Saturated 

column experiments with packed field soil, 

demonstrated much greater transport potential 

for somatic coliphage than bacterial indicators 

(Enterococcus and total E. coli). Retention 
69

rates for the indicator microorganisms were 

not log-linear with depth (not first-order with 

respect to concentration), demonstrating the 

complexity of the microorganism transport 

behavior. A worst case transport scenario 

of ponded infiltration on a large undistributed soil column from the field was also 

investigated. Concentrations of the indicator microorganisms were not detected in the 

column outflow and in the soil at a depth 

of 65 cm. The remaining concentrations of 

the indicator microorganisms in the column 

rapidly decreased toward low concentrations 

within 1 week at a temperature of 25°C. Both 

of these observations were consistent with 

field NMP results and conclusions. 

Results from field and laboratory experiments 

demonstrate that the fate of microorganisms 

at NMP sites will depend on both transport and survival characteristics. Although 

transport and survival of microorganisms in 

NMP soils were shown to be complicated and 

are likely to be site specific, a few recommendations for NMP implementation can 

be developed. The transport potential of 

microorganisms can be significantly reduced 

by minimizing water leaching below the root 

zone and surface water runoff. This can be 

achieved by: (i) precise estimation of ET

rate; (ii) uniform application of wastewater; 

and (iii) selecting water application timing 

and quantities based on considerations of 

soil permeability and ET. Special caution is 

warranted in coarse textured and structured 

soils and during water flow transients where 

enhanced microorganism transport potential 

has been reported in the literature (Natsch et 

al., 1996; Bradford et al., 2003; and Saiers 

et al., 2003). Survival characteristics of 

microorganisms are also likely to be site specific, but may be quantified through relatively 

simple batch type experiments that mimic 

natural conditions. Timing of water application should allow for adequate die-off of 

microorganisms before leaching the root zone 

by irrigation or natural precipitation. Finally, 

the potential for groundwater contamination 

will increase with shorter travel times and 

distances. The water table depth is therefore 

another important consideration for environmentally protective NMPs. Implementation 

of these NMP recommendations will undoubtedly be technically challenging, and may not 

be economically feasible in some instances. 

Nevertheless, these recommendations provide 

guidance to minimize the potential risks of 

pathogen contamination of water resources at 

NMP sites. 

In Chapter 5 we discuss experimental and 

theoretical studies that were undertaken to 

explore the coupling of physical and chemical mechanisms of colloid retention under 

unfavorable attachment conditions (pH=10). 

Negatively charged latex microspheres (1.1 

and 3 μm) and quartz sands (360, 240, 

and 150 μm) were used in packed column 

studies that encompassed a range in suspension ionic strengths (6-106 mM), Darcy 

water velocities (0.1-0.45 cm min-1), and 

water saturations (40-100%). DerjaguinLandau-Verwey-Overbeek (DLVO) calculations, 

moment analysis, and batch results suggest 

that attachment of colloids to the solid-water 

and air-water interfaces was not a significant 

mechanism of deposition for the selected 

experimental conditions. Breakthrough curves 

and hyperexponential deposition profiles were 

strongly dependent on the solution chemistry, 

the system hydrodynamics, and the colloid 

and collector grain size. A mathematical 

model, accounting for time- and depthdependent straining, produced a reasonably 

good fit for both the breakthrough curves and 

final deposition profiles. Greater deposition 

occurred for increasing ionic strength, lower 

flow rates and water saturations, and larger 

ratios of the colloid to the median grain diameter. Increasing the solution ionic strength is 

believed to increase the adhesive force and 

number of colloids in the secondary minimum 

of the DLVO interaction energy profile. These 

weakly associated colloids can be funneled to 

small pore spaces formed adjacent to graingrain junctions and the air-water-solid triple 
70

point. For select systems, the ionic strength 

of the eluant solution was decreased to 6 mM 

following the recovery of the breakthrough 

curve. In this case, only a small portion of 

the deposited colloids was recovered in the 

effluent and the majority was still retained in 

the sand. These observations suggest that 

the extent of colloid removal by straining is 

strongly coupled to solution chemistry.

Experimental research has demonstrated 

that much greater microorganism retention is 

possible in the smallest regions of the pore 

space that are associated with lower flow 

rates. We have therefore developed models 

that are based upon physical and chemical 

nonequilibrium (PCNE), dual permeability, 

and stochastic stream tube formulations. This 

research is discussed in Chapter 6. Although 

specific examples were provided for colloid 

transport in homogeneous porous media, these 

same models are well designed to simulate 

microorganism transport and retention in the 

field at NMP sites.

The PCNE model partitions the pore space 

into “mobile” and “immobile” flow regions 

with first-order mass transfer between these 

two regions (i.e, “physical” nonequilibrium or 

PNE). Partitioning between the aqueous and 

solid phases can either proceed as an equilibrium or a first-order process (i.e, “chemical” 

nonequilibrium or CNE) for both the mobile 

and immobile regions. An analytical solution 

for the PCNE model was obtained using iterated Laplace transforms. The PCNE model 

allows greater flexibility to describe experimental breakthrough curves and retention profiles 

than the traditional CNE model. Expressions 

for moments and transfer functions were 

developed to facilitate the analytical use of the 

PCNE model. 

The dual-permeability model accounts for 

different rates of advective and dispersive transport, as well as first-order colloid 

retention and release in fast and slow velocity regions of the pore space. The model 

also includes provisions for the exchange 

of colloids from fast to slow regions in the 

aqueous phase and/or on the solid phase. A 

sensitivity analysis performed with the dualpermeability model parameters indicated that 

low rates of advective transport to low-velocity 

regions had a pronounced influence on colloid 

retention profiles, especially near the inlet. 

The stochastic model for colloid transport 

and retention is based on the conventional 

advective dispersion equation that accounts 

for first-order kinetic deposition and release 

of colloids. One or two stochastic parameters 

can be considered in this model, including 

the deposition coefficient, the release coefficient, and the average pore water velocity. 

In the case of one stochastic parameter, the 

probability density function (PDF) is characterized using lognormal, bimodal log-normal, 

or a simple two species/region formulation. 

When two stochastic parameters are considered, then a joint log-normal PDF is employed. 

Simulation results indicated that variations in 

the deposition coefficient and the average pore 

water velocity can both produce hyperexponential deposition profiles. Bimodal formulations 

for the PDF were also able to produce hyperexponential profiles, but with much lower 

variances in the deposition coefficient. 

Quality Assurance

This project was designed to evaluate and 

select basic options, and/or to perform preliminary assessment of unexplored areas with 

regard to NMPs. A quality assurance plan was 

followed in all experimental phases of this 

study. The data was obtained using standard 

physical, chemical, and/or microbiological 

laboratory measurements, analytical procedures, bench studies, and field evaluations 

outlined in this report. The data presented 

in this report met the project’s data quality 

requirements. Conclusions and recommendations made in this report are supported by 

this data, and are scientifically, but not legally 

defensible. Caution is warranted when extrapolating this information and findings to other 

conditions. 
71

8.0

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