Methodology and Interpretation

Total Ammonia Concentration Predictions (mg/L)

Methodology

The 244 water-quality sampling locations were used to delineate 244 subwatersheds. The study area can be depicted as a grouping of the 244 subwatersheds, each of which contains a single hydrologic outlet (sometimes called a "pour point"). The 244 subwatersheds facilitate the mapping of landscape metrics in landscape reporting units. The 244 subwatersheds provide the basis for the statistical development of water-quality vulnerability indicators. It is important to understand that some of the 244 subwatersheds are nested completely within other larger subwatersheds, and thus the total area of the 244 subwatersheds exceeds the total study area. The value of using this unconventional view of the landscape is that the cumulative effects of landscape condition on water quality can be assessed, thereby increasing the predictive power of any determined relationships between land-cover and water-quality parameter. Thus, in the two- dimensional metric and indicator maps, solely a portion of some larger subwatersheds are shown, but in the digital browser there is an additional capability for viewing the nested (i.e., stacked) subwatersheds and viewing a synopsis of the landscape metrics and other pertinent information for all subwatersheds.

The Missouri land-cover data set and the Arkansas land-cover data set were imported and evaluated using Imagine image processing software (ERDAS v. 9.0). While evaluating the forest class in the Missouri land-cover data, it was discovered that the original classification did not fully capture the forest area. The forest areas that were lacking where filled in by classifying and merging a new forest layer from multiple Landsat Thematic Mapper (TM) imagery 'scenes'. The new Missouri land-cover was superimposed onto 2003 county Digital Ortho Quarter Quadrangles (DOQQ). The preliminary land-cover map was edited and updated to match the 2003 DOQQs, using aerial photographic interpretation techniques. Edits were made to the forest, urban, water, and agriculture classes. The Arkansas land-cover was then superimposed onto the DOQQs and updated. The updated state land-cover maps were exported as ArcInfo (ESRI ArcGIS v. 9.0) Grids. Each land-cover classification schema was aggregated to meet the project's classification scheme requirements. The land-cover grids for Missouri and Arkansas were merged to create a unified land-cover map for 2003, and used for statistical analyses of landscape metrics (i.e., Landscape Metric Maps - Analysis by Decade Status, and Landscape Metric Change Maps - Analysis by Decadal Interval).

For each of the selected sites, the watershed support area was delineated and a suite of landscape metrics was calculated. A total of 46 landscape metrics were tested among watersheds. Measured total phosphorous, total ammonia, and E. coli solely existed in 18, 6, and 15 sites, respectively. Landscape metrics were for year 2003 and surface water constituents were averaged over a period of 1997-2002. Each of the surface water constituents from the above sites was used in a Partial Least Squares analysis (PLS) to predict water-quality values for all of the 244 subwatersheds.

PLS is a multivariate analysis technique that permits analysis and prediction for data sets with missing values, with collinearity, and with a relatively small number of observations (for additional information about PLS see references, below). In the PLS analyses, both data sets (e.g. water and landscape variables) are first centered and scaled. A linear combination is composed on the independent variables (T = L_o W; T is the score and W is weight) forming a number of orthogonal latent variables [T] that are less in number (dimensions) than that of the original landscape variables. The linear combination in [T] is formed so that the covariance between [T] and the linear composition of the dependent variables are maximized (T& U; U = B_o V; U is the score and V is weight). Prediction of both water and landscape data will be via regression on the common latent variables (T). Modeling and prediction in PLS, therefore, is not solely based on the conditional distribution of the predictors (water variables) in the presence of independent variables (landscape variables), instead it accounts for both landscape and water together through [T].

PLS produces n-1 factors, with each factor containing a pair of scores (T_i, U_i). Linear combinations on each data set are called factors. The above was the extraction of the first factor. PLS extracts the second factor using the residuals from the first and finds the linear combinations of both data sets such that their covariance is maximized. This process is repeated by taking residuals from the previous factor, producing n-1 factors, where n is the number of observations. For example, if the number of sites (observations) is 89, then 88 factors will be produced. Not all of these factors are significant using the Cross Validation (CV) method; only the significant factors are used in the final model. When applying CV, data set is divided into groups (5 to 9 groups; see references in Nash et al., 2005). The fitted models are tested using the test data sets and the predicted values are compared with that of observed using PRESS (Predictive Residual Sum of Square) to assess the predictive ability of the model. SAS gives the root means PRESS and its significant level (the lower the value, the better the model is).

After defining the significant PLS factors; scores, weights and VIP (Variable Influence on Projection) are used to examine the strength of the relationship, irregularities and the contribution of the independent variable (landscape) in the model. If VIP for an independent variable is small in value, it implies that variable has a relatively small contribution to the model and may be deleted from the model. It was indicated VIP values of less than 0.8 are considered to be small. The quality of the model was determined by examining the residuals for both the response and the landscape variables. An examination of any possible outliers using residuals was carried out to finalize the fitted PLS model. SAS was used for statistical analyses.

Interpretation

The total ammonia PLS model resulted in one significant factor explaining 93% of the variability in surface water total ammonia concentrations. Riparian forested areas (e.g., forested areas within 120 meters of streams) are negatively correlated with total ammonia, whereas riparian urban areas are positively correlated with surface water total ammonia concentrations.

(The following excerpt is from Ecological Restoration: A Tool To Manage Stream Quality - U.S. EPA Report Number EPA/841/F-95/007)

Ammonia (NH₃ and NH₄⁺) is present in variable concentrations in many surface and ground water supplies. A product of microbiological activity, ammonia when found in natural water is regarded as indicative of sanitary pollution.

Ammonia is rapidly oxidized by certain bacteria, in natural water systems, to nitrite and nitrate, a process that requires the presence of dissolved oxygen. Ammonia, being a source of nitrogen is also a nutrient for algae and other forms of plant life and thus contributes to overloading of natural systems and a cause of pollution. Ammonia can be toxic to aquatic life and has been found to be a source of toxic effects to aquatic life in some streams. Ammonia, an inorganic form of nitrogen, is a product of the metabolism of organic nitrogen and the biological conversion by bacteria of nitrate to ammonia in anaerobic waters and sediments. Inadequately treated municipal wastewater, agricultural runoff, groundwater contamination by fertilizer, storm water, and feedlots are potential sources of ammonia and nitrate to streams.

The un-ionized form of ammonia exists in equilibrium with the ammonia and hydroxide ions. The reaction occurs rapidly and is controlled by pH and temperature. Monitoring and water-quality models usually report total ammonia, and the un-ionized fraction must be estimated.

Ammonia is present in water in two forms, un-ionized (NH₃) and ionized (NH₄⁺) ammonia. Of these two forms of ammonia, NH₃ has relatively high toxicity and NH₄⁺ has relatively negligible toxicity. The proportion of NH₃ is determined by the pH and temperature of the water: As pH or temperature increase, the proportion of un-ionized ammonia and the toxicity increases.

Any instream, riparian, or upland restoration practice that decreases pH or temperature will decrease the potential toxicity of ammonia to aquatic life in streams:

• Restoring or enhancing the re-aeration potential of the stream can reduce ammonia concentrations by providing more DO for biological oxidation of ammonia to nitrate and by increasing the volatilization of ammonia into the atmosphere. The re-aeration potential can be increased by constructing small drop structures, riffles, and other structures that increase turbulence and mixing.
• Restoring wetlands that will intercept nutrients, thus reducing plant growth and photosynthesis in the stream, thereby decreasing pH levels, and also reducing ammonia concentrations, both of which will reduce ammonia toxicity.
• Re-establishing trees and bushes along stream banks to reduce incident sunlight and water temperature and trap nutrients, thereby reducing aquatic plant growth and photosynthesis; and
• Restoring stream depth and re-establishing undercut banks and narrowing stream width to reduce aquatic plant growth and decrease water temperatures.
• Reducing nonpoint source inputs of nutrients (e.g., nitrogen, phosphorus) to streams.

The decline of pH in response to these restoration measures is adequate to mitigate the accumulation of high concentrations of ammonia. However, the pH shift is generally not large enough to present a problem for increased mobilization of metals (e.g., aluminum, selenium, arsenic, mercury) from sediments. Generally, these metals are mobilized at a pH much lower than those associated with ammonia toxicity.

References

Helland, I. S., 1988. On the structure of partial least square regression. Commun. Statist. Simula. 17(2), 581-607.

Lindberg, W., Persson, J-A, and Wold, S., 1983. Partial Least-Square method for spectrofluorimetric analysis of mixture of humic acid and lignisulfonate. Anal. Chem. 55, 643-648.

Nash, M.S., Chaloud, D., and Lopez, R.D., 2005. Multivariate Analyses (Canonical Correlation Analysis and Partial Least Square, PLS) to Model and Assess the Association of Landscape Metrics to Surface Water Chemical and Biological Properties using Savannah River Basin Data. Untied States Environmental Protection Agency. EPA/600/X-05/004. 82pp.

SAS (SAS Institute), 1998. Version 9 User's Guide. SAS Institute. Inc., Cary, NC.

Wold, S., 1995. PLS for multivariate Linear Modeling. In: H. van de Waterbeemd (Editor), Chemometric methods in molecular design methods and principles in medicinal chemistry. Verlag-Chemie, Weinheim, Germany, p.195-218.

Quantile: Each class contains an approximately equal number (count) of features. A quantile classification is well-suited to linearly distributed data. Because features are grouped by the number within each class, the resulting map can be misleading, in that similar features can be separated into adjacent classes, or features with widely different values can be lumped into the same class. This distortion can be minimized by increasing the number of classes. For continuity of the browser content, and consistency among maps, legend gradients are from higher values (red) to lower values (green).

Metric input GIS data:

• 244 Subwatersheds - Metadata

• 2003 Land-Cover - Metadata

• Water-quality sampling locations - Metadata