### TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters



## 4-1 4. 

# Chapter 4: Methodology for Deriving U.S. EPA’s Proposed Criteria

for Canals

There are thousands of miles of canals in Florida, particularly in the southeastern part of the

state. Canals are artificial wate rways that are the result of modi fications to existing rivers or streams, or waters that have been created for various purposes, incl uding drainage and flood control (stormwater management), irrigation, navigation, and recreat ion. These canals also allow for the creation of many waterfront home sites in Florida. Ecosyste ms that existed in rivers and

streams before their modification into canals ar e altered. These changes can affect fish and wildlife and plant growth. Newly cr eated canals may have a tendency to fill with aquatic plants. Canals in south Florida vary gr eatly in size and depth. They ca n be anywhere from a few feet wide and a few feet deep to hundreds of f eet wide and as deep as 30 to 35 feet.

South Florida canals vary in their hydrology a nd behavior due to th eir size, function, and seasonality. Shallow canals with slow water flow have poor turnover of water and little flushing. Large canals also can have low flow and turnove r during the dry season. In contrast, during the wet season these same large canals are flowing systems that quickly move large volumes of water, as they were designed to. Excess nutrien ts in canals, in combination with poor water

circulation and decreased levels of dissolved oxygen, can lead to accelerated eutrophication and adverse impacts on other forms of aquatic life such as fish and other aquatic animals. In these canals, the accumulation of decaying organic matter on the canal bottom can also adversely affect healthy aquatic ecosystems.

South Florida canals are highly managed waterway s. Some canals are prone to an overabundance of aquatic plants. Without regular and freque nt management, dense vegetation can clog the waterways, making navigation di fficult and slowing the movement of water through the canal system. This can interfere with flood control, boating, and fishing. A quatic plants (like plants in the terrestrial environment) res pond and grow when fertilized with nutrients such as phosphorus and nitrogen, and thus nutrient runoff into canal s is likely a significant contributor to both nuisance algal blooms and clogging of canal systems by aquatic plants.

The U.S. Environmental Protection Agency (U.S. EPA) (USEPA 2000a) describes several general approaches, with certain op tions, that can be used to devel op numeric nutrient criteria for streams. In all cases, consistent with the Clean Water Act (CWA), the objective is to determine a level of nutrients that will provide protection of aquatic life both in-stream and downstream of a point of application.

One approach that EPA recommends is the refe rence condition approach. With this approach, a

reference condition in term s of nutrient concentrati on is empirically determined from a statistical

distribution of nutrient data that can be assumed to inherently protect aquatic life. One option used in the reference condition approach i nvolves setting criteria based on an inclusive

distribution of nutrient values ob tained from minimally disturbed reference sites in a designated ecoregion (based on climate and geology). In this case, U.S. EPA recommends using an upper percentile value of this distribution to repres ent a level of nutrient concentration that will inherently protect aquatic life. This methodology will ensure th at nutrient concentrations associated with biologically healthy, well-balanc ed communities would be considered acceptable

### TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters



## 4-2 as protective of those communities. Lacking refe rence or benchmark canal sites, U.S. EPA is proposing criteria based on the 75 th 

## percentile from all available fr eshwater canal data from sites located in waterbody IDs (WBIDs) that have not been identified for nutrient impairment.

It is noted that this approach is an inferen ce model and does not definitively demonstrate that exceeding the threshold established by the dist ribution of reference s ites results in harm (impairment) to the aquatic lif e in a particular waterbody. Multiple factors, such as water velocity, residence time, availabil ity of the other nutrient, presen ce of grazers, av ailability of light (due to tree cover and/or water transparency), and availability of suitable habitat, can strongly influence the expression of biological responses to nutr ients across waterbodies. The “minimally affected reference water approach” iden tifies concentrations that are presumed to be inherently protective of the waterbody because those concentrations are associated with demonstrated minimally disturbed system s with healthy biological communities.

Because soil or substrate type at the bottom of a canal can influence the nutrient cycling and relationships between the observed biological re sponse and the total phosp horus (TP) and total nitrogen (TN) levels in canals, U.S. EPA used data on soil types in s outh Florida, along with knowledge of the Everglades Agricultural Ar ea (EAA) and the Evergl ades Protection Area (EvPA), to subdivide the canal ar eas for criteria derivation (see Figures 4-1a and 4-1b). Thus the first step in this and other analyses was to group canals and canal data by soil type. The four groupings are the histosol and entisol soils of th e EAA; histosol and enti sol soils of the EvPA; spodosol and alfisol soils and ar eas west of the EvPA and EAA (hereafter, West Coast); and spodosol, entisol, and alfisol soils and areas east of the EvPA a nd EAA (hereafter East Coast).

U.S. EPA performed the following steps to process the available data:

 FDEP provided the “SFWMD Canal Stream” data base for freshwater canals electronically. The database included 1,291,363 results for 2,174 sites. Similarly, FDEP provided “Broward County Canal Data,” which was an Excel sheet that contained 49,056 results associated with 43 sites. Miami-Dade County provided its data, which included 293,159 results for 175 stations. FDEP provided a STORET retrieva l of Miami-Dade (Organization ID = “21FLDADE”) data as well. The STORET retr ieval included 303,956 results for 1,041 sites.

 U.S. EPA performed an extensive comparison of the FDEP-supplied Miami-Dade data with the data directly from Miami-Dade County. This comparison included a review of site locations, sampling dates, and parameters analyzed. Although the FDEP-supplied data included data for more sites, U.S. EPA found that 878 of these sites were not located in canals or did not include key parameters of interest (e.g., no nutrient data). The remaining 163 sites coincided with the Miami-Dade-supplied data set, but usually had a shorter period of record, with the Miami-Dade-supplied data set supplying data from 1998 to the present. For those sites with coincident sampling dates, selected results were compared and found to be identical. Based on this assessment, the FDEP-supplied Miami-Dade data were set aside and not

considered further. 3

3 It is noted that FDEP does not have a record of auditing DERM’s laboratory or its contract laboratorie s. The DERM laboratory did participate in the Everglades round robin inter-laboratory phosphorus comparab ility exercises (Ken Weaver, personal communication, October 12, 2009).

### TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters



## 4-3  The remaining 2,392 sites (South Florida Wa ter Management District (SFWMD): 2,174, Broward: 43, Miami-Dade: 175) were attributed with ecoregions and WBID. Of these sites, 994 were not located in the Everglades ecoregion and were removed from further analysis. In addition, 17 sites were dropped because of mi ssing or poor location data; 101 sites were removed because they reported only “marine” matrix samples; 88 sites were dropped because more than 5% of the specific conductiv ity results were greater than 3,000 μS/cm; 4 

## and 498 sites were excluded because they were associat ed with stormwater treatment areas, marshes, wetlands, class IV canals, private pump stati ons, etc. and not applicable to the current analysis. 5 

## Finally, 163 sites were dropped because they were from Lake Okeechobee or nearby canals that might be expected to behave more like a lake than a freshwater canal.

 Overall, 531 sites were maintained for furthe r analysis, and they include samples collected from 1973 through 2009. Approximately two-thirds ( 64%) of the samples were collected from 1999 through 2009. The samples are uniformly spaced throughout the year with each month having from 7% to 9% of the total samples. Considering the geographic diversity of the site locations and the temporal sp read (both yearly and seasona lly), these data take into consideration temporal, seas onal and spatial variability.

 Using a geographic information system (GIS), site locations were attributed to one of the four canal regions. Because of the complexity of th e existing canal network, all sites near or along the borders of the four canal regions were revi ewed manually to ensure that sites were placed in the correct region. The result of these efforts is displayed in Figure 4-2.

 Results with data qualifiers indicating es timated results—value not accurate, blank contamination, laboratory protocol issues, etc.––were deleted from the analysis. One-half the

detection limit was used for results where the data qualifier indicated not -detected or the result was reported as “<.” 6 

## The sample type was also review ed, and results with the following samples types were deleted: rainwater samples, well water samples, sediment samples, typha (cattails), cladium (sawgrass), and “NOB No Bottle Sample.” Re sults with questionable units were also deleted. For the Broward County Canal data, replicat es were averaged.

 In the analysis of benchmark sites in northern Florida, FDEP removed observations greater than the upper adjacent value. 7 

## That approach was considered fo r the “all-streams” analysis in northern Florida, but it was ultimately abandoned in lieu of a simple screening threshold of 15 mg/L and 100 mg/L for TP and TN, respectiv ely, to remove erroneous data. The same approach of deleting samples with a TP or TN concentration greater 15 mg/L or 100 mg/L was adopted herein: Two samples were deleted from the SFWMD Canal Stream database, and no samples were deleted from the Broward C ounty or Miami-Dade County Canal Data. No chlorophyll observations were removed, with a maximum chlorophyll value of 482 μg/L

observed.

 The remaining data were organized by “Reg ion-WBID-Year,” and observation counts were made in a manner identical to that used in FDEP’s analysis of benchmark sites. Whenever the chlorophyll observation count for a particular Region-WBID-Year was less than 4, all

4 Specific conductivity was examined to exclude sites w ith higher salinities, indica tive of marine canals.

5 FDEP and EPA staff performed this sc reening process manually by examining site descriptions and GIS displays.

6 Remark code processing also applied to specific conductivity results analyzed in the above site-screening process.

7 The upper adjacent value is defined as th e largest observation that is less than or equal to the upper qua rtile plus 1.5 times the inter-quartile range.

### TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters



## 4-4 chlorophyll observations for that Region-WBID-Y ear were dropped. A similar process for TP and TN was implemented.

 The chlorophyll, TP, and TN data were tran sformed by taking the natu ral logarithm (LN(TP) and LN(TN)). Log transformation converts da ta that are positively skewed to more symmetrically (closer to normally) distributed data. The mean LN(chlorophyll), LN(TP), and LN(TN) were computed for each combination of Region-WBID-Year.

Following the screening process to eliminate Region-WBID-Years with less than 4 samples, there were 68,544 samples, 8 

## of which 5,524 samples had chlorophyll results. Summarizing (computing the mean of the transformed data ) to Region-WBID-Year level resulted in 1,622 records, of which 275 records had chlorophyll data . Of the 275 records w ith chlorophyll data,

258 records had matched TN and/or TP data to enable regression analysis. Figure 4-3 shows the 72 stations with chlorophyll da ta. Figure 4-4 shows the same 72 stations together with the

WBIDs that FDEP has identified as nutrient-i mpaired. The 1,622 records summarized to the Region-WBID-Year level are the starting point for th e analyses described in this section, as well as alternative analyses described later.

The data were further screened by selecting only the sites located in WBIDs that were not listed as nutrient-impaired on FDEP’s 303( d) list. Allowing for potential in accuracies in location data, sites that were within 1,000 me ters of a WBID that was liste d by FDEP as nutrient-impaired were excluded (see Figure 4-5). Using the means of the natural l ogarithm-transformed data at the Region-WBID-Year level, the mean and standard deviation were estimated by region as well as for all regions. The mean and standard deviation for TP were also computed for the West Coast, EAA, and East Coast regions combined. (Thi s “three-region” result was developed in acknowledgement of the existing TP criteria for the EvPA.) Using the mean and standard deviation, the theoretical 75 th 

## (and other) percentiles were estimated assuming a lognormal distribution (see Table 4-1).

The 75 th 

## percentile chlorophyll and TN concentrations across all f our regions at sites located within WBIDs not listed was impaired for nutrien ts are 4.0 μg/L and 1.6 mg/L, respectively. The

75 th 

## percentile TP concentration at sites located within WBIDs not listed as impaired for nutrients across the West Coast, EAA, and East Coast regions is 0.042 mg/L.



## 4a. Other Analyses Conducted and Consider ed by U.S. EPA in the Derivation of Proposed Numeric Nutrie nt Criteria for Canals

Another option that can be used in a reference condition approach is to extrapolate a reference

condition of nutrient concen tration that can be assumed to inhere ntly protect aquatic life. In this application of the all canals approa ch, a distribution of nutrient data is assembled from all sites in an ecoregion (or, alternatively, a random selecti on of sites). Assuming th e pool of sites in the distribution is large enough to represent all waters in the re gion and can be presumed to reasonably reflect the full range of ambient conditi ons with a disturbance gr adient from least to most impacted, the selection of a lower percentile of this distribution (e.g., 25 th 

## percentile),

8 To be included, that sample must have at least one result associated with TN, TP, or chlo rophyll. The process of excluding Region-WBID-Year with less than four observations was adopted from procedures used by FD EP in its reference condition approach analyses.

### TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters



## 4-5 should provide a nutrient level that can be assumed to in herently protect aqua tic life. Although an all sites data distribution approach does not di rectly link nutrient concentrations to valued ecological attributes (healthy, well-balanced populations of flora or fauna), it does presume that the

25 th 

## percentile will closel y approximate a re ference condition for that ge ographic location. Several examples with data from actual reference sites have supported this assumption (USEPA 2000a, b).

Using the means of the natural logarithm-transf ormed data at the Region-WBID-Year level (the 1,622 records described earlier), the mean and st andard deviation were estimated by region as well as for all regions. The mean and standard devi ation for TP were also computed for the West Coast, EAA, and East Coast regions combine d. (This three-region result was developed in acknowledgement of the existing TP criteria for the EvPA.) As one effort to consider canal benchmark conditions, summary statistics for site s along Rt. 41 were computed. The Rt. 41 sites (see Figure 4-1 and 4-2) are surrounded by the Ev PA to the east and the Big Cypress National Preserve to the west. Site S356, the most eastward site, is one m ile west of Krome Avenue on the L-29 canal, north side of Tamiami Trail. Site BC19, the most westward site, is on Rt. 41 at Bridge #69. Although these sites have not been vetted through the same process as the sites for the streams and rivers analyses, they are genera lly believed to represent canal sites with the lowest impact from anthropogenic activities in the study area. These summary statistics were used to compute theoretical distributions, a ssuming a lognormal distribution, and were backtransformed (see Table 4-2). The results follow:

 Chlorophyll. The 25 th 

## percentile distribution ranged from 1.9 to 2.2 μg/L for the EvPA, West Coast, East Coast, and all regions. The 25 th 

## percentile distribution was 6.3 μg/L for the EAA.

The 75 th 

## percentile distribution for Rt. 41 was 3.4 μg/L.

 TN. The 25 th 

## percentile distribution ra nged from 0.8 to 1.4 mg/L for the EvPA, West Coast, East Coast, and all regions. The 25 th 

## percentile distribution was 2.1 mg/L for the EAA. The

75 th 

## percentile distribution for Rt. 41 was 1.3 mg/L.

 TP. The 25 th 

## percentile distribution ranged from 0.013 to 0.023 mg/L for the EvPA, West Coast, East Coast, and all re gions. Excluding the EvPA, the 25 th 

## percentile distribution was 0.018 mg/L for the remaining three regions when combined. The 75 th 

## percentile distribution for Rt. 41 was 0.018 mg/L.



## 4b. Duration and Frequency fo r Proposed Canals Criteria

Aquatic life water quality criter ia contain three components: ma gnitude, duration, and frequency. The criterion-duration for this magnitude (or av eraging period) is specified as an annual geometric mean. U.S. EPA is proposing two expr essions of allowable frequency, both of which are to be met. First, U.S. EPA proposes a no mo re than one in three years excursion frequency for the annual geometric mean criteria for canal s. Second, U.S. EPA proposes that the long-term arithmetic average of annual geometric means not exceed the criterion-magnitude concentration. These proposed duration and frequency components of the criteria are consistent with the data set used to derive the criteria, which contained data from multiple years of record, all seasons, and a variety of hydrologic conditions. This freq uency of excursions should not result in unacceptable effects on aquatic lif e as it will allow the canal aquatic system enough time to recover from the occasionally elevated year of nut rient loadings. The full rationale for both lakes (section 1d) and streams (section 2d) are also relevant for canals.

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## 4-6 

## 4c. Alternative Approach Co nsidered by U.S. EPA

i. Stressor-Response Approach

Another approach described in U.S. EPA gui dance for developing nutrient criteria (USEPA 2000a) is to quantify a stressor-response rela tionship between excess nutrients and adverse effects on aquatic life, and then use that relationship to establish “effects-based” criteria. The necessary observed dose-response relationship could be described by a model (e.g., trophic state classification, regional predictive m odel, nutrient sensitive biocrite ria), which in turn would link

nutrient concentrations to the relative risk of environmental harm. Nutrient criteria based on a cause/effect relationship between nutrients and valued ecological attribut es could thus be considered directly linked to maintaining designat ed uses. Such criteria might be scientifically defensible but have proved to be elusive. Alt hough the underlying relationsh ip certainly exists, in nature it has often been difficult to quantify beca use of the complexity of nutrient dynamics and

the influence of in terfering factors.

In this analysis, matched chlorophyll and nutrient data were analyzed us ing linear regression. For those regression analyses that were statisti cally significant, an assumed geometric mean chlorophyll concentration of 20 μg/L was used to estimate the corresponding nutrient concentration that results from the 90% predic tion interval of the regression equation. The selection of 20 μg/L does not imply a linkage to a particular designate d use in canals. As described earlier and shown in Figures 4-3 and 4-4, 72 of the 531 sites had chlorophyll data for the stressor-response analysis performed. Those site s resulted in 258 reco rds of chlorophyll

matched with TP or TN.

TP. Figure 4-6a is a bivariate plot of annual geometric mean chlorophy ll as function of TP. Figure 4-6b presents the fitted regression equatio n along with the 90% confidence interval using data from across all regions. Figures 4-6c and 4- 6d provide the regression analysis statistics and residual plots, respectively. This regression mode l explains 49% of the variance; the residual plots suggest that the residuals have a constant variance, ar e independent, and are normally distributed. Based on this regre ssion analysis, an annual geometri c mean TP concentration less than 0.124 mg/L would yield an annual geometric mean chlorophyll concen tration of less than 20 μg/L with a 90% confidence level.

The TP regression analysis was rerun excludi ng data from the EvPA to evaluate model sensitivity (see Figure 4-7). There were negligible differences: An annual geometric mean TP

concentration less than 0.124 mg/L would yield an annual geometric mean chlorophyll concentration of less than 20 μg/L with a 90% confidence level.

The region-specific regression analyses for TP are shown in Figure 4-8. While the model for the EAA was not statistically significant, the regressi on models developed for the other three regions explained from 43% to 73% of the data variabilit y. Residual plots for the EvPA, West Coast, and East Coast were visually examined and found to have constant variance, independence, and normal distribution. Region-specific regression diagnostics for sta tistically significant models are shown in Figures 4-9 through 4-11. The TP concentr ations for the EvPA, West Coast, and East Coast regions that corresponded to a chlorophyll concentration of 20 μg/L were computed as 0.052, 0.25, and 0.069 mg/L, respectively.

### TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters



## 4-7 TN. Figure 4-12a is a bivariate plot of annual geom etric mean chlorophyll as function of TN by canal region. Figure 4-12b presents the fitted re gression equation along with the 90% confidence interval using data from acr oss all regions. Figures 4-12c and 4-12d provide the regression analysis statistics and residual plots, respectively. This re gression model explains 16.5% of the variance, and the residual plots suggest that the residuals are normally distributed. U.S. EPA acknowledges the visual appear ance of a higher vari ance in the middle of the TN range. The lowest and highest chlorophyll concentrations were investigated further. The six records with annual geometric mean chlorophyll concentrations less than 1 μg/L are attributed to WBID 3283 (1999 and 2003) and 3284 (1999, 2000, 2003 and 2006), both along Snake Creek Canal in northern Dade County. The five records with an nual geometric mean chlorophyll concentrations greater than 15 μg/L are reported with data from 2002 through 2007 for WBIDs 3264 and 3226F (twice) (Hillsboro Canal in northern Broward County), 3271 (Pompano Canal in Broward County), 8063 (West Coast in Collier County) , and 3261C (SR 29 Canal in Collier County). There was insufficient evidence in the data analyzed to suggest that these results were anomalous, and thus they were main tained. It is also noted that th e higher spread in the data at mid-range TN levels may also be attributed, in part, to the higher freque ncy of observations in this range of the data. Based on this regres sion analysis, an annual geometric mean TN

concentration less than 3.6 mg/L would yield an annual geometric mean chlorophyll concentration of less than 20 μg/L with a 90% confidence level.

The region-specific regression analyses for TN are shown in Figure 4-13. The models for the EAA and EvPA regions were not statistically si gnificant. The regressi on models developed for the West and East Coast regions explained from 9% to 21% of the data va riability. Residual plots for the West and East Coast regions were visually examined and found to be normally distributed. Region-specific regr ession diagnostics for statistically significant models are shown in Figures 4-14 and 4-15. The variance associat ed with the West Coast residuals did show a tendency to increase as a functi on of TN. The TN concentrations for the West and East Coast regions that corresponded to a chlorophyll concentration of 20 μg/L were computed as 4.0 and 2.35 mg/L, respectively.

Alternative Regression Analysis. Quantile regression may also be used to analyze the same nutrient-chlorophyll data that were analyzed with linear regression. In th is application, quantile regression computes an estimate on the 90 th 

## conditional quantile function of the response, given the stressor (TN or TP). The function presumes a linear specification for the quantile regression model, i.e., that the formula defines a model that is linear in parameters. The function minimizes a weighted sum of absolute residuals that ca n be formulated as a linear programming problem. To minimize the impact of skewed data, the log 10 

## (logarithm, base 10) transformation was computed before applying the quantile regression algorithm. Table 4-3 presents the Spearman’s rho correlation coefficient, the quantile regression slope, and inte rcept terms. Candidate TN and TP endpoints were computed using the regr ession terms and an assumed chlorophyll concentration of 20 μg/L (see Table 4-3). Agai n, the selection of 20 μg/L does not imply a linkage to a particular designa ted use in canals. Figures 4-16 through 4-19 presents bivariate plots of chlorophyll as a func tion of TN and TP (in log 10 

## space) overall and for the regions with significant correlations. Each plot displays th e Spearman’s rho correlation coefficient, the

### TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters



## 4-8 LOESS 9 

## smoothed line (black line), and the quantile regression results (b lue line and blue text for the regression terms).



## ii. Calculation of TP Criteria for the EAA Using a Downstream Protection Approach

Florida has adopted and U.S. EP A has approved a long-term geometric mean TP criterion for the EvPA of 10 ppb. This 10 ppb long-term geometri c mean water quality criterion applies throughout the EvPA. Walker (2005) determined th at the ratio of the l ong-term flow-weighted mean to the long-term geometric mean across all stormwater treatment areas (STAs) for 24 water years is 1.18, with a standard error 0.04. The criterion has been cal culated to translate into an equivalent maximum annual flow-weighted concentr ation of approximately 16 ppb at discharges into the Everglades (Payne et al. 2005; Walker 2005). Payne et al. (2005) concluded that a TP effluent limit of 15.5 ppb expressed as a maximu m annual flow-weighted mean is recommended for incorporation into permits for all discharges into the EvPA to ensure that the discharges do

not cause or contribute to exceedances of th e phosphorus criterion in the downstream marsh receiving waters. This target can be reevaluated in the future as more data become available. Although the annual maximum limit has not been fo rmally adopted by FDEP as a Water Quality Based Effluent Limit (WQBEL), at the present tim e it represents the direct translation of the long-term numeric criterion to the discharge such that all water delivered to the EvPA would

meet the numeric criterion w ith a level of confidence.

The 16 ppb annual maximum concentration limit might be used as a downstream protective value for the EvPA. This approach should ensure that all waters delivered to the EvPA at WBIDs

3252B and 3265B would meet the exis ting TP criterion for the EvPA.

Approach 1: Estimate STA inflow loads resulting in WQS in downstream waters

To meet the 10 ppb criterion and protect downstrea m waters of the EvPA, STAs must discharge an annual maximum of 16 ppb. STA performan ce is a function of phosphorus loading rate (PLR), expressed as grams TP per acre of STA area per year (Kadlec 2006; Pietro et al. 2007; Goforth et al. 2007a–2007f). As PLR increases, th e projected STA discharge TP concentration also increases. Relationships also have been explored for TP concentration at STA inflow versus TP outflow concentration (Pietro et al. 2007) . The best predictor of STA outflow TP concentration appears to be PLR.

Pietro et al. (2007) summarize annual STA performance across all STAs for the history of the program. From 1995 to 2006 the three years with the lowest STA outflow concentrations (1997, 19 ppb; 1998, 20 ppb; and 1999, 19 ppb) had corr esponding PLRs of 0.93, 0.82 and 0.75, and inflow TP concentrations of 98 ppb, 98 ppb, and 90 ppb, respectively. Outflo w TP for the other nine years ranged from 20 ppb to 44 ppb; PLR wa s always greater than 1.0, ranging from 1.26 to 3.51; and inflow TP concentrations were all greater than 100 ppb, ranging from 109 ppb to 147 ppb. The years with the performan ce closest to the 16 ppb discharg e goal all had PLRs less than 1.0 g/m2/yr and inflow TP less than 100 ppb. This 100 ppb concentration mi ght be the initial

9 We used a locally weighted scatterplo t smoothing (LOESS) technique to visually examine possibl e trends along environmental gradients. The LOESS technique (Cleveland 1979) models nonlinear relationships where linear methods do not perform well, and fits simple models to localized subsets of the data to construct a function that describes, essentially, the central tendency o f data along nutrient gradients. We used a smoothing factor of 0.7 to compute lowess curves.

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## 4-9 STA inflow target concentration until such time th at it is demonstrated that the STAs can attain the maximum annual flow-weighted concentration of 16 ppb discharge goal with a higher inflow

concentration.



## iii. Upper Percentile Criterion w ith Low Exceedance Frequency

The purpose of this analysis is to compute a “n ot to exceed” concentration for a single sample based on sites that meet the annual geometric mean criteria proposed for canals. The computational approach is the same as that used for other waterbodies of the inland criteria work.

Using the canal water quality data prior to summ arizing to the Region-WBID-Year level (i.e., the point with 68,544 samples, of which 5,524 samples had chlorophyll results), the geometric mean chlorophyll, TN, and TP were computed for each “s ite-year.” All data asso ciated with site-year geometric means greater than the corresponding pr oposed nutrient criteria were removed from the analysis.

The “site” standard deviation was computed using the remaining da ta, regardless of year. Using the proposed criteria and the standard deviation, the one-sided 95% prediction interval was used

to estimate the not to exceed criteria for a single observation. Table 4-4 includes summary

statistics and the estimated not to exceed concentration.

### TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters



## 4-10 Table 4-1. Distributional Statisti cs Computed from the Natural Logarithm Transformed Data at Sites Located in WBIDs not Listed as Nutrient Impaired

Observations Distribution Statistics

Estimated from

LN

Transformed

Data Percentiles Computed from Mean and

Standard Deviation Back Transformed to Regular Units

Parameter Region Number
of

WBIDYears Number
of

WBIDs Mean

LN Std

Dev LN 10 th



### %tile

LN 25 th



### %tile

LN 50 th



### %tile

LN 75 th



### %tile

LN 90 th



### %tile

LN

EAA (Not Impaired) n/a n/a n/ a n/a n/a n/a n/a n/a n/a

EvPA (Not Impaired) 25 2 1.059 0.521 1.478 2.028 2.882 4.095 5.618

West (Not Impaired) 78 9 1.005 0.393 1.652 2.097 2.732 3.561 4.519

East (Not Impaired) 40 4 1.195 0.676 1.389 2.095 3.305 5.215 7.862 Chlorophyll
(percentiles are in μg/L)

All (Not Impaired) 131 14 1.046 0.508 1.483 2.019 2.845 4.008 5.457

EAA (Not Impaired) 3 1 0.320 0.327 0.906 1.105 1.377 1.717 2.095

EvPA (Not Impaired) 134 7 0.398 0.391 0.902 1.144 1.489 1.939 2.458

West (Not Impaired) 206 13 -0.055 0.408 0.561 0.719 0.946 1.246 1.596

East (Not Impaired) 229 21 0.127 0.450 0.637 0.838 1.135 1.538 2.022 TN
(percentiles

are in mg/L)

All (Not Impaired) 547 40 0.137 0.459 0.636 0.841 1.146 1.563 2.065

EAA (Not Impaired) 3 1 -3.299 0.244 0.0270 0.0313 0.0369 0.0436 0.0505

EvPA (Not Impaired) 158 7 -4.111 0.575 0.0078 0.0111 0.0164 0.0242 0.0343

West (Not Impaired) 220 14 -3.526 0.618 0.0133 0.0194 0.0294 0.0447 0.0650

East (Not Impaired) 293 23 -4.011 1.057 0.0047 0.0089 0.0181 0.0370 0.0703
3 Region (Not Impaired) 516 37 -3.800 0.925 0.0068 0.0120 0.0224 0.0418 0.0732 TP
(percentiles

are in mg/L)

All (Not Impaired) 649 43 -3.868 0.875 0.0068 0.0116 0.0209 0.0377 0.0642

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## 4-11 Table 4-2. Distributional Statistics Computed from the Natural Logarithm Transformed Data Using All Data

Observations Distribution Statistics

Estimated from

LN

Transformed

Data Percentiles Computed from Mean and

Standard Deviation Back-Transformed to Regular Units

Parameter Region Number
of

WBIDYears Number
of

WBIDs Mean

LN Std Dev

LN 10 th



### %tile LN 25 th



### %tile LN 50 th



### %tile LN 75 th



### %tile LN 90 th



### %tile LN

EAA 19 3 2.015 0.260 5.381 6.299 7.504 8.940 10.465

EvPA 26 3 1.110 0.575 1.453 2.060 3.036 4.474 6.343

West 94 11 1.158 0.530 1. 614 2.227 3.183 4.550 6.275

East 136 15 1.198 0.805 1. 182 1.926 3.314 5.703 9.295

ALL 262 30 1.226 0.711 1. 370 2.109 3.407 5.504 8.475 Chlorophyll
(percentiles are in μg/L)

Rt. 41 26 2 0.976 0.383 1.625 2.050 2.654 3.437 4.336

EAA 250 14 0.976 0.334 1.729 2.118 2.654 3.325 4.073

EvPA 344 22 0.555 0.340 1.127 1.385 1.742 2.190 2.692

West 295 19 0.048 0.401 0. 627 0.800 1.049 1.376 1.755

East 558 49 0.243 0.381 0. 782 0.986 1.274 1.648 2.077

ALL 1355 89 0.406 0.487 0. 803 1.080 1.500 2.084 2.801 TN
(percentiles

are in mg/L)

Rt. 41 86 5 0.088 0.296 0.748 0.895 1.093 1.334 1.596

EAA 270 16 -2.660 0.572 0.0336 0.0476 0.0699 0.1028 0.1454

EvPA 365 23 -3.762 0.721 0. 0092 0.0143 0.0232 0.0378 0.0586

West 313 20 -3.276 0.738 0.0147 0.0230 0.0378 0.0621 0.0972

East 649 51 -3.579 1.195 0.0060 0.0125 0.0279 0.0625 0.1290

ALL 1491 94 -3.415 1.008 0. 0090 0.0167 0.0329 0.0648 0.1195
3 Region 1190 81 -3.328 1.050 0.0093 0.0177 0.0359 0.0728 0.1378 TP
(percentiles

are in mg/L)

Rt. 41 110 5 -4.301 0.420 0.0079 0.0102 0.0136 0.0180 0.0232

### TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters



## 4-12 Table 4-3. Quantile Regression Results for Matched Annual Geometric Mean Chlorophyll and

Nutrient Data

Quantile Regression

Parameter Region Spearman’s

rho Intercept Slope Candidate Endpoint

(mg/L)

Overall 0.445 0.848 0.588 5.89

EAA -0.058

EvPA -0.130

West 0.531 0.967 1.025 2.12 TN

East 0.394 0.851 0.643 5.01

Overall 0.733 1.738 0.589 0.181

EAA -0.430

EvPA 0.816 2.595 1.039 0.057

West 0.612 1.455 0.484 0.481 TP

East 0.739 1.807 0.621 0.153

Candidate endpoint was computed assuming a chlorophyll concentration of 20 μg/L.



## Table 4-4. Results of “Not to Exceed” Criteria Computations

Parameter Proposed

Annual

Geometric

Mean

Criteria Total

Number
of

Observations Analyzed Total

Number
of SiteYears

with

Data Number of

Site-Years

with Data

That Met

Annual
Geometric

Mean Criteria Number of

Sites with

Data That

Met Annual

Geometric

Mean Criteria Standard

Deviation

of

Transfor

med Data Not to

Exceed

Value
Chlorophyll 4.0 μg/L 5,524 596 412 58 0.8291 15.6 μg/L
TN 1.6 mg/L 42,607 2,965 1,768 292 0.3040 2.6 mg/L
TP 0.042 mg/L 67,000 3,781 2,516 344 0.6376 0.120 mg/L



## Figure 4-1a. Location of EAA

and EvPA. Figure 4-1b. STATSGO soil orders for southern

Florida. EA

EPA

### TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters



## 4-13 Figure 4-2. Site locations ( n =513) with canal water quality da ta evaluated in this document.

### TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters



## 4-14 Figure 4-3. Site locations ( n =72) with chlorophyll data evaluated in this document.

### TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters



## 4-15 Figure 4-4. Site locations ( n =72) with chlorophyll data evaluate d in this document together with

Florida WBIDs that FDEP has iden tified as nutrient-impaired.

### TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters



## 4-16 Figure 4-5. Sites located within WB IDs not listed as nutrient-impaired.TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters4-17a)TP

Chl_500.1000.0100.00110.01.00.1EAAEPAWestEastArea_Nameb)TP

Chl_500.1000.0100.00110.01.00.1200.124S 0.218665R-Sq 49.2%R-Sq(adj) 49.0%Regression90% CI90% PIlogten(Chl_50) = 1.459 + 0.5693 logten(TP)c)**Regression Analysis: Chl_50 versus TP**The regression equation islogten(Chl_50) = 1.459 + 0.5693 logten(TP)S = 0.218665 R-Sq = 49.2% R-Sq(adj) = 49.0%Source DF SS MS F PRegression 1 11.7784 11.7784 246.34 0.000Error 254 12.1448 0.0478Total 255 23.9232d)Residual

Percent1.00.50.0-0.5-1.099.99990501010.1Fitted Value

Residual1.000.750.500.250.001.00.50.0-0.5-1.0Residual

Frequency0.90.60.30.0-0.3-0.6604530150Observation Order

Residual 2 4 0

2 2 0

2 0 0

1 8 0

1 6 0

1 4 0

1 2 0

1 0 0

8 0

6 0

4 0

2 0

11.00.50.0-0.5-1.0Normal Probability Plot of the ResidualsResiduals Versus the Fitted ValuesHistogram of the ResidualsResiduals Versus the Order of the DataResidual Plots for logten(Chl_50) vs TPFigure 4-6. a) Bivariate plot of annual geometric mean chlorophyll as a function of TP by canal region, b) bivariate plot withfitted linearregression and 90% prediction interval, c)regression analysis statistics, and d)residual plots from regression analysis.TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters4-18a)TP

Chl_500.1000.0100.00110.01.00.1EAAEPAWestEastArea_Nameb)TP

Chl_500.1000.0100.00110.01.00.1200.124S0.223073R-Sq 49.1%R-Sq(adj) 48.9%Regression90% CI90% PIlogten(Chl_50) = 1.452 + 0.5707 logten(TP)c)**Regression Analysis: Chl_50 versus TP**The regression equation islogten(Chl_50) = 1.452 + 0.5707 logten(TP)S = 0.223073 R-Sq = 49.1% R-Sq(adj) = 48.9%Source DF SS MS F PRegression 1 10.9457 10.9457 219.96 0.000Error 228 11.3456 0.0498Total 229 22.2914d)Residual

Percent1.00.50.0-0.5-1.099.99990501010.1Fitted Value

Residual1.000.750.500.250.001.00.50.0-0.5-1.0Residual

Frequency0.90.60.30.0-0.3-0.6483624120Observation Order

Residual 2 4 0

2 2 0

2 0 0

1 8 0

1 6 0

1 4 0

1 2 0

1 0 0

8 0

6 0

4 0

2 0

11.00.50.0-0.5-1.0Normal Probability Plot of the ResidualsResiduals Versus the Fitted ValuesHistogram of the ResidualsResiduals Versus the Order of the DataResidual Plots for logten(Chl_50)Figure 4-7. a) Bivariate plot of annual geometric mean chlorophyll as a function of TP by canal region excluding the EvPA, b) bivariate plot withfitted linear regression and 90% prediction interval, c) regression analysis statistics, and d) residual plots from regressionanalysis.TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters4-19a)TP

Chl_500.1000.0100.00110.01.00.1200.001S 0.110211R-Sq9.7%R-Sq(adj) 4.4%Regression90% CI90% PIEAA TPlogten(Chl_50) = 0.6135 - 0.2755 logten(TP)b)TP

Chl_500.1000.0100.00110.01.00.1200.052S0.129383R-Sq 74.2%R-Sq(adj) 73.2%Regression90% CI90% PIEPA TPlogten(Chl_50) = 2.312 + 0.9844 logten(TP)c)TP

Chl_500.1000.0100.00110.01.00.1200.25S0.173105R-Sq 44.1%R-Sq(adj) 43.4%Regression90% CI90% PIWest Coast TPlogten(Chl_50) = 1.279 + 0.4892 logten(TP)d)TP

Chl_500.1000.0100.00110.01.00.1200.069S0.250179R-Sq 48.7%R-Sq(adj) 48.3%Regression90% CI90% PIEast Coast TPlogten(Chl_50) = 1.692 + 0.6913 logten(TP)Figure 4-8. Bivariate plot of annual geometric mean chlorophyll asa function of TP with fittedlinear regression and 90% predictioninterval for a) EAA (notstatistically significant), b) EvPA, c) West Coast, and d) East Coast. (Residual plots for the EvPA, West Coast,and East Coast were visually examined and found to haveconstant variance, independence, and normal distribution.)TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters4-20**Results for: EvPA Regression Analysis: Chl_50 versus TP** The regression equation islogten(Chl_50) = 2.312 + 0.9844 logten(TP)S = 0.129383 R-Sq = 74.2% R-Sq(adj) = 73.2%Source DF SS MS F PRegression 1 1.15733 1.15733 69.14 0.000Error 24 0.40176 0.01674Total 25 1.55909Residual

Percent0.300.150.00-0.15-0.30999050101Fitted Value

Residual1.000.750.500.250.000.20.0-0.2Residual

Frequency0.30.20.10.0-0.1-0.26.04.53.01.50.0Observation Order

Residual26242220181614121086420.20.0-0.2Normal Probability Plot of the ResidualsResiduals Versus the Fitted ValuesHistogram of the ResidualsResiduals Versus the Order of the DataEPA TP-Residual Plots for logten(Chl_50)Figure 4-9. EvPA regression analysis statistics for annual geometric mean chlorophyll as a function ofTP and residual plots.**Results for: West Coast Regression Analysis: Chl_50 versus TP** The regression equation islogten(Chl_50) = 1.279 + 0.4892 logten(TP)S = 0.173105 R-Sq = 44.1% R-Sq(adj) = 43.4%Source DF SS MS F PRegression 1 1.91713 1.91713 63.98 0.000Error 81 2.42718 0.02997Total 82 4.34431Residual

Percent0.500.250.00-0.25-0.5099.99990501010.1Fitted Value

Residual1.00.80.60.40.20.500.250.00-0.25-0.50Residual

Frequency0.60.40.20.0-0.2-0.420151050Observation Order

Residual807060504030201010.500.250.00-0.25-0.50Normal Probability Plot of the ResidualsResiduals Versus the Fitted ValuesHistogram of the ResidualsResiduals Versus the Order of the DataWest Coast TP-Residual Plots for logten(Chl_50)Figure 4-10. West Coast regression analysisstatistics for annual geometric mean chlorophyll as a function ofTP and residualplots.TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters4-21**Results for: East Coast Regression Analysis: Chl_50 versus TP** The regression equation islogten(Chl_50) = 1.692 + 0.6913 logten(TP)S = 0.250179 R-Sq = 48.7% R-Sq(adj) = 48.3%Source DF SS MS F PRegression 1 7.4766 7.47656 119.45 0.000Error 126 7.8863 0.06259Total 127 15.3628Residual

Percent1.00.50.0-0.5-1.099.99990501010.1Fitted Value

Residual1.000.750.500.250.001.00.50.0-0.5-1.0Residual

Frequency0.90.60.30.0-0.3-0.6-0.93020100Observation Order

Residual 1 3 0

1 2 0

1 1 0

1 0 0

9 0

8 0

7 0

6 0

5 0

4 0

3 0

2 0

1 0

11.00.50.0-0.5-1.0Normal Probability Plot of the ResidualsResiduals Versus the Fitted ValuesHistogram of the ResidualsResiduals Versus the Order of the DataEast Coast TP-Residual Plots for logten(Chl_50)Figure 4-11. East Coast regressionanalysis statistics forannual geometric mean chlorophyll asa function of TP and residual plots.TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters4-22a)TN

Chl_5010.01.00.110.01.00.1EAAEPAWestEastArea_Nameb)TN

Chl_5010.01.00.110.01.00.1203.6S 0.279109R-Sq 16.8%R-Sq(adj) 16.5%Regression90% CI90% PIlogten(Chl_50) = 0.5238 + 0.5738 logten(TN)c)**Regression Analysis: Chl_50 versus TN**The regression equation islogten(Chl_50) = 0.5238 + 0.5738 logten(TN)S = 0.279109 R-Sq = 16.8% R-Sq(adj) = 16.5%Source DF SS MS F PRegression 1 3.9005 3.90051 50.07 0.000Error 248 19.3197 0.07790Total 249 23.2202d)Residual

Percent1.00.50.0-0.5-1.099.99990501010.1Fitted Value

Residual1.00.80.60.40.21.00.50.0-0.5-1.0Residual

Frequency0.60.30.0-0.3-0.6-0.9403020100Observation Order

Residual 2 4 0

2 2 0

2 0 0

1 8 0

1 6 0

1 4 0

1 2 0

1 0 0

8 0

6 0

4 0

2 0

11.00.50.0-0.5-1.0Normal Probability Plot of the ResidualsResiduals Versus the Fitted ValuesHistogram of the ResidualsResiduals Versus the Order of the DataResidual Plots for logten(Chl_50) vs TNFigure 4-12. a) Bivariate plot ofannual geometric mean chlorophyll as a function of TN by canal region, b) bivariate plot withfitted linearregression and 90% prediction interval, c)regression analysis statistics, and d)residual plots from regression analysis.TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters4-23a)TN

Chl_5010.01.00.110.01.00.120S0.115747R-Sq0.4%R-Sq(adj) 0.0%Regression90% CI90% PIEAA TNlogten(Chl_50) = 0.9085 - 0.0723 logten(TN)b)TN

Chl_5010.01.00.110.01.00.120S0.248864R-Sq4.7%R-Sq(adj) 0.7%Regression90% CI90% PIEPA TNlogten(Chl_50) = 0.4841 + 0.4881 logten(TN)c)TN

Chl_5010.01.00.110.01.00.1204.0S 0.206710R-Sq 22.3%R-Sq(adj) 21.3%Regression90% CI90% PIWest Coast TNlogten(Chl_50) = 0.5965 + 0.5881 logten(TN)d)TN

Chl_5010.01.00.110.01.00.1202.35S0.327084R-Sq 10.2%R-Sq(adj) 9.4%Regression90% CI90% PIEast Coast TNlogten(Chl_50) = 0.4645 + 0.7553 logten(TN)Figure 4-13. Bivariate plot of annual geometric mean chlorophyllas a function of TN with fitted linear regression and 90% predictioninterval for a) EAA (notstatistically significant), b) EvPA (not statistically significant), c) West Coast, and d)East Coast. (Residualplots for the West and East Coasts werevisually examined and found to have independence and normal distribution. The varianceassociated with the West Coast residuals did showa tendency to increase as a function of TN.)TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters4-24**Results for: West Coast Regression Analysis: Chl_50 versus TN** The regression equation islogten(Chl_50) = 0.5965 + 0.5881 logten(TN)S = 0.206710 R-Sq = 22.3% R-Sq(adj) = 21.3%Source DF SS MS F PRegression 1 0.94490 0.944901 22.11 0.000Error 77 3.29015 0.042729Total 78 4.23505Residual

Percent0.80.40.0-0.4-0.899.99990501010.1Fitted Value

Residual0.80.60.40.20.80.40.0-0.4-0.8Residual

Frequency0.60.40.20.0-0.2-0.4-0.624181260Observation Order

Residual807060504030201010.80.40.0-0.4-0.8Normal Probability Plot of the ResidualsResiduals Versus the Fitted ValuesHistogram of the ResidualsResiduals Versus the Order of the DataWest Coast TN-Residual Plots for logten(Chl_50)Figure 4-14. West Coast regression analysisstatistics for annual geometric mean chlorophyll as a function of TN and residual plots.**Results for: East Coast Regression Analysis: Chl_50 versus TN** The regression equation islogten(Chl_50) = 0.4645 + 0.7553 logten(TN)S = 0.327084 R-Sq = 10.2% R-Sq(adj) = 9.4%Source DF SS MS F PRegression 1 1.5011 1.50111 14.03 0.000Error 124 13.2660 0.10698Total 125 14.7672Residual

Percent1.00.50.0-0.5-1.099.99990501010.1Fitted Value

Residual0.60.40.21.00.50.0-0.5-1.0Residual

Frequency0.90.60.30.0-0.3-0.6-0.924181260Observation Order

Residual 1 3 0

1 2 0

1 1 0

1 0 0

9 0

8 0

7 0

6 0

5 0

4 0

3 0

2 0

1 0

11.00.50.0-0.5-1.0Normal Probability Plot of the ResidualsResiduals Versus the Fitted ValuesHistogram of the ResidualsResiduals Versus the Order of the DataEast Coast TN-Residual Plots for logten(Chl_50)Figure 4-15. East Coast regressionanalysis statistics forannual geometric mean chlorophyll asa function of TN and residual plots.

### TSD for U.S. EPA’s Proposed Numeric Nutrient Cr iteria for FL Inland Surface Fresh Waters



## 4-25 Figure 4-16. Bivariate plot of annua l geometric mean chloro phyll as a function of TN and TP across all regions together with Spearman’s rh o correlation coefficient, LOESS smoother (black line, f=0.7), and quantile regression results (b lue line and blue text).

### TSD for U.S. EPA’s Proposed Numeric Nutrient Cr iteria for FL Inland Surface Fresh Waters



## 4-26 Figure 4-17. Bivariate plot of annual geometric mean chlorophy ll as a function of TP for the EvPA together with Spearman’s rho correlation co efficient, LOESS smooth er (black line, f=0.7), and quantile regression results (blue line and blue text).

### TSD for U.S. EPA’s Proposed Numeric Nutrient Cr iteria for FL Inland Surface Fresh Waters



## 4-27 Figure 4-18. Bivariate plot of annual geometric mean chlorophyll as a f unction of TN and TP for the West Coast together with Spearman’s rho correlation coefficient, LOESS smooth er (black line, f=0.7), and quantile regression results (blue line and blue text).

### TSD for U.S. EPA’s Proposed Numeric Nutrient Cr iteria for FL Inland Surface Fresh Waters



## 4-28 Figure 4-19. Bivariate plot of annual geometric mean chlorophyll as a f unction of TN and TP for the East Coast together with Spearman’s rho correlation coefficient, LOESS smoother (black line, f=0.7), and quantile regression results (b lue line and blue text).