Bridge Stormwater Runoff Analysis and Treatment Options (2014)

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E-1 BMP Evaluation Tool Modeling Methodology This appendix summarizes the modeling methodology and underlying data used in the BMP Evaluation Tools. Introduction and Purpose This document summarizes the modeling methodology, assumptions, and default parameters used in the develop- ment of the BMP Evaluation Tools. These tools can be used to estimate average annual runoff volumes and pollutant loads before and after BMP construction and estimate con- struction and lifecycle costs associated with the BMP. These tools can be used for a variety of scenarios, pollutants, and BMP types installed to treat bridge deck runoff to aid in the decision-making process regarding BMP type and sizing. The predicted load reduction is caused by decreases in pol- lutant concentrations and reduction in runoff volume. Con- centration reductions are estimated using the difference in concentrations between characteristic highway runoff quality (influent) and estimated BMP effluent data based on regres- sion analyses of paired influent and effluent composite data from the International Stormwater BMP Database. Volume reductions are based on long-term continuous simulation hydrologic modeling using the EPA’s Storm Water Manage- ment Model (SWMM). The results of the regression analy- ses and hydrologic modeling are combined to provide load reductions estimates. The following sections describe the approach and assumptions for estimating highway runoff quality, conducting the regression analyses, and performing and summarizing the results from hydrologic modeling. Pollutants and BMPs Analyzed The pollutants analyzed and supported in the BMP Tools include total zinc (TZn), total lead (TPb), total copper (TCu), total nitrogen (TN), total phosphorus (TP), nitrate (NO3-), total Kjeldahl nitrogen (TKN), dissolved phosphorus (DP), total suspended solids (TSS), Escherichia coli (E. coli), and fecal coliforms (FC). A separate tool has been developed for each of the following BMPs: vegetated swales, dry deten- tion basins, bioretention, sand filters, and permeable friction course (PFC) overlay. Highway Runoff Quality Highway runoff quality data were obtained from the Highway-Runoff Database (HRDB) (Granato and Cazenas 2009; Smith and Granato 2010) and the National Stormwater Quality Database (NSQD) (Pitt 2008). Tables E-1 and E-2 sum- marize the data available for the two databases, before 1986. Data before 1986 were excluded in the analysis because of the use of leaded gasoline that caused an unrepresentative sample of modern conditions. The HRDB provides nearly three times as much highway runoff data as the NSQD. To assess the impact of average annual daily traffic (AADT) on constituent concentration, five AADT categories were cre- ated: 0-25,000; 25,000–50,000; 50,000–100,000; 100,000+; and unknown. As noted in Table E-3, these categories pro- vide a reasonable division of the data, with a fairly balanced distribution of the data between categories. In general, the 25K-50K category has the least data. Fecal coliform data are sparse in all categories and no categorization was possible with the E. coli data. Values for TN are sparse and TKN values were used where there was no data. Table E-4 summarizes the arithmetic means and 90% confidence intervals about those means for each AADT bin. To handle non-detects, a robust regression-on-order statis- tics (ROS) method as described by Helsel and Cohn (1988) was utilized to provide probabilistic estimates of non-detects before computing descriptive statistics. Confidence intervals were generated using the bias corrected and accelerated (BCa) bootstrap method described by Efron and Tibishirani (1993). This method for computing confidence intervals is resistant to outliers and does not require any restrictive distributional assumptions common with parametric confidence intervals. A P P E N D I X E

E-2 As indicated by the confidence intervals in Table E-4, there does not appear to be a clear relationship between AADT and pollutant concentration except for possibly dissolved phos- phorus, total copper, and total zinc, particularly when com- paring the low traffic AADT (<25K) against the high traffic AADT (>50K). Therefore, for the purposes of developing the BMP Evaluation Tools, the default concentrations used for characterizing runoff from bridge decks is the mean concen- trations for all of the data combined regardless of AADT. Tool users have the option of overriding this default with a value from the table or from other monitoring data. Regression Analysis The International Stormwater BMP Database (BMP Data- base) is a repository of influent and effluent water quality data from over 500 BMP studies. This database provides an avenue for a data-driven analysis of the relationship between influent concentration (Cinf) and effluent concentration (Ceff) for a wide range of BMP-pollutant combinations. Pollutants analyzed in this study included total suspended solids (TSS), total zinc (TZn), total lead (TPb), total copper (TCu), total nitrogen (TN), total phosphorus (TP), nitrate (NO3-), total Kjeldahl nitrogen (TKN), dissolved phosphorus (DP), ortho- phosphate (OP) as a surrogate for DP when needed, fecal coli- form (FC), and Escherichia coli (E. coli). TN is estimated as the sum of NO3- and TKN (nitrite is assumed negligible). The BMPs analyzed in this effort included swales, detention basins, bioretention, and sand filters. Permeable friction course (PFC) was also considered, but there are insufficient data available to evaluate influent and effluent relationships. Data from the BMP Database were analyzed using a multi- step process. This process is shown in Figure E-1 and consists of five steps: • Determine if sufficient paired data for analysis exist in the BMP Database • Determine if there is a statistical difference between Cinf and Ceff • Determine if a monotonic relationship between Cinf and Ceff. exists • Conduct linear, log-linear, and log-log regression between Cinf and Ceff and develop functional relationship • Ensure results do not show logical inconsistencies (e.g., dissolved fraction is greater than total) Since water quality data are often highly variable and posi- tively skewed, nonparametric statistics were selected over NSQD HRDB Combined # of sites 43 93 136 # of events 669 1,537 2,206 # of sample results 3,027 8,813 11,184 # ND 41 458 499 Table E-1. Summary of available data. Constituent Non-detects/ Total Samples TSS 11 / 1,713 NO3 92/1,047 TN 0 / 122 TKN 49 / 1,408 DP 32 / 217 TP 120 / 2,022 TCu 72 / 1,808 TPb 102 / 1,683 TZn 12 / 2,099 Fecal Col. 0 / 65 Total E. coli 0 / 13 Table E-2. Summary of non-detects and total samples for each constituent. AADT Bin Constituent 0 - 25K 25K - 50K 50K - 100K 100K + Unknown All TSS 388 198 301 563 263 1713 NO3 355 151 191 350 0 1047 TN 0 0 3 0 119 122 TKN 336 146 176 412 338 1408 DP 46 38 28 73 32 217 TP 428 264 332 508 490 2022 TCu 426 243 304 555 280 1808 TPb 402 240 264 492 285 1683 TZn 424 253 323 569 530 2099 FC 3 0 4 19 39 65 E. coli 0 0 0 0 13 13 Table E-3. Count of sample results by constituents by average annual daily traffic.

E-3 parametric statistics for this analysis. The Wilcoxon signed rank test was used to evaluate whether the influent and efflu- ent concentrations are statistically different and the Spear- man’s rho correlation coefficient was used to evaluate whether a monotonic relationship exists (Helsel and Hirsch 2002). The Wilcoxon signed ranked test assumes the distribution of the paired differences is symmetric, so the data were log- transformed prior to conducting the test. No transformation was needed for the Spearman’s rho computation because the correlation analysis uses the ranks of the data. If the Wilcoxon test found a statistically significant differ- ence between the influent and effluent concentrations, and the Spearman’s rho test found that a monotonic relationship exists, regression equations were developed using the Kendall- Theil robust line procedure described by Granato (2006). Lin- ear and log-linear relationships were evaluated and the best fit equation was used based on the median absolute difference. Statistical significance for all analyses was determined at a level of a = 0.10. The analysis results are presented and discussed. Sufficient Paired Data for Analysis Paired data, those for which both influent and effluent concentrations were measured on the same BMP for the same rainfall event, were the only data used for this analysis. This was done to eliminate the impact of miscellaneous variables that might influence either influent or effluent quality separately. A minimum of 3 distinct studies and 20 distinct influent/effluent AADT Bin Constituent 0 - 25K 25K - 50K 50K - 100K 100K+ Unknown All TSS (mg/L) 162.76 178.28 120.08 143.61 85.18 138.84 (136.12- 190.42) (127.11- 233.81) (95.05-150.62) (130.62- 157.11) (72.84-98.43) (127.37-150.25) NO3 (mg/L) 0.48 1.12 0.82 1.74 No Data 1.06 (0.42-0.53) (0.94-1.32) (0.73-0.92) (1.51-2.02) (0.96-1.16) TN (mg/L) No Data No Data 3.61 No Data 3.59 3.59 (2.30-4.68) (3.17-4.03) (3.18-4.02) TKN (mg/L) 1.62 2.5 1.9 3.18 2.11 2.32 (1.45-1.81) (2.23-2.76) (1.72-2.09) (2.84-3.50) (1.94-2.28) (2.20-2.44) DP (mg/L) 0.09 0.14 0.12 0.54 0.09 0.25 (0.08-0.10) (0.11-0.17) (0.09-0.15) (0.32-0.81) (0.07-0.11) (0.17-0.34) TP (mg/L) 0.38 0.46 0.25 0.39 0.68 0.44 (0.27-0.49) (0.29-0.63) (0.23-0.28) (0.34-0.44) (0.47-0.99) (0.37-0.52) TCu (ug/L) 14.92 26.83 30.79 82.11 27.11 41.76 (13.50-16.44) (24.18-29.42) (28.23-33.32) (60.65-114.55) (20.29-35.10) (34.68-51.86) TPb (ug/L) 18.26 31.29 26.24 61.6 77.63 44.08 (10.17-30.10) (26.36-36.73) (21.38-31.64) (53.81-70.28) (70.32-85.98) (40.37-48.32) TZn (ug/L) 98.02 152.1 172.72 329.63 142.98 189.93 (87.65-108.00) (133.09-170.64) (157.56- 188.22) (287.03- 382.57) (128.10- 157.58) (176.81-205.66) FC (MPN/100 mL) 6147.73 No Data 5625.2 8701.79 9215.27 8699.89 (300.00- 10333.33) (1700.00- 8575.00) (1794.64- 15786.44) (3519.61- 16607.39) (4518.54- 13556.63) E. coli (MPN/100 mL) No Data No Data No Data No Data 5948.28 6025.22 (1716.92- 12641.77) (1714.13- 12654.39) Table E-4. Means and confidence intervals for combined NSQD and HRDB data.

E-4 measurement pairs was set. PFC was the only BMP with only one study in the BMP Database. Table E-5 summarizes the number of data pairs by BMP and constituent. As shown, the PFC study had no data avail- able for OP, FC, or E. coli. OP was also not available for deten- tion basins and E. coli was not available for sand filters. Statistical Difference between Influent and Effluent Quality While some pollutants, such as TSS, are easily removed by a wide variety of BMPs, others, such as NO3-, are more difficult to remove. The non-parametric Wilcoxon signed- rank test was used to verify a statistical difference between influent and effluent quality for each BMP-pollutant pair in order to determine if removal of a pollutant was occurring in a BMP. Because this test requires a symmetric distribu- tion, the data were log-transformed prior to performing the analysis. As shown in Table E-6, several BMP-pollutant combinations involving nutrients and bacteria indica- tors show statistically significant concentration reductions (p>0.1 means no statistically significant reduction). In these instances, no removal would be assumed in the BMP Evalu- ation Tools. Monotonic Relationship between Influent and Effluent The next step in this process required establishing the pres- ence of a monotonic relationship between influent and effluent BMP – pollutant pair Yes Yes Yes No KTRL regression on 1) Ceff vs. Cinf, 2) Ceff vs. ln(Cinf), 3) ln(Ceff) vs. ln(Cinf). Select best fit. Monotonic relaonship (Spearman’s rho test)? Stascal difference between influent and effluent (Wilcoxon test)? Sufficient data for regression ( 3 disnct studies and 20 pollutant data pairs)? No No removal assumed for BMP pollutant NoCeff = mean effluent from BMP Database Use relaonship based on similar pollutant (DP uses OP data, E. coli uses FC data) Figure E-1. Analysis process for influent-effluent regression. Constituent BMP Type Bioretention Grass Swale Detention Basin Sand Filter PFC* TSS 171 195 265 296 22 NO3- 19 77 105 158 22 TKN 160 92 59 127 0 DP 167 151 176 270 22 OP 21 52 117 65 22 TP 123 26 34 99 0 TCu 214 191 245 286 22 TPb 67 119 191 267 22 TZn 54 138 193 248 22 FC 110 152 209 293 22 E. coli 26 79 109 121 0 *PFC pairs are based on paired watershed data as influent concentration for this BMP was unavailable. Table E-5. Number of data pairs by BMP and constituent.

E-5 quality. To do this, the Spearman’s rho test was applied to each BMP-pollutant combination. Those combinations showing a statistically significant difference between influent Cinf and Ceff generally exhibited a monotonic relationship between the two. The only exceptions were the swale-DP combi- nation and all available constituent data for PFC where a statistically significant monotonic relationship between Cinf and Ceff was not observed. In these cases, a regression analy- sis was not performed. However, since the Wilcoxon test results indicate a statistically significant reduction in DP for swales and a statistically significant reduction in all constituents except for NO3- and DP for PFC, the arithme- tic estimate of the log mean of effluent concentration data from the BMP Database was selected as an appropriate esti- mate of Ceff for these BMP-constituent combinations. Note that when implementing constant effluent concentrations in the BMP Evaluation Tool, the BMPs are assumed to never be a source of pollutants. Therefore, if Cinf is estimated to be less than Ceff , then no concentration reduction is assumed in the tool. As shown in Table E-7, the correlation analysis for PFC indicates that the effluent concentrations for all available pollutants are not correlated with the influent concentra- tions because no p-value is < 0.1. Viewing these results with the Wilcoxon signed rank test results, it is concluded that average effluent concentrations independent of influent concentrations are appropriate for all pollutants except for NO3- and DP. No removal will be assumed for these two constituents and no removal will also be assumed for E. coli due to lack of data. Constituent BMP Type Bioretention Grass Swale Detention Basin Sand Filter PFC TSS <0.001 0.023 <0.001 <0.001 <0.001 NO3- NA <0.001 <0.001 <0.001 0.118 TKN 0.037 0.485 <0.001 <0.001 <0.001 DP 0.035 <0.001 0.659 0.066 0.239 OP <0.001 <0.001 0.458 <0.001 NA TP 0.984 <0.001 <0.001 <0.001 <0.001 TCu <0.001 <0.001 <0.001 <0.001 <0.001 TPb <0.001 <0.001 <0.001 <0.001 <0.001 TZn <0.001 <0.001 <0.001 <0.001 <0.001 FC <0.001 0.525 0.007 <0.001 NA E. coli 0.026 0.128 <0.001 NA NA Values in bold indicate no statistically significant reduction from influent to effluent Table E-6. Wilcoxon signed-rank test p-values. Constituent BMP Type Bioretention Grass Swale Detention Basin Sand Filter PFC TSS 0.30 (<0.001) 0.46 (<0.001) 0.55 (<0.001) 0.41 (<0.001) 0.2 (0.286) NO3- NA 0.89 (<0.001) 0.79 (<0.001) 0.75 (<0.001) 0 (0.636) TKN 0.57 (<0.001) 0.73 (<0.001) 0.70 (<0.001) 0.71 (<0.001) 0.07 (0.389) DP -0.06 (0.786) 0.68 (<0.001) 0.67 (<0.001) 0.69 (<0.001) 0.05 (0.416) OP 0.46 (<0.001) 0.80 (<0.001) 0.67 (<0.001) 0.65 (<0.001) NA TP 0.38 (<0.001) 0.63 (<0.001) 0.66 (<0.001) 0.71 (<0.001) 0.36 (0.207) TCu 0.41 (<0.001) 0.81 (<0.001) 0.87 (<0.001) 0.61 (<0.001) 0.27 (0.245) TPb NA NA 0.90 (<0.001) 0.71 (<0.001) 0.29 (0.236) TZn 0.49 (<0.001) 0.82 (<0.001) 0.72 (<0.001) 0.43 (<0.001) 0.19 (0.291) FC 0.70 (<0.001) 0.83 (<0.001) 0.65 (<0.001) 0.70 (<0.001) NA E. coli 0.34 (0.012) 0.83 (<0.001) 0.58 (<0.001) NA NA Values in bold indicate no statistically significant correlation between influent and effluent Table E-7. Spearman’s rho test results (p-value in parentheses).

E-6 Regression Analysis of the Relationship between Influent and Effluent Based on the results of the Wilcoxon and Spearman’s rho tests, several BMPs appear to provide statistically significant reductions in pollutant concentrations along with mono- tonic influent/effluent relationships. These results together indicate that regression analyses can be conducted to develop functional relationships that can be used to predict BMP performance. Given the prevalence of outliers in environmental data and the strong influence these outliers can have on standard lin- ear regression techniques, the nonparametric Kendall-Theil robust line (KTRL) (Granato 2006) regression method was selected for this analysis. The KTRL method computes the slopes between all possible combinations of two data points and selecting the median of these slopes. A y-intercept is then calculated according to the formula: ( ) ( ) ( )= −  (Eq. 1)Intercept median y median slope median x Similar to linear regression, the calculation of slope and intercept creates a line of the form y = m ∗ x + b that can then be used as a generalized relationship between x and y. Pollutant concentrations in stormwater often exhibit a lognormal, rather than a normal distribution. Consequently, both linear and log-linear forms of the influent and effluent regression equations were considered in the analysis. Kendall- Theil robust lines were calculated for three possible relation- ships between influent and effluent, as shown in Table E-8. The median absolute deviation (MAD) was used to select the best regression equation for each BMP-pollutant combi- nation. This statistic is defined by: ( )= − (Eq. 2)MAD median C C for all values of Ceff predicted eff where Cpredicted is the value of the Ceff predicted by the Kendall- Theil regression line. Equation Selection and Regression Parameters Regression equations were developed using all available storm event data pairs for each BMP–pollutant combination where both a statistically significant reduction was observed (Wilcoxon) and a monotonic relationship was found. BMPs are assumed to not be a source of pollutants and thus effluent concentrations will not exceed the influent concentrations or load. Some BMPs can contribute to constituent concentra- tions, but including this assumption in the analysis introduced difficulty accounting for mass balance. Table E-9 summarizes the form of equation selected for each BMP–pollutant com- bination based on the hypothesis test results and the best fit regression equation. Based on the various possible influent-effluent relation- ships considered in Table E-6, a generalized equation was developed as follows: i i i [ ] ( )( ) = + + + + C min C , max A B C C ln C D C , (Eq. 3)E e DL eff inf inf inf inf i where Ceff is the predicted effluent concentration, Cinf is the predicted influent concentration, A, B, C, D, and E are param- eters of the equation, ei is the bias correction factor for equa- tion 3, and DL is the minimum detection limit observed for the available data sets. This equation ensures that BMPs are not a source of pollutants (e.g., Ceff is never greater than Cinf) and predicted effluent concentration is never below a reported detection limit (Tables E-10–E-14). The regression equations are used to represent the average performance for each BMP type. The BMP Evaluation Tools are not intended to model event-by-event loads. For a particu- lar site, the equations are used to produce a single average efflu- ent concentration given an average influent concentration. Hydrologic Modeling and Rainfall Data Analysis A large number of long-term continuous simulation modeling scenarios were performed using EPA SWMM5 to provide the hydrologic performance data for specific BMP configurations and locations that the user may desire to ana- lyze. Three hundred forty-three (343) National Climatic Data Center (NCDC) Cooperative Observer Program (COOP) rain gages with hourly rainfall data and covering all of the major climatic regions of the contiguous United States were selected for continuous simulation model runs. A variety of unit area storage volumes and drawdown characteristics were simulated for each rainfall record. Summary statistics, including the 85th and 95th percentile storm event depths and the average annual rainfall depth, were computed for each rain gage. The percentile storm events are used to scale modeling results to better match the site specific hydrology of user’s study area. The average annual rainfall depths are used to estimate the average annual runoff volume to a BMP. Data pairs plotted for KTRL Calculations KTRL Equation Derived Ceff , Cinf Ceff = m*Cinf + b Ceff , ln(Cinf) Ceff = m*ln(Cinf) + b ln(Ceff), ln(Cinf) ln(Ceff) =m*ln(Cinf) + b Table E-8. KTRL equations used for nonparametric regression.

Pollutant Bioretention Grass Swale Detention Basin Sand Filter PFC TSS 3 3 3 3 8 NO3- 4 1 1 1 4 TKN 2 4 1 1 8 TN 9 9 9 9 9 DP 8 1 4 1 4 TP 4 2 2 2 8 TCu 3 3 3 3 8 TPb 4 3 1 1 8 TZn 3 3 3 3 8 FC 3 4 3 3 4 E. coli 3 4 3 7 4 1 - KTRL regression of Ceff vs. Cinf. 2 - KTRL regression of Ceff vs. ln(Cinf). 3 - KTRL regression of ln(Ceff) vs. ln(Cinf). 4 - Failed Wilcoxon test or lack of data for analysis. No removal assumed. 5 - Insufficient data for DP analysis. KTRL line (Ceff vs. ln(Cinf) based on OP data. 6 - Insufficient data for DP analysis. OP data failed Wilcoxon test. No removal assumed. 7 - Insufficient paired data for analysis. Used data for fecal coliform to develop equation parameters for this BMP. 8 - Failed Spearman's test for monotonic relationship, but passed Wilcoxon test. Ceff = arithmetic estimate of log mean for all available effluent data in the BMP Database using regression-on-order statistics for handling non-detects followed by bootstrapping as described in Geosyntec and WWE (2012). 9 - To be determined by addition of NO3 and TKN (nitrite assumed negligible). Table E-9. Equation selection summary for BMP-pollutant combinations. Pollutant A B C D E ei DL TSS (mg/L) 0.00 0.00 0.00 2.49 0.37 1.35 0.00 NO3- (mg/L) 0.00 1.00 0.00 0.00 0.00 0.00 TKN (mg/L) 0.83 0.00 0.50 0.00 0.00 0.71 0.04 TN (mg/L) <-- TN = TKN + NO3 --> DP (mg/L) -0.82 0.00 0.00 0.00 0.00 0.03 TP (mg/L) 0.00 1.00 0.00 0.00 0.00 0.01 TCu (ug/L) 0.00 0.00 0.00 2.77 0.44 1.26 0.50 TPb (ug/L) 0.00 1.00 0.00 0.00 0.00 1.00 TZn (ug/L) 0.00 0.00 0.00 1.11 0.68 1.26 0.01 FC (col/100mL) 0.00 0.00 0.00 0.01 1.06 7.29 100.00 E. coli (col/100mL) 0.00 0.00 0.00 2.40 0.51 24.48 1.00 Table E-10. Equation parameters for predicting bioretention effluent concentrations. Pollutant A B C D E ei DL TSS (mg/L) 0.00 0.00 0.00 2.16 0.59 1.42 1.00 NO3- (mg/L) 0.13 0.73 0.00 0.00 0.00 0.10 TKN (mg/L) 0.32 0.68 0.00 0.00 0.00 0.02 TN (mg/L) <-- TN = TKN + NO3 --> DP (mg/L) 0.00 1.00 0.00 0.00 0.00 0.02 TP (mg/L) 0.41 0.00 0.14 0.00 0.00 0.02 TCu (ug/L) 0.00 0.00 0.00 0.94 0.84 1.10 0.10 TPb (ug/L) 0.60 0.36 0.00 0.00 0.00 0.10 TZn (ug/L) 0.00 0.00 0.00 1.87 0.71 1.06 0.01 FC (col/100mL) 0.00 0.00 0.00 11.37 0.66 2.60 1.00 E. coli (col/100mL) 0.00 0.00 0.00 2.84 0.65 2.89 1.00 Table E-11. Equation parameters for predicting detention basin effluent concentrations.

E-8 In addition to the 343 COOP rain gages, 40 Automated Surface Observing System (ASOS) rain gages with 5 minute rainfall data were analyzed. As described later in this section, the higher temporal resolution is needed for estimating the performance of flow-based BMPs, such as vegetated swales, where the volume treated is more of a function of the design flow rate than the available storage capacity. This analysis sup- plements continuous simulation modeling to provide a more complete estimate of the volume captured and volume lost for flow-based BMPs. The conceptual framework, simulation approach, and post-simulation computations are described below. Conceptual Framework Capture efficiency (or “percent capture”) is a metric that measures the percent of runoff that is captured and managed by a BMP (i.e., does not bypass or immediately overflow). Captured stormwater may be infiltrated, evapotranspired, or treated and released. Capture efficiency is typically expressed as an average capture rate over a long period, for example, average annual percent capture. Runoff volume that is not captured by a BMP is referred to as bypass or overflow and is assumed untreated. Volume reduction by a BMP can only occur when water is captured. Pollutant A B C D E ei DL TSS (mg/L) 0.00 0.00 0.00 5.74 0.45 1.35 0.50 NO3- (mg/L) 0.02 1.07 0.00 0.00 0.00 0.10 TKN (mg/L) 0.00 1.00 0.00 0.00 0.00 0.10 TN (mg/L) <-- TN = TKN + NO3 --> DP (mg/L) -0.01 1.41 0.00 0.00 0.00 0.02 TP (mg/L) 0.44 0.00 0.12 0.00 0.00 0.01 TCu (ug/L) 0.00 0.00 0.00 0.85 0.88 0.92 6.00 TPb (ug/L) 0.00 0.00 0.00 0.66 0.92 0.87 3.00 TZn (ug/L) 0.00 0.00 0.00 2.99 0.56 1.21 0.01 FC (col/100mL) 0.00 1.00 0.00 0.00 0.00 1000.00 E. coli (col/100mL) 0.00 1.00 0.00 0.00 0.00 0.00 Table E-12. Equation parameters for predicting swale effluent concentrations. Pollutant A B C D E ei DL TSS (mg/L) 0.00 0.00 0.00 1.38 0.46 1.69 0.50 NO3- (mg/L) 0.11 1.21 0.00 0.00 0.00 0.01 TKN (mg/L) 0.19 0.35 0.00 0.00 0.00 0.10 TN (mg/L) <-- TN = TKN + NO3 --> DP (mg/L) 0.02 0.69 0.00 0.00 0.00 0.02 TP (mg/L) 0.20 0.00 0.05 0.00 0.00 0.00 TCu (ug/L) 0.00 0.00 0.00 1.16 0.73 1.10 0.40 TPb (ug/L) 0.20 0.11 0.00 0.00 0.00 0.12 TZn (ug/L) 0.00 0.00 0.00 2.26 0.46 1.37 0.01 FC (col/100mL) 0.00 0.00 0.00 0.89 0.87 2.85 2.00 E. coli (col/100mL) 0.00 0.00 0.00 0.89 0.87 2.85 2.00 Table E-13. Equation parameters for predicting sand filter effluent concentrations. Pollutant A B C D E ei DL TSS (mg/L) 13.7 0 0 0 0 1 NO3- (mg/L) 0 1 0 0 0 0.04 TKN (mg/L) 1.11 0 0 0 0 0.4 TN (mg/L) <-- TN = TKN + NO3 --> DP (mg/L) 0 1 0 0 0 0.02 TP (mg/L) 0.086 0 0 0 0 0.02 TCu (ug/L) 13.0 0 0 0 0 2 TPb (ug/L) 0.84 0 0 0 0 0.5 TZn (ug/L) 25.8 0 0 0 0 5 FC (col/100mL) 0 1 0 0 0 1 E. coli (col/100mL) 0 1 0 0 0 1 Table E-14. Equation parameters for predicting PFC effluent concentrations.

E-9 When evaluating capture efficiency and volume reduction, each BMP can be considered to consist of a set of storage com- partments, each with a distinct volume, discharge rate, and pathway by which water discharges, i.e., surface discharge, infil- tration, evapotranspiration (ET). For example, a bioretention area with raised underdrain may have storage below the under- drain that would be considered retention storage (infiltrates, rather than leaving the project location via surface discharge). Ponded water and gravitational water temporarily held in the soil pore space would be considered detention storage (leaves primarily through the underdrain via surface discharge). Sim- ilarly, water not freely draining from pore spaces (e.g., plant available water) would be considered ET storage. Figure E-2 illustrates how ET, retention, and detention stor- age compartments were modeled. When storage capacity is available in a retention or detention storage compartment, then that compartment can capture additional inflow. When storage capacity is not available in either compartment, then inflowing water overflows or bypasses the system without treatment. The capture and volume reduction performance of a BMP are primarily a function of the amount of storage volume provided and the rate at which the storage drains to volume reduction pathways and surface discharge pathways. Two classes of storage compartments were simulated: con- sistent drawdown compartments (such as the retention and detention storage mentioned above) and seasonally variable drawdown compartments (such as ET storage). The approach taken is to model a range of unit storage volumes and draw- down characteristics for each type of compartment separately and then to post-process the modeling results to estimate the performance of a specific BMP. The conceptual representation of BMPs having discrete storage compartments allows for the development of a gen- eralized hydrologic model that only requires two parameters for estimating percent capture and volume reduction: • Normalized storage volume, expressed as an equivalent precipitation depth over the watershed that would produce a runoff volume equivalent to the compartment volume. For example, a 3,000 cu-ft storage volume for a watershed that is 1 acre with a runoff coefficient of 0.9 would translate to an equivalent precipitation depth of 0.92 inches [3,000 cu-ft × 12 in/ft / (1 ac × 43,560 sq-ft/ac × 0.9)]. Larger BMP sizes (storage volumes) relative to contributing area and imperviousness will provide a larger equivalent precipi- tation depth, which will allow them to bypass less volume (i.e., more capture). • Drawdown time for consistent drawdown. For BMP storage elements with nominally consistent drawdown rates regard- less of season (i.e., infiltration, filtration, orifice-controlled surface discharge), the representative drawdown time can be expressed in hours. For example, a bioretention area with a storage depth of 18 inches and an underlying design infil- tration rate of 0.5 inches per hour would have a drawdown time of 36 hours (18 inches / 0.5 in/hr). Similarly, a detention basin with a 50,000 cubic foot, a 4-foot average depth, and a single 3-inch orifice will drain in approximately 60 hours (based on an orifice coefficient of 0.6). BMPs with shorter drawdown times allow for larger volume reductions and percent captures. • Drawdown time for seasonally variable drawdown. For BMP storage elements with seasonally varying drawdown Lost Volume Inflow Overflow or Bypass Storage Volume Definitions Detention Storage Surface detention + Freely drained pore storage (above underdrain) Retention Storage Surface retention + Sump storage (below underdrain, if present) Total capture volume = Treated discharge + lost volume Freely drained pore storage = Porosity – Field capacity (FC) Retained soil moisture = Field capacity (FC) Wilting point (WP) Sump storage = Porosity of soil below underdrain Infiltration volume + Evapotranspiration volume Treated Discharge Discharged from treatment outlet (underdrain, riser, orifice, etc.) ET Storage Retained soil moisture (plant availablewater) Figure E-2. Conceptual representation of BMP storage compartments for purpose of estimating capture efficiency and volume reduction.

E-10 rates (i.e., storage drained by ET), the concept of a repre- sentative drawdown time is not applicable. In this case, the ET storage depth (i.e., the amount of potential ET that must occur for the ET storage to drain) is a more appropriate indicator of how quickly storage is recovered. By isolating these two most important predictive variables, a limited number of continuous simulation model runs and associated results can be used to describe the expected long- term performance of a wide range of BMP types and con- figurations. For example, the results of a long term model simulation for a 0.75-inch normalized storage depth with 24 hour drawdown would be representative of a wide range of different BMP configurations. The two examples below would both be reliably represented by this single model run: • Example 1: 20,000 cu-ft infiltration basin draining 8.2 acres of pavement (equates to 0.75-inch equivalent storm), with 3-foot ponding depth and a design infiltration rate of 1.5 inches per hour (equates to 24 hour drawdown time). • Example 2: 300 cu-ft bioretention area with underdrains with a tributary area of 0.122 acres of pavement (equates to 0.75-inch equivalent storm), with 12 inches of ponding storage depth and a design media filtration rate of 0.5 inches per hour (equates to a 24 hour drawdown time). Percent Capture and Volume Reduction Estimation An array of continuous simulation runs was executed in the EPA SWMM (version 5.0.022) to encompass the range of normalized storage volumes and drawdown times that were needed to simulate the variety of BMP types and design con- figurations considered for this effort. For each combination of design variables, the percent capture was calculated as: [ ]( )= −Percent Capture 100 1 (Eq. 4)V Vby c where: Vby = the total volume bypassed over the simulation period Vc = the total runoff volume flowing into the BMP over the simulation period Volume reduction efficiency refers to the portion of the “captured” volume that is lost to infiltration, ET, or consump- tive use and does not discharge directly to surface water. Within the tool, the following assumptions have been made: • For storage compartments without a surface discharge pathway (i.e., retention storage), the volume reduction efficiency was set to 100% (i.e., complete retention of all water that is captured). • For storage compartments with surface discharge as well as significant volume loss pathways, the volume reduction efficiency is estimated by computing the average loss rate as a fraction of the average total discharge rate. For example, if the average surface discharge rate during the drawdown period is 2 inches per hour and the average infiltration plus ET loss rate during that period is 0.5 inches per hour, then the vol- ume reduction efficiency would be estimated as 20 percent (0.5 / (2 + 0.5)). • For storage elements with only surface discharge pathways (i.e., lined systems with limited ET), then the volume reduc- tion efficiency is assumed to be zero. The volume estimated to be discharged from the primary treatment outlet (e.g., underdrain, riser, orifice, etc.) is assumed to be treated and having a concentration according to the estimated concen- tration for the particular BMP-pollutant combination. An example percent capture nomograph is shown in Fig- ure E-3. This is based on continuous hydrologic simulations using a 54-year hourly rainfall record (1954-2008) from the New Orleans International Airport. To use these graphs, the design volume (in watershed inches) and drawdown time (DDT) of each major storage volume must be estimated. The percent capture can then be estimated through visual interpolation. Number of Simulations A large number of SWMM model runs (58,310) were completed to develop the underlying database to support the BMP Evaluation Tools. Two types of modeling scenarios were conducted. Consistent drawdown scenarios were used to represent storage compartments that drawdown at a nominally con- stant rate throughout the year (i.e., not influenced signifi- cantly by seasonal variations in ET or use patterns). These runs can be used to represent compartments that drain to infiltration or surface discharge. Key variables include: • Climate station • Normalized storage volume • Drawdown time • Tributary area imperviousness • Tributary area soil type ET drawdown scenarios were used to represent storage compartments of BMPs that are regenerated via ET losses (i.e., are regenerated at different rates throughout the year). These runs can be used to represent the water stored in soil as well as water stored in cisterns that is applied at agronomic rates. Key variables include: • Climate station • Normalized storage volume • ET drawdown depth (i.e., the amount of ET that must occur for the ET storage to drain completely)

E-11 • Tributary area imperviousness • Tributary area soil type Table E-15 provides a summary of the supporting model runs that were executed to provide the database to support the tool. Key results from each SWMM run were extracted using automated routines to develop lookup databases indexed by the key parameters described in the table above. Rainfall Data Analysis for Flow Based BMPs For flow-based BMPs, such as vegetated swales, estimation of percent capture differs slightly from the approach used for volume-based BMPs. For volume-based BMPs, bypass occurs when the storage volume is exceeded. For flow-based BMPs, bypass or cessation of treatment occurs when the water qual- ity design flow rate is exceeded. With percent capture being only a function of instantaneous flow rates, nomographs can be developed simply by analyzing rainfall records and expressing design flow rates in terms of design storm intensi- ties. The volume captured by an online, flow-based BMP can be estimated by summing all flows less than or equal to the design flow rate. This assumes that once the design flow rate is reached, treatment effectively ceases. For offline BMPs, it can be assumed that a portion of all flows up to the design flow can be treated. Therefore, offline BMPs will tend to have a higher percent capture than online BMPs. To account for storage routing effects associated with the time of concentration of a watershed, various averaging peri- ods were used to aggregate the instantaneous intensities into average intensities prior to computing the volumetric percent captures. Nomographs were created for 40 ASOS rain gages by ana- lyzing five minute rainfall data from each gage to estimate the capture efficiency for various design intensities and times of concentration. Results are developed for both online (no treat- ment assumed to occur once the design flow rate exceeded) and offline BMP configurations. Each of the 343 COOP sta- tions is assigned one of the 40 ASOS gages based on proximity. Sample flow-based nomographs for Portland International Airport (PDX) show an online configuration (Figure E-4) and one offline configuration (Figure E-5) for a single BMP. Each data point on the nomographs reflects a percent of runoff captured by a BMP assuming a particular time of concentra- tion and design intensity. Using the nomographs below, the required design intensity required to achieve 80% capture, assuming a 10-minute time of concentration, is approxi- mately 0.21 in./hr for an online configuration and approxi- mately 0.12 in./hr for an offline configuration. As shown in the figures, choosing higher design intensities and times of concentration achieves higher percent capture. Figure E-3. Example percent capture for volume-based BMPs (New Orleans Airport).

E-12 Parameter Number of Increments Consistent Drawdown Model Runs (Infiltration, Surface Discharge) Climate Regions 343 Modeled Imperviousness of Tributary Area 1 Supported Imperviousness 0 to 100% (analog scale; more reliable above 25%) Modeled Soil Type Not Applicable (100% impervious) Supported Soil Type User can select between 4 soil texture classes or enter a user defined soil infiltration rate within the range supported. Storage Volume 10 Drawdown Time 10 Total – Consistent Drawdown Runs 34,300 ET Drawdown Model Runs Climate Regions 343 Modeled Imperviousness of Tributary Area 1 Supported Imperviousness 0 to 100% (analog scale; more reliable above 25%) Modeled Soil Type Not Applicable (100% impervious) Supported Soil Type User can select between 4 soil texture classes or enter a user defined soil infiltration rate within the range supported. Storage Volume 10 ET Depth Increments 7 Total – ET Runs 24,010 Table E-15. Summary of supporting model runs. 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 0.00 0.10 0.20 0.30 0.40 0.50 A ve ra ge A nn ua l C ap tu re E ffi ci en cy Design Intensity, in/hr 5 10 20 30 60 Time of Concentrations Figure E-4. Example flow-based nomograph—online configuration (Portland International Airport). Utilizing the Percent Capture Nomographs The continuous simulation modeling and post-processing described in the previous sections provide the basis for esti- mated average annual volume captured, reduced, and treated for a wide variety of climates, BMP types, and design configu- rations. The specific outputs from this process are summarized in Table E-16. The BMP Evaluation Tools query the nomograph results associated with the selected rain gage to estimate the approxi- mate volume treated and volume reduced for a BMP given the site location and planning level information about the drainage area and BMP design. Example 5-1 summarizes the approach used by the tools to complete the computations given user input. The example computations use the example nomograph presented in Figure E-6. Example 5-1 illustrates the process used by the BMP Evalua- tion Tools to estimate percent capture and percent volume loss using linear interpolation of the nomograph data. The BMP design volumes are stored as unitless values that have been

E-13 normalized by the 85th percentile, discrete storm event for the selected rain gage. These normalized values can be used to scale the nomographs for the selected rain gage to a particular location. Load Reduction Estimation Runoff loads and load reductions are computed by the BMP Evaluation Tools in a sequence of steps based on a mass balance approach as indicated in Figure E-7. Runoff loads are estimated as the product of the average annual runoff volume (Vw) and the characteristic runoff con- centration (Cw). The total estimated percent capture is used to determine the load bypassed (VbyCw) and influent load (VInfCw). Concentration reductions by the BMP are determined using the influent-effluent relationships described in Section 0 using the equation parameters for each BMP-pollutant combination shown in Tables 10 through 14. The effluent volume (VEff) is computed as the difference between in the influent volume (VInf) and volume reduction estimated from the nomographs (VRd). The effluent load is then the product of the effluent vol- ume and estimated effluent concentration (CEff). The combined discharge load and the load reductions are simply computed by applying a mass balance of the other terms. Information Provided for Load Reduction Estimation Source of Information Average annual rainfall volume Determined from analysis of rainfall record associated with the rain gage selected by user (or may be entered directly by the user) Runoff volume from tributary area Calculated using tributary area (user input), imperviousness (user input), and the average annual rainfall for the project site (based on rain gage selected, or optional user input). A volumetric runoff coefficient is computed using the following equation: where is the volumetric runoff coefficient, is the impervious fraction, and and are the parameters of the equation. The defaults for and are 0.225 and 0.129 when IMP<0.55, and 1.14 and -0.371 when IMP>0.55, respectively based on Granato (2006). Percent capture Determined by lookup, interpolation, and post-processing of the developed nomographs. Volume reduction (as percent of captured water) Determined by post-processing of continuous simulation percent capture results for retention, and ET compartments. Table E-16. Hydrologic analysis outputs used in calculating site-specific annual load reductions. 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 0.00 0.10 0.20 0.30 0.40 0.50 A ve ra ge A nn ua l C ap tu re E ffi ci en cy Design Intensity, in/hr 5 10 20 30 60 Time of Concentrations Figure E-5. Example flow-based nomograph—offline configuration (Portland International Airport).

E-14 Graphical operations supporting solution: 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2.0 Pe rc en t Vo lu m e Ca pt u re Storage BMP Design Volume (in) 1-Hour DDT 2-Hour DDT 3-Hour DDT 6-Hour DDT 12-Hour DDT 24-Hour DDT 36-Hour DDT 48-Hour DDT 72-Hour DDT 96-Hour DDT 120-Hour DDT 180-Hour DDT 240-Hour DDT 300-Hour DDT 360-Hour DDT 480-Hour DDT 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2.0 Pe rc en t Vo lu m e Ca pt u re Storage BMP Design Volume (in) 1-Hour DDT 2-Hour DDT 3-Hour DDT 6-Hour DDT 12-Hour DDT 24-Hour DDT 36-Hour DDT 48-Hour DDT 72-Hour DDT 96-Hour DDT 120-Hour DDT 180-Hour DDT 240-Hour DDT 300-Hour DDT 360-Hour DDT 480-Hour DDT Step 1 Step 2 Step 3 Step 4 Figure E-6. Graphical operations supporting Example 5.1.

E-15 Given: Drainage area = 1.5 acres Runoff coefficient of drainage area = 0.86 (computed) Effective area of bioretention = 1000 ft2 Depth of bioretention media = 3 ft Porosity of bioretention media = 0.4 Field capacity of bioretention media (fc) = 0.2 Wilting point of bioretention media (wp) = 0.1 Depth of surface ponding = 1 ft Media infiltration rate = 1.5 in/hr Subsurface soil infiltration rate = 0.1 in/hr Average evapotranspiration rate = 0.15 in/day Negligible sump storage Required: Estimate the capture efficiency and percent volume loss Solution: Since there is an underdrain and sump storage is negligible, a significant amount of the surface storage plus the freely drained pore storage will become treated discharge. The major components of the retention volume include: (V1) surface reten- tion plus freely drained pore storage and the (V2) retained soil moisture. Variables V1 = surface retention plus freely drained pore storage V2 = retained soil moisture d1 = surface retention plus freely drained pore storage as runoff storm depth in watershed inches d2 = retained soil moisture volume as runoff storm depth in watershed inches D1 = effective storage depth of surface retention plus freely drained pore storage D2 = effective storage depth of retained soil moisture DDT1 = brimful draw downtime of surface retention + freely drained pore storage assuming constant rate. DDT2 = brimful draw downtime of surface retention + freely drained pore storage assuming constant rate. Storage Volume Calculations: V1 = (1 ft × 1000 ft2) + ((0.4-0.2) × 3 ft × 1000 ft2) = 1,600 ft3 V2 = ((0.2-0.1) × 3 ft × 1000 ft2) = 300 ft3 Effective Storm Depth Calculations: d1 = (1,600 ft3 × 12 in./ft) / [0.86 × 1.5 acres × 43560 ft2/ac] = 0.34 watershed inches d2 = (300 ft3 × 12 in./ft) / [0.86 × 1.5 acres × 43560 ft2/ac] = 0.06 watershed inches Effective Storage Depth Calculations: D1 = 1 ft + ((0.4-0.2) ∗ 3 ft) = 1.6 ft D2 = ((0.2-0.1) ∗ 3 ft) = 0.3 ft Drawdown Time Calculations: DDT1 = 1.6 ft × (12 in./ft) / (1.5 in/hr) = 13 hrs (controlled by media infiltration rate) DDT2 = 0.3 ft × (12 in./ft) × (24 hrs/day) / (0.15 in/day) = 576 hrs (controlled by evapotranspiration) Total Percent Volume Capture for V1 plus V2 using Figure E-3. 1. For a design storm depth of 0.34 inches and a 13 hr DDT, the percent volume capture for V1 is approxi- mately 45%. 2. Identify the design storm depth associated with 45% on the 576 hr DDT curve: ~1.8 in. 3. Add d2 to this depth: 1.8 in + 0.06 in. = 1.86 in. 4. Identify the approximate percent capture off of a 576 hr DDT curve: ~47% 5. Total volume lost = 0.06 watershed inches Example 5-1: Computing Capture Efficiency for Bioretention with Underdrain

E-16 Whole Life Cost Tool Whole life costing (also known as life cycle cost analysis) is about identifying future costs and referring them back to pres- ent day costs using standard accounting techniques such as present value (PV). PV is defined here as “the value of a stream of benefits or costs when discounted back to the present time.” It can be thought of as the sum of money that needs to be spent today to meet all future costs as they arise throughout the life cycle of a facility. The formula for calculating the pres- ent value is from Weiss, Gulliver, and Erickson (2007): ( ) = + + − −       1 1 1 P A r i r i n where: P = present value of O&M ($) A = average annual O&M costs ($) r = annual inflation rate i = annual interest rate n = number of years The average rate of inflation can be estimated using the CPI. Between January 1990 and January 2010 the average annual inflation rate was 3.5%. The annual interest rate can be estimated from municipal bond yield rates. The current national average return rate for “A” rated municipal bonds with a 30-year maturity is 4.0%. The proper interest rate for DOTs is the interest rate that the Federal Reserve Bank charges on loans to institutions that borrow money from it, and it is generally very close to the inter- est rate that one would receive on short-term deposits (http:// www.fmsbonds.com/Market_Yields/index.asp; 4% rate based on data as of 4/24/2013). In these calculations, the under- lying objective is to determine how much money would have to be deposited in an interest bearing account to pay for all future capital and maintenance costs for a BMP installation. Consequently, the PV is very sensitive to the assumed interest and inflation rates and assumptions of future costs. An important consideration is that the formula calculates present value, assuming that average annual maintenance costs are fixed for the life of the facility. This is obviously not the case unless labor and material costs are constant, which is highly unlikely. Consequently, the tool also provides a cell for the user to input the rate at which these costs rise. The benefits from developing an accurate whole life cost include the following: • Improved understanding of long-term investment require- ments, in addition to capital costs • More cost-effective project choices for stormwater control selection • Explicit assessment and management of long term financial risk when integrated with a planned maintenance program • Better understanding of the future financial liabilities when considering acceptance of the responsibility for a system. All expenditures incurred by the DOT, whether they are termed operational or capital, result from the requirement to manage surface water runoff. Adopting a long-term approach complements the fact that most drainage assets have a rela- tively long useful life providing appropriate management and maintenance are performed. There are a series of stages in the life cycle of a drainage asset. A conceptual diagram of these stages is shown in Figure E-8. These stages represent ‘cost elements’ and can be defined as: Acquisition, which may include: • Feasibility studies – Conceptual design – Preliminary design – Detailed design and development • Construction (or purchase of a proprietary device) • Use and maintenance • Disposal/decommissioning. Economies of scale can be realized as project size increases, due to the existence of significant fixed initial costs such as mobi- lization of staff and equipment, and travel. To provide users with a better understanding of whole life costs as they relate to bridge deck BMP incorporation, a whole life cost (WLC) tool with a standard framework was developed for each BMP. The following sections discuss the WLC methodology and tool. WLC Tool Calculation Foundations The WLC tool presents an estimate of average or likely costs for an assumed set of conditions and characteristics that can be reviewed and adjusted for site-specific applications. Costs can be highly variable, and will depend, to a certain extent, on the size of the system being considered. The costs associated with BMPs incorporated for treatment of bridge deck runoff will include both capital and maintenance costs. Figure E-7. General approach for computing BMP load reductions.

E-17 The methodology and issues in determining these costs are presented in the following sections. Capital Costs Capital costs for BMPs include construction costs and vari- ous associated costs. Construction costs vary widely depend- ing on site constraints and other factors. Most U.S. cost studies assess only part of the cost of constructing a stormwater man- agement system, usually excluding permitting fees, engineer- ing design and contingency or unexpected costs. In general, these costs are expressed as a fraction of the construction costs (e.g., 30%). These costs are generally only estimates, based on the experience of designers. The cost of land varies regionally and often depends on surrounding land use. Many suburban jurisdictions require open space allocations within the developed site, reducing the effective cost of land for the control to zero for certain types of facilities. DOTs may have surplus ROW that can be used to locate a BMP. On the other hand, the cost of land, if surplus DOT ROW is not available, may far outweigh construction and design costs in dense urban settings. Actual capital costs for controls depend on a large num- ber of factors. Many of these factors are site-specific and thus are difficult to estimate; there are also regional cost differ- ences. Consequently, locally derived cost estimates are more useful than generic estimates made using national data. This document provides nationally derived values for planning purposes. The following is a brief description of some major factors affecting costs: • Project scale and unit costs. Stormwater controls can be built at much lower costs as part of a larger project rather than as stand-alone projects. • Retrofits vs. new construction. These two scenarios exhibit very different costs, with retrofit costs being much higher and uncertain. • Regulatory requirements. Each jurisdiction in the United States has varying requirements for treatment water quan- tity and quality volume. • Flexibility in site selection, site suitability. Stormwater con- trol cost can vary considerably due to local conditions (i.e., the need for traffic control, shoring, and availability of work area, existing infrastructure and/or site contamination). • Level of experience of both agency and contractors. Some regions in the United States have required and constructed stormwater controls for over 20 years. In these areas, local contractors adapt to the market and learn the skills needed to build the controls. • State of the economy at the time of construction. Another consideration is the strength of a local economy when a control is bid and built. If work is plentiful, the work may be less desirable and the cost may rise due to less competition. • Region. Region may influence the design rainfall and rain- fall-runoff characteristics of a site, which will in turn affect drainage system component sizing. • Land allocation and costs. The cost of land is extremely vari- able by location, both regionally and locally depending on surrounding land use. • Soil type/groundwater vulnerability. These will dictate whether infiltration methods can be used to dispose of excess runoff volumes on site, or whether additional stor- age and attenuation will be required. • Planting. The availability of suitable plants and required level of planting planned for a particular control compo- nent will have a significant influence on costs, including irrigation and maintenance requirements. Figure E-8. Life cycle stages and associated costs (Lampe et al., 2005).

E-18 An important consideration when assessing cost is what would be constructed in lieu of the selected practice. For instance, engineered swales are typically a much less expen- sive option for stormwater conveyance than the curb and gut- ter systems they replace, which leads to the conclusion that these water quality benefits facilities are effectively free, since some type of system is required for drainage purposes. Con- sequently, one should consider only the net cost attributable to the water quality component. It should be noted, however, that net costs can be difficult to generalize because the deter- mination of what would be constructed in lieu of a practice can be very site specific. Maintenance Costs Maintenance is a necessary activity required to preserve the intended water quality benefit and stormwater convey- ance capacity of stormwater controls. However, there is often little planning regarding future maintenance activities and the financial and staff resources that will be needed to perform these activities. Maintenance costs, often assumed to be con- stant for a given type of BMP, can actually have a wide range depending on the pollutant loading rate as well as the aesthetic and safety needs of the maintenance crew and public living, driving, or working on/near them. At many sites, vegetation management constitutes the majority of maintenance activities, rather than tasks one might expect such as sediment, debris and trash removal, or structural repair. The frequency of mowing and other vegeta- tion management activities may have little effect on storm- water control performance, but result from the expected level of service by residents living near these facilities or by regula- tory requirements. For example, tall vegetation can decrease the line of sight and dry vegetation can become a fire hazard. The frequency of maintenance has been found to depend on the surrounding land use with more maintenance requests generated in urban areas. Consequently, the expected main- tenance cost for a given type of facility can vary significantly depending on the expectations of the nearby community. Two general maintenance categories have been established in the WLC tool: (1) routine and (2) intermittent. Routine maintenance consists of basic tasks performed on a frequent and predictable schedule. These include inspections, vegetation management, and litter and minor debris removal. In addition, three levels of routine maintenance can be identified and these relate mainly to frequency of the activity being undertaken. These are defined as: • Low/Minimum: A basic level of maintenance required to maintain the function of the stormwater control. • Medium: The normal level of maintenance to address func- tion and appearance. Allows for additional activities, includ- ing preventative actions, at some facilities. • High: Frequent maintenance activities performed as a result of high sediment loads, wet climate, and other factors such as safety and aesthetics. Intermittent maintenance typically consists of correc- tive and infrequent maintenance activities. These are typi- cally more resource intensive and unpredictable tasks to keep systems in working order, such as repair of structural damage and regrading eroded areas. In some cases, complete facility reconstruction may be required. The intermittent category can include a wide range of tasks that might be required to address maintenance issues at a BMP (invasive species removal, animal burrow removal, forebay cleanout, etc.). The tool will calculate costs individually for routine BMP maintenance items while corrective and infrequent items are calculated as a generalized cost since these maintenance activities are typically unplanned. For detention basins that will be used for dual-use stormwater and spill control sys- tems, additional cost for corrective and infrequent mainte- nance should be added to reflect the costs for pumping and cleanup efforts that would be incurred in the event that the basin was actually used to contain a hazardous spill. While it has not been attempted to identify possible corrective and infrequent (unplanned) maintenance activities for each BMP, the following routine (planned) maintenance activities have been identified in Table E-17. New vs. Retrofit Costs In a report prepared by the URS Corporation (2012) for the NCDOT, “Stormwater Runoff from Bridges: Final Report to Joint Legislation Transportation Oversight Committee,” URS evaluates the adjustment required when estimating costs for stormwater retrofit projects for bridges compared to new construction of the same design. To provide a comparison, URS evaluated 16 NCDOT retrofit projects and determined the percent increase in cost compared to an identical new construction project. The retrofit specific costs were project costs that would have likely been absorbed by a new construction project including mobilization, surveying, and traffic control. These retrofit-specific costs were deducted from the total retrofit cost to develop an estimated new construction cost. From these 16 retrofit projects (construction costs ranged between $7,336 and $246,780), the increase of cost due to retrofits was found to be 17% on average, with a range between 8 and 33% (URS Corporation 2012). The same methodology used in the 2012 URS report to determine the percentage increase due to retrofit was applied to the Center Street and Marion Street Bridge Stormwater Retrofit project starting construction in 2013 in Salem, OR. This project’s total estimated construction cost was $802,206

E-19 Table E-17. BMP routine maintenance tasks. BMP Routine Maintenance Task Swale Remove sediment accumulation in swale bottom Remove Trash and Debris Check for standing water and repair Remove clogging if necessary Restore vegetative cover where required Repair/check dams Mow to maintain ideal grass height Remove invasive and woody vegetation Repair minor erosion/scour Till Swale bottom Dry Detention Basin Remove sediment accumulation in basin Remove Trash and Debris Check for embankment erosion Check for animal burrows and repair Remove invasive and woody vegetation Mow to maintain ideal grass height Check for standing water and repair Check for settling of berm and repair Check inlets/outlets for obstructions Restore vegetative cover where required Stabilize banks and channels Check for erosion on spillway and repair rip rap Ensure low flow channel is clear of obstructions Bioretention Remove sediment accumulation in basin Remove Trash and Debris Fertilize and maintain basin vegetation Repair minor erosion/scour Check for standing water and repair Check inlets/outlets for obstructions Add mulch if necessary Remove invasive and woody vegetation Sand Filter Remove sediment buildup in filter bed Remove trash and debris Check for leaks and noticeable odors Inspect condition of structural components Remove invasive and woody vegetation Check for standing water and repair Check inlets/outlets for obstructions Bridge Scupper Clean trash and debris Clean sediment Visual inspection of damage and repair Open Graded Friction Course Overlay High pressure air/water or vehicles to unclog pores Check for localized dams within the overlay course Pontoons, Tanks, Vaults Remove sediment accumulation Check inlets and outlets for obstructions Pipes Check for obstructions/sediment and flush Check for leaks and repair Check fittings and connections and repair Check for pipe settling and repair Berms and Baffles Check for damage or misplacement Replace (baffles) or repair (berms) when required Skimmers and Booms Check for damage or misplacement Replace or repair skimmer when required Replace absorbent boom when capacity is reached Valve Controls Remove sediment Remove trash and debris Inspect all components Lubricate as required Check for leaks Test operation Liners Visual inspection for holes and other irregularities Inspect backfill for settling Check for potential animal/vegetation damage Check anchors and seams if applicable RTCs Remove sediment/debris from sensors or valve Remove trash and debris Replace small parts Repair valves/other equipment Inspect all components Web/monitoring services Troubleshooting

E-20 and the stormwater retrofit-specific costs were estimated to be $102,040, resulting in a 13% increase from the esti- mated new construction cost due to the project being built as a retrofit. This lower percent difference from the average found in the URS report is likely due to the fact that this is a much larger retrofit project compared to the 16 projects evaluated for the URS report, with corresponding lower unit prices. In general, retrofits have higher costs associated with them because retrofit projects are usually smaller, and unit prices are typically higher for smaller material quantities. Addition- ally, design costs for retrofits were estimated at 150% of new construction costs, primarily because retrofits are designed as separate, individual projects including its own site visits, sur- veying, utility locates, and bidding process. Retrofits can also have unforeseen costs such as difficult site drainage or other difficulties that may not be encountered with a new construc- tion project (URS Corporation 2012). From evaluation of the URS report and application of the report methodology to a recent bridge stormwater retrofit project, it appears that 10 to 30% of the new construction cost is a reasonable range to represent the additional costs attrib- uted specifically to stormwater retrofit projects for bridges. RTC Capital and Annual Maintenance Costs Typical stormwater BMPs and BMP components are com- mon, and capital costs should be easily identifiable in the event they are needed for inclusion in the WLC tool and are not already listed. The exception to this is the potential future use of real time controls (RTCs), which is an uncommon, new technology for bridge deck runoff mitigation with variable capital and maintenance costs (Table E-18). BMP Life-Cycle vs. Bridge Life-Cycle The life-cycle for pipes and conveyance systems is gener- ally much shorter than that of the typical bridge structure itself. Although the difference may vary with the selection of materials and systems used, the life span for such systems is typically about 25 years to replace the whole system versus an over 50-year bridge life. Therefore, implementation of BMPs for bridge deck runoff mitigation should consider future ret- rofit and/or replacement issues. Whole life costs provided in the tool are for the BMPs themselves and do not consider future replacement requirements. WLC Tool Calculator Guide The WLC tool consists of a series of Excel spreadsheets for a variety of stormwater treatment practices that are inte- grated into the BMP Evaluation Tool. The development of these spread sheets was initially supported by the Water Envi- ronment Research Foundation and described by Lampe et al. (2005) and Pomeroy (2009). The spreadsheets have been revised for this project by including DOT specific values for many of the required fields. The tool provides a framework for the calculation of capital and long-term maintenance costs associated with individual BMPs based on national averages. Local data can be used to adjust the estimates by the user. Multi-system and regional solutions will generally be built up from a number of different components, from source control to site and regional control facilities. Several spreadsheets may then be required, and costs will be built up by adding together outputs. Care should be taken to include all—but not duplicate any—relevant costs between individual BMP spreadsheets. Costs for improvements that would have otherwise been required for an operational RTC Cost Description Estimated Average Cost Capital Design, Fabrication, and Procurement Modeling, sizing, fabricating and testing the system $9,000 Coordination and Installation Installation at the site and testing $8,500 Equipment Valves, sensors, controller enclosures, and other components such as conduits and piping $7,000 Operation and Maintenance (annual) Web / Monitoring Services Monitoring real time data and controls $8,000 Misc. Maintenance Clean accumulated debris, inspect components, etc. $7,000 Troubleshooting Internet connectivity, system logic, power issues, etc. $2,000 Table E-18. RTC capital and annual maintenance costs.

E-21 facility had the BMP not been built should also be computed and subtracted as appropriate from the final BMP WLC. Costs are calculated using unit prices developed from DOT bid tabulations that reflect average values of costs, RS Means 100. This option is a “first cut” for cost analysis and should be used cautiously and as a starting point. Users are encouraged to substitute local values, where known, so that the estimates more accurately reflect actual site conditions. Basic cost dynamics are made apparent by this application, such as the relative importance of capital cost versus mainte- nance costs for different BMPs. In addition, the tool provides estimates of the annual outlay, so agencies responsible for maintenance will be able to estimate future resource needs and maintain these facilities in proper working order. For practitioners who are using the tool to compare BMPs, many of the potential problematic assumptions or errors will cancel. Consequently, the best use of the cost tool is to compare the WLC of various options rather than to compute explicit costs and values for capital or O&M budget purposes. Using this approach, various practices can be easily compared to determine the most cost effective option for improving stormwater runoff quality. Each spreadsheet tool includes several sheets for the user to input information on the design, capital costs, and mainte- nance costs. The content of the sheets is described in Table E-19. Whole Life Cost Tool Inputs The model user will likely want to start with a basic, default scenario and then build in user entered, site-specific informa- tion as available. Again, given the significant differences in system design requirements and regional cost variables (e.g., labor costs, frequency of maintenance due to variation in cli- mate, etc.), it is difficult to generalize for the entire United States using default values. When parametric equations are used to drive capital cost estimates, the regions of the original cost data are listed in each tool’s respective “design and cost information” sheets. The user can also enter custom values for virtually every component tracked by the spreadsheet: system design and sizing, capital costs, and maintenance costs. This option best reflects costs for a given geographical area and site condi- tions. The user can employ a combination of default and user entered values as desired. Site-specific costs and characteristics should be entered into the spreadsheet wherever available. As an example, all references to RS Means costs assume the RS Means 100 cost. RS Means 100 is a representation of cost based on the his- torical national average of construction costs that can be adjusted to a specific location and time by multiplying the RS Means 100 cost by location and time factors. A first step Table E-19. Data entry requirements of each spreadsheet section. Sheet Title Spreadsheet Description Project Options Requires inputs needed for the parametric cost estimations and WLC calculations. For example the Bioretention Tool required input include: Local RS Means scaling factor to adjust for regional cost differences Expected level of maintenance (H, M, L) Design Life (years) Discount rate (used in the WLC computation) Inflation rate for labor and materials Sales tax User option to display capital and maintenance cost inputs, which are hidden by default All of these inputs are essential user-entry. Model default values are available for all cells, but should be overridden with site-specific data wherever possible. Capital Costs Display this sheet by selecting “yes” in the “Would you like to view/edit capital cost inputs?” on the Project Options tab. Calculates the facility base costs and associated capital costs (e.g., engineering, land, etc.), based on the design parameters provided on the Project Design tab. Default values are provided for unit costs; however, the user can also enter specific unit costs and quantities. Maintenance Costs Display this sheet by selecting “yes” in the “Would you like to view/edit maintenance cost inputs?” on the Project Options tab. Calculates the ongoing costs associated with the operation of the system. The following costs are included: Routine, scheduled maintenance. Corrective maintenance (e.g., periodic repair). Infrequent maintenance (e.g., sediment removal). Users can adjust existing and create new categories. Whole Life Costs This sheet is hidden by default, but the user can open it by right clicking on any tab and selecting “unhide”. The sheet presents a time series of the costs for the system and computes the present value of these costs. These annual costs can be useful for budgeting for future maintenance requirements Whole Life Cost Summary This sheet summarizes the maintenance and capital cost inputs and provides the Present Value of Cost over time as a graph, along with Cumulative Discounted Cost and Discounted Cost Over Time.

E-22 in improving the accuracy of a user-created cost estimate would be for the user to multiply these unit costs by the appropriate location factor, adjust to the current year using a similar factor, then enter the product in the “user entered” column. As a minimum, the assumptions and costs compo- nents should be reviewed for appropriateness prior to model application in a generic mode. Table E-20 provides an example of the Design and Main- tenance Worksheet for bioretention systems. Cells shaded yellow provide fields for the model user to input site specific information for the various model parameters. In the tool, the parameters are imported automatically from the BMP performance spreadsheets. The level of maintenance is a function of sediment load and climatic conditions for the site of interest. Table E-21 presents the worksheet used to estimate capi- tal costs for the facility. The default Baseline Unit Costs were developed by examining DOT bid tabulations and adjusting to an RS Means value of 100. The Adjusted Unit Cost is the default baseline adjusted for the RS Means value at the project location. The quantities of each element are calculated auto- matically based on the size and design of the facility speci- fied in the BMP Performance worksheets. Associated Capital Costs are calculated as a fraction of the construction cost. Table E-22 allows the user to adjust default maintenance parameters, such as task frequency, crew size, hourly rate, and other factors. The lower portion of the worksheet is a Lookup table (currently hidden in rows 58–69) that pro- vides the default values that depend on the expected level of maintenance. Table E-20. Project options worksheet.

E-23 Table E-21. Example capital cost worksheet.

Table E-22. Example maintenance worksheet.

E-25 WLC Tool Outputs The WLC model summarizes the expected annual costs on the Whole Life Cost worksheet (hidden by default) as shown in Table E-23. This sheet allows the user to budget future expenditures. The WLC Summary sheet provides the capital costs and the cost per year for maintenance activities as shown in Table E-24. It also provides the total cost discounted to present value in tabular format, as well as a graph depicting the time related expenditures as shown in Figure E-9. In addition, the model provides the cumulative WLC in graphic format, which is Whole Life Costs Cash Present Value Cash Sum ($) 571864.78 170119.50 0.00 1.00 20063.04 20063.04 20063.04 20063.04 20063.04 1.00 0.95 1.03 0.00 3120.00 0.00 3213.60 3213.60 3060.57 23276.64 23123.62 2.00 0.91 1.06 0.00 3120.00 0.00 3310.01 3310.01 3002.27 26586.65 26125.89 3.00 0.86 1.09 0.00 3120.00 0.00 3409.31 3409.31 2945.09 29995.96 29070.98 4.00 0.82 1.13 0.00 3120.00 6740.00 11097.52 11097.52 9129.95 41093.48 38200.93 5.00 0.78 1.16 0.00 3120.00 0.00 3616.94 3616.94 2833.96 44710.41 41034.90 6.00 0.75 1.19 0.00 3120.00 0.00 3725.44 3725.44 2779.98 48435.86 43814.88 7.00 0.71 1.23 0.00 3120.00 0.00 3837.21 3837.21 2727.03 52273.06 46541.91 8.00 0.68 1.27 0.00 3120.00 6740.00 12490.35 12490.35 8453.96 64763.42 54995.87 9.00 0.64 1.30 0.00 3120.00 0.00 4070.89 4070.89 2624.13 68834.31 57620.01 10.00 0.61 1.34 0.00 3120.00 0.00 4193.02 4193.02 2574.15 73027.33 60194.16 11.00 0.58 1.38 0.00 3120.00 0.00 4318.81 4318.81 2525.12 77346.14 62719.28 12.00 0.56 1.43 0.00 3120.00 6740.00 14058.00 14058.00 7828.02 91404.14 70547.30 13.00 0.53 1.47 0.00 3120.00 0.00 4581.83 4581.83 2429.84 95985.96 72977.14 14.00 0.51 1.51 0.00 3120.00 0.00 4719.28 4719.28 2383.56 100705.24 75360.69 15.00 0.48 1.56 0.00 3120.00 0.00 4860.86 4860.86 2338.16 105566.10 77698.85 16.00 0.46 1.60 0.00 3120.00 6740.00 15822.41 15822.41 7248.43 121388.51 84947.28 17.00 0.44 1.65 0.00 3120.00 0.00 5156.88 5156.88 2249.93 126545.39 87197.21 18.00 0.42 1.70 0.00 3120.00 0.00 5311.59 5311.59 2207.08 131856.98 89404.28 19.00 0.40 1.75 0.00 3120.00 0.00 5470.94 5470.94 2165.04 137327.92 91569.32 20.00 0.38 1.81 0.00 3120.00 6740.00 17808.26 17808.26 6711.74 155136.18 98281.07 21.00 0.36 1.86 0.00 3120.00 0.00 5804.12 5804.12 2083.34 160940.30 100364.41 22.00 0.34 1.92 0.00 3120.00 0.00 5978.24 5978.24 2043.66 166918.54 102408.07 23.00 0.33 1.97 0.00 3120.00 0.00 6157.59 6157.59 2004.73 173076.13 104412.81 24.00 0.31 2.03 0.00 3120.00 6740.00 20043.35 20043.35 6214.80 193119.48 110627.61 25.00 0.30 2.09 0.00 3120.00 0.00 6532.59 6532.59 1929.09 199652.07 112556.70 26.00 0.28 2.16 0.00 3120.00 0.00 6728.56 6728.56 1892.35 206380.63 114449.04 27.00 0.27 2.22 0.00 3120.00 0.00 6930.42 6930.42 1856.30 213311.05 116305.34 28.00 0.26 2.29 0.00 3120.00 6740.00 22558.97 22558.97 5754.65 235870.02 122059.99 29.00 0.24 2.36 0.00 3120.00 0.00 7352.48 7352.48 1786.26 243222.51 123846.25 30.00 0.23 2.43 0.00 3120.00 0.00 7573.06 7573.06 1752.24 250795.56 125598.49 31.00 0.22 2.50 0.00 3120.00 0.00 7800.25 7800.25 1718.86 258595.82 127317.35 32.00 0.21 2.58 0.00 3120.00 6740.00 25390.32 25390.32 5328.57 283986.13 132645.91 33.00 0.20 2.65 0.00 3120.00 0.00 8275.29 8275.29 1654.00 292261.42 134299.92 34.00 0.19 2.73 0.00 3120.00 0.00 8523.54 8523.54 1622.50 300784.96 135922.41 35.00 0.18 2.81 0.00 3120.00 0.00 8779.25 8779.25 1591.59 309564.21 137514.01 36.00 0.17 2.90 0.00 3120.00 6740.00 28577.02 28577.02 4934.04 338141.24 142448.04 37.00 0.16 2.99 0.00 3120.00 0.00 9313.91 9313.91 1531.54 347455.14 143979.58 38.00 0.16 3.07 0.00 3120.00 0.00 9593.32 9593.32 1502.37 357048.47 145481.95 39.00 0.15 3.17 0.00 3120.00 0.00 9881.12 9881.12 1473.75 366929.59 146955.70 40.00 0.14 3.26 0.00 3120.00 6740.00 32163.69 32163.69 4568.71 399093.29 151524.41 41.00 0.14 3.36 0.00 3120.00 0.00 10482.88 10482.88 1418.14 409576.17 152942.55 42.00 0.13 3.46 0.00 3120.00 0.00 10797.37 10797.37 1391.13 420373.54 154333.68 43.00 0.12 3.56 0.00 3120.00 0.00 11121.29 11121.29 1364.63 431494.83 155698.31 44.00 0.12 3.67 0.00 3120.00 6740.00 36200.52 36200.52 4230.44 467695.35 159928.75 45.00 0.11 3.78 0.00 3120.00 0.00 11798.58 11798.58 1313.14 479493.93 161241.89 46.00 0.11 3.90 0.00 3120.00 0.00 12152.54 12152.54 1288.13 491646.47 162530.02 47.00 0.10 4.01 0.00 3120.00 0.00 12517.11 12517.11 1263.59 504163.58 163793.62 48.00 0.10 4.13 0.00 3120.00 6740.00 40744.00 40744.00 3917.21 544907.58 167710.83 49.00 0.09 4.26 0.00 3120.00 0.00 13279.40 13279.40 1215.91 558186.99 168926.74 50.00 0.09 4.38 1.00 3120.00 0.00 13677.79 13677.79 1192.75 571864.78 170119.50 Cumulative Costs Year Discount Factor Cost Escalation Capital & Assoc. Costs Regular Maint. Costs Base Corrective Maint. Escalated Maint. Cost Total Costs Present Value of Costs Table E-23. Example whole life cost.

E-26 Whole Life Cycle Costs Summary Total Facility Base Cost Total Associated Capital Costs (e.g., Engineering, Land, etc.) Capital Costs Inspection, Reporting & Information Management 0.5 $180 $360 Vegetation Management with Trash & Minor Debris Removal 0.5 $1,380 $2,760 add additional activities if necessary 0 $0 $0 add additional activities if necessary 0 $0 $0 Totals, Regular Maintenance Activities $3,120 Corrective Maintenance 4 $6,740 $1,685 add additional activities if necessary 0 $0 $0 add additional activities if necessary 0 $0 $0 Totals, Corrective & Infrequent Maintenance Activities $1,685 Capital Costing Method Assumed Level of Maintenance Estimated Capital Cost, $ (2013) Estimated NPV of Design Life Maintenance Costs, $ (2013) Estimated NPV of Design Life Whole Life Cycle Cost, $ (2013) Estimated Annualized Whole Life Cycle Cost, $/yr (2013) Totals are based on design life with routine and major maintenance. Line Item Engineer's Estimate CORRECTIVE AND INFREQUENT MAINTENANCE ACTIVITIES (Unplanned and/or >3yrs. betw. events) CAPITAL COSTS Total Cost REGULAR MAINTENANCE ACTIVITIES Years between Events Total Cost per Visit Total Cost per Year $20,063 $7,181 $12,882 Total Cost per Year Total Cost per VisitYears between Events $4,502 $112,557 $92,494 $20,063 H Table E-24. Example whole life cost summary. $0 $5,000 $10,000 $15,000 $20,000 $25,000 0.00 4.00 8.00 12.00 16.00 20.00 24.00 28.00 32.00 36.00 40.00 44.00 48.00 N et Pr es en t Va lu e Year Net Present Value over Time Figure E-9. Example present value of costs graph.

E-27 shown in Figure E-10. The WLC for a variety of BMPs can then be calculated and compared to determine the least cost alternative for a given scenario. References Efron, B. and Tibishirani, R. (1993). An Introduction to the Bootstrap. Chapman & Hall, New York. Geosyntec and Wright Water Engineers (WWE) (2012). International Stormwater Best Management Practices (BMP) Database Pollutant Category Summary Statistical Addendum: TSS, Bacteria, Nutrients, and Metals. Prepared for the Water Environment Research Foun- dation. http://www.bmpdatabase.org/performance-summaries. html. Granato, G. E. (2006). Kendall-Theil Robust Line (KTRLine— version 1.0)—A Visual Basic Program for Calculating and Graphing Robust Nonparametric Estimates of Linear-Regression Coefficients Between Two Continuous Variables, Techniques and Methods of the US Geological Survey, Book 4, Chap. A7, 31 p. http://pubs.usgs.gov/ tm/2006/tm4a7/ Granato, G. E., and P. A. Cazenas (2009). Highway-Runoff Database (HRDB Version 1.0)—A Data Warehouse and Preprocessor for the Stochastic Empirical Loading and Dilution Model, Washing- ton, D.C., U.S. Department of Transportation, Federal Highway Administration, FHWA-HEP-09-004, http://webdmamrl.er.usgs. gov/g1/FHWA/SELDM.htm Helsel, D. R. and Cohn, T. A. (1988). Estimation of Descriptive Statis- tics for Multiply Censored Water Quality Data. Wat. Res. Research, 24(12): 1997-2004. Helsel, D. R. and Hirsch, R. M. (2002). Statistical Methods in Water Resources. U.S. Geological Survey, Techniques of Water-Resources Investigations Book 4, Chapter A3. Water Resources Division, USGS. Reston, VA. Lampe, Barrett, et al. (2005). Performance and Whole Life Costs of Best Management Practices and Sustainable Urban Drainage Systems, Project 01-CTS-21Ta. Alexandria, VA: Water Environment Research Foundation. Pitt, R. (2008). National Stormwater Quality Database (NSQD) Version 3 Spreadsheet. [Accessed 4/25/2013] http://rpitt.eng.ua.edu/Research/ ms4/mainms4.shtml Smith, K. P., and Granato, G. E. (2010). Quality of Stormwater Runoff Discharged from Massachusetts Highways, 2005–07, US Geological Survey Scientific Investigations Report 2009–5269. http://pubs.usgs. gov/sir/2009/5269/ URS Corporation (2012). Stormwater Runoff from Bridges: Final Report to Joint Legislation Transportation Oversight Committee. Prepared for NC Department of Transportation (NCDOT), May. Weiss, P. T., Gulliver, J. S., and Erickson, A. J. (2007). Cost and Pol- lutant Removal of Storm-Water Treatment Practices. Journal of Water Resources Planning and Management. $0 $20,000 $40,000 $60,000 $80,000 $100,000 $120,000 $140,000 $160,000 $180,000 0.00 5.00 10.00 15.00 20.00 25.00 30.00 35.00 40.00 45.00 50.00 Cu m ul a ve N et Pr es en tV al ue Year Net Present Value Cumulave Figure E-10. Example cumulative discounted costs graph.

Abbreviations and acronyms used without definitions in TRB publications: A4A Airlines for America AAAE American Association of Airport Executives AASHO American Association of State Highway Officials AASHTO American Association of State Highway and Transportation Officials ACI–NA Airports Council International–North America ACRP Airport Cooperative Research Program ADA Americans with Disabilities Act APTA American Public Transportation Association ASCE American Society of Civil Engineers ASME American Society of Mechanical Engineers ASTM American Society for Testing and Materials ATA American Trucking Associations CTAA Community Transportation Association of America CTBSSP Commercial Truck and Bus Safety Synthesis Program DHS Department of Homeland Security DOE Department of Energy EPA Environmental Protection Agency FAA Federal Aviation Administration FHWA Federal Highway Administration FMCSA Federal Motor Carrier Safety Administration FRA Federal Railroad Administration FTA Federal Transit Administration HMCRP Hazardous Materials Cooperative Research Program IEEE Institute of Electrical and Electronics Engineers ISTEA Intermodal Surface Transportation Efficiency Act of 1991 ITE Institute of Transportation Engineers MAP-21 Moving Ahead for Progress in the 21st Century Act (2012) NASA National Aeronautics and Space Administration NASAO National Association of State Aviation Officials NCFRP National Cooperative Freight Research Program NCHRP National Cooperative Highway Research Program NHTSA National Highway Traffic Safety Administration NTSB National Transportation Safety Board PHMSA Pipeline and Hazardous Materials Safety Administration RITA Research and Innovative Technology Administration SAE Society of Automotive Engineers SAFETEA-LU Safe, Accountable, Flexible, Efficient Transportation Equity Act: A Legacy for Users (2005) TCRP Transit Cooperative Research Program TEA-21 Transportation Equity Act for the 21st Century (1998) TRB Transportation Research Board TSA Transportation Security Administration U.S.DOT United States Department of Transportation