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TE66?.A3no.FHW.A-RD-81 -044
No. FHWA/RD-81/044
ISTITUENTS OF HIGHWAY RUNOFF
Vol. III. Predictive Procedure for Determining Pollutant
Characteristics in Highway Runoff
February 1981
Final Report
2JJS&S*
J^rfsot,k
Document is available to the public through
the National Technical Information Service,
Springfield, Virginia 22161
Prepared for
FEDERAL HIGHWAY ADMINISTRATION
Offices of Research & Development
Environmental Division
Washington, D.C. 20590
FOREWORD
This report is composed of six volumes: Volume 1documents the con-
It tuents of highway stormwater runoff and their pollut.ona effects;
Volume it contains detailed procedures for conducting a monitor mg
and analysis program for highway runoff pollutant data; Volume 111
describes a simple predictive procedure for estimating runoff quant.
t
Y
and auality from highway systems; Volume IV is the research report
discuss no re Srch approach and findings; Volume V conta.ns the com-
parers manual forP
a highway runoff data borage program and
Solume VI is an executive summary. The report will be of mterest to
planners designers and researchers involved in evaluation of h ghway
stormwater runoff contributions to nonpoint sources of water pollut.on.
Research in Water Quality Changed due to Highway Operations is included
in the Federally Coordinated Program of Highway Research and Develop-
I !/t«L * of Proiect 3E, "Reduction of Environmental Hazards to
Water ResourceslueTthe^ ghway System". Mr. Byron N. Lord is the
Project and Task Manager.
Sufficient copies of the report are being distributed to provide a
minimum of one copy to each FHWA Regional office. Division o ice and
State highway agency. Direct distribut.on .s being made to the
Di vi sion offices
.
-}oV Charles F. Scheffey
Director, Office of Research
Federal Highway Administration
NOTICE
This document is disseminated under the sponsorship. of the Department
of Transportation in the interest of information exchange. The
United States Government assumes no liability for its contents or
use thereof. The contents of this report reflect the views of the
contractor, who is responsible for the accuracy of the data pre-
sented herein. The contents do not necessarily reflect the official
views or policy of the Department of Transportation. This report
does not constitute a standard, specification, or regulation.
The United States Government does not endorse products or manufacturers,
Trade or manufacturers' names appear herein only because they are
considered essential to the object of this document.
Technical Report Documentation Page
1. Report No.
FHWA/RD-81/044
2. Government Accession No. 3. Recipient's Catolog No.
4. Title and Subtitle
Constituents of Highway Runoff
Volume III, Predictive Procedure for Determining
Pollution Characteristics in Highway
Runoff
5. Report Dote
February 19816. Performing Organization Code
7. Author's) Kobriger> N . p., Meinholz, T. L.,Gupta, M. K. , and Agnew, R. W.
8. Performing Organization Report No.
9. Performing Organization Name and Address
Envirex Inc., (A Rexnord Company)
Environmental Sciences Division
5103 West Beloit Road
Milwaukee, Wisconsin 53214
DEPAB1TRANSPORT
10. Work Unit No. (TRAIS)
~f-CPs3|3E3-01
4
1 1 .-1Contxact or Grant No.
dOT^Ffi-1 1-8600
12. Sponsoring Agency Name and Address
Federal Highway Administration
Office of Research and Development
U.S. Department of Transportation
Washington. D.C. 20590
jnuJ3. Typejjof Report and Period Covered
^Inal Report - Volume III
February 1975 - July 1978
LIBRAR14. Sponsoring Agency Code
15. Supplementary Notes
FHWA Contract Manager: Byron N. Lord.(HRS-42)
16. Abstract
This report documents the development and application of a simple predictive
procedure (model) for determining the runoff quantity and quality from highway
systems based on the data gathered from 5 monitoring sites around the country.
Equations for 3 highway site types were developed to predict runoff volume and
pollutant wash-off coefficients for 17 water quality parameters based on total
rain, rainfall duration and dry days. Analysis indicated that pollutant accumu-
lation rates within highway systems can be best predicted using average daily
traffic values.
The titles of the volumes of this report are:
FHWA-RD-
81/042
81/04381/044
81/045
81/046
81/047
SubtitleVol. I
Vol. II
Vol. Ml
Vol. IV
Vol. V
Vol. VI
State-of-the-Art ReportProcedural Manual for Monitoring of Highway Runoff
Predictive Procedure for Determining Pollutant
Characteristics in Highway Runoff
Characteristics of Runoff from Operating Highways,
Research ReportHighway Runoff Data Storage Program and Computer
User's ManualExecutive Summary
17. Key Words
Highway runoffWater pol lution
Envi ronment
SaltingUrban storm water
Water qual i tyRunoffWater qual i tyModel ing
18. Distribution Statement
This document is available to the
public through the National Technical
Information Service, Springfield,
Virginia 22161
19. Security Classif. (of this report)
Unclassified
20. Security Classif. (of this page)
Unclassified
21. No. of Poges
205
22. Price
~orm DOT F 1700.7 (8-72) Reproduction of completed page authorized
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Table of Contents
Page number
Documentation Page i
Metric Conversion Factors ii
Tables of Contents ii"1
Tables iv
Figures vil
SECTIONS
I Introduction 1
II Predictive Procedure Development 3
III Input Requirements and Output Format 50
IV Predictive Procedure Results 61
V Receiving Water Impact 88
VI Conclusions and Recommendations 93
VII References 95
VIM Glossary 97
Append? es 99A. Rainfall - Runoff Data 99B. Washoff Variable (K
2 ) Sensitivity 115
C. Model Listing 119
D. Model Input Format 13*t
E. Model Variables and Description 136
F. Receiving Water Impact Sampling 139
G. Model Results 1^6
H. Flow, Total Rain and Dry Days Scallergram For 193
Type II Si tes
111
TABLES
Number Page
1 Site characteristics, rainfall - runoff summary. k
2 Predicted Q/R ratios for various rainfall volumes. 7
3 Dry day calculations for DD and DDI using a
hypothetical rainfall record. 8
k Results of regression analyses using DD andDDI values for Type I! sites. 10
5 Correlation analysis results for rainfall -
runoff durations. 12
6 Rainfall-runoff duration equations. Ik
7 General equations relating rainfall and runoffdurations. 13
8 Example of rainfall record and monitoring dataneeded to develop Kj using Method I. 19
9 Total solids per runoff rate by rainfall cate-gory at the Milwaukee, I-792
* site, 1976. 21
10 Total solids washoff using method 2 at theMilwaukee, I -794 site, 1976, 22
11 K^ values for monitoring sites. 2k
12 Predictive procedure K, values. 25
13 Published K1values. 26
"ik Comparison of total solids accumulation rates
to site characteristics. 27
15 Regression analysis results for K-| (dependentvariable), and ADT and dustfall for (independentvariable). 27
16 K2 sensitivity at Milwaukee l-79*». 31
17 Range of K2 values for each monitoring site. 31
TV
TABLES (continued)
Number Page
18 K2 values used in predictive procedure. 40
19 Constituent equations developed for Type I andType I I I sites. 41
20 Constituent equations developed for Type II sites. 42
21 Predicted constituent loads using site specificequations and average equations. 44
22 Constituent equations developed for Type II sites. 45
23 Milwaukee 1-794 calculated suspended solids andlead loadings using Type I site equations. 47
24 Milwaukee Hwy. 45 calcualted suspended solidsand lead loadings using Type II site equations. 48
25 Nashville 1-40 calculated suspended solids andlead loadings using Type II site equations. 49
26 Nonwinter rainfall summaries for a hypotheticalarea. 54
27 Model results for Milwaukee 1-794, 1976 and 1977. 62
28 Model results for Milwaukee Hwy. 45, 1976 and 1977- 28
29 Model results for Nashville I-40, 1977. 73
30 Model results for Harrisburg I -81, 1976 and 1977- 77
31 Model results for Denver I -25, 1976 and 1977. 80
32 Model results for Texas 1-45, 1977. 84
33 Summary of highway runoff quality data for all
six monitoring sites - nonwinter 1976-77. ^9
34 Yearly highway runoff loads. 88
35 Average concentrations for combined sewer, stormsewer and highway discharges. 91
TABLES (continued)
Number Page
36 Annual loads for highway, municipal and urban 92runoff sources - lb/ac-yr.
FIGURES
Number Page
Estimation of pollutant level after build-up. 18
Surface total solids level, Milwaukee 1-794. 20
Selected total solids accumulation rates versusaverage daily traffic. 29
Milwaukee 1-794 K2
sensitivity, 1976. 32
Milwaukee 1-794 K2
selection, 1976. 33
Milwaukee 1-794 K£
selection, 1977- 34
Milwaukee Hwy. 45 K2
selection, 1977- 35
Milwaukee Hwy. 45 selection, 1977. 36
Harrisburg K2
selection, 1976. 37
Harrisburg K2 selection, 1977. 38
Nashville K2
selection, 1977. 39
Typical paved area cross-sections. 51
Predictive procedure flow diagram. 56
Measured versus computed total solids at
Milwaukee I -794, 1976 and 1977- 65
Measured versus computed lead at Milwaukee1-794, 1976 and 1977- 66
Measured versus computed total solids at
Milwaukee Hwy. 45, 1976 and 1977. 70
Measured versus computed lead at MilwaukeeHwy. 45, 1976 and 1977- 71
Measured versus computed total solids at
Nashville 1-40, 1977. 75
Measured versus computed lead at Nashville1-40, 1977. 76
VI i
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
FIGURES (continued)
Number Page
20 Measured versus computed total solids atHarrisburg 1-81, 1976 and 1977. 78
21 Measured versus computed lead at Harrisburg1-81, 1976 and 1977- 79
22 Measured versus computed total solids at
Denver 1-25, 1976 and 1977. 81
23 Measured versus computed lead at Denver I -25,1976 and 1977. 32
2k Measured versus computed total solids at
Dallas I -45, 1977. 85
25 Measured versus computed lead at Dallas I -45,
1977. 86
VI 11
ACKNOWLEDGEMENTS
Acknowledgement is given to numerous State highway and U.S. GeologicalSurvey representatives as well as individual experts who provided assistanceduring the progress of the study and preparation of the final report. Itis not possible to name all who participated in this research study andtherefore only a few key agencies and persons are listed below.
The following State highway agencies provided technical assistance, sitesfor field investigations, traffic and maintenance data:
Colorado State Department of HighwaysLouisiana Department of Transportation and DevelopmentPennsylvania Department of TransportationTennessee Department of TransportationWisconsin Department of Transportation
District offices of the U.S. Geological Survey in Colorado, Pennsylvania,Tennessee and Wisconsin provided technical assistance in equipmentinstallation and site operation.
Review and assistance in the preparation of the final reports was providedby: Richard Howell, California Department of Transportation andRoderick Moe, Texas State Department of Highways and Public Transportation.
In addition to those agencies and individuals acknowledged above, sincereappreciation is expressed for the work of Frank K. Stovicek, (deceased)
,
FHWA, Office of Research who thoroughly reviewed the final report.
ix
SECTION I
INTRODUCTION
The following report describes the development of a predictiveprocedure for determining the quantity and quality of storm generatedrunoff from highway areas. The development of the procedure is basedupon the monitoring program carried out in 1976 and 1977 at sitesin Milwaukee, Wisconsin; Harrisburg, Pennsylvania; Nashville, Tennessee;and Denver, Colorado. Complete details of the monitoring programs atthese sites can be found in Volume IV of this document series (1).
Data summaries and monitoring results related to the development of thepredictive procedure are presented in the Appendices of this report.(See Table of Contents).
PURPOSE
The purpose of the predictive procedure is to provide highway designersand other interested individuals or agencies with a simplified tool to
estimate the quantity and quality of storm generated highway runoff.The predictive procedure is comprised of a series of equations whichcan be computerized to form a math model. The procedure is made up
of four components corresponding to the following functions:
Rainfal 1- runoff
Pol 1 utant bui ldup
Pollutant wash-offConstituent loadings
The predictive procedure can be used for Environmental Impact Statements(EIS) or to determine the loadings for analysis of the pollutantdischarge effects of various design storms at a particular site. Thepredictive procedure is not meant to be an all-inclusive model whichpredicts hydrographs and concentrations versus time but rather an easyto use method of determining the total volume of runoff and totalpounds of a pollutant discharged from a highway area. The highwayarea under consideration is defined as the paved roadway and total
nonpaved area which contributes to a particular discharge point. Thestandard data available to highway designers such as average dailytraffic, site length, percent paved and curb type are used to defineeach area of investigation.
In addition to EIS applications, the procedure can also be used to
evaluate existing highway systems. Thus, the effects of increased
traffic, changes in the curb or barrier configuration or modifications
to the nonpaved area can be determined as they relate to the total
pollutant loading. Design storm analyses can also be conducted on an
existing highway drainage area using the predictive procedure. The
pollutant loadings, as predicted for these storms, can then be used to
evaluate different water quality impacts through mass balance ormodeling analyses. In this manner, the pounds of a pollutant dischargedfrom a highway drainage area can be compared to other pollutant sources
1
within the area. Model use for evaluating receiving water impacts
is discussed in Section V of this report.
FORM OF THE PREDICTIVE PROCEDURE
The predictive procedure presented in the following portions of this
report will be available to the user in two separate forms. Becauseof the requirement of being easy to use, the first form of the
procedure is presented as a series of equations and tables with a
detailed step-by-step list of instructions for implementation. Theseequations require the user to select a number of coefficientscorresponding to particular site characteristics and to select a
rainfall period for analysis. Directions on the selection ofcoefficients and examples of the various applications are included.
The second form of the predictive procedure is a computerized versionfor use in long term applications which require the analysis ofextended rainfall periods. Here the procedure takes on the form ofa simulation model which performs the calculations based upon the usersupplying the appropriate input data. Again, suitable documentationis included with the model. Further descriptions of the input require-
ments for each form of the model are presented in Section III. A
listing of the computer program statements is contained in the Appendi-
ces of this report.
SECTION II
PREDICTIVE PROCEDURE DEVELOPMENT
The development of the functional relationships between the individualsets of data from the monitoring program comprise the framework forthe predictive procedure. The modules of the procedure represent thebuild-up of pollutants on the right-of-way surfaces, the wash-off ofthese pollutants during runoff and the determination of the dischargeloads for selected pollutant constituents. Although the pollutantbuild-up process occurs prior to the rainfall-runoff process, thelatter is discussed first in order to evaluate the length of time in
which the pollutant accumulation actually occurs and to determine theamount of material remaining on the right-of-way after the previousrainfall event. The period of buildup and washoff from previousstorms are collectively referred to as the prestorm history throughoutthis report.
RAINFALL-RUNOFF RELATIONSHIPS
The volume and duration of runoff from highway right-of-way are cal-culated within this module of the predictive procedure. The averagerunoff rate (volume of runoff per hour) is then used to remove(wash-off) a portion of the pollutants that have accumulated duringthe prestorm history. The prediction of the average runoff rate is
based upon the characteristics of the highway drainage area and therainfall volume and duration of each event under analysis.
The rainfall volume (R) for each storm and the associated runoffvolume (Q_) when expressed in the same units, provide a means of
calculating the runoff coefficient which can be written as;
runoff coefficient = volume of runoff = Q/R ratiovolume of rain
The runoff coefficient or Q/R ratio as used in this report, relates
the total volume of runoff from both the paved and nonpaved portionsof the drainage area to the amount of rain causing the runoff. Ap-pendix Tables A-1 - A-5 on pages 99- 1 1 A present the rainfall andrunoff data from the monitoring sites. Table 1 presents a summary of
the monitoring site characteristics, rainfall data, and runoff data.
These data used to develop the predictive procedure are only for
the nonwinter months, April through October. Runoff during the win-ter months, November through March, is difficult to characterize,especially in the northern climates where snowfall and frozen groundconditions are prevalent. Runoff during the winter months in theseareas is a function of snowfall, snowpack and temperature.
As can be seen from Table 1, some of the Q/R ratios are greater than
1.0, which indicates that the amount of runoff from these sites is
greater than the rainfall that falls on the drainage area. Thisphenomenon is, of course, impossible unless an external source offlow is contributing to the monitoring site. The logical explanationfor these large values is likely due to the accuracy of the instru-ments used for measuring the rainfall and flow volumes. A 10 to 15
percent error is enough to affect each OVR value at the all-pavedMilwaukee 1-794 site so that the ratios are greater than 1.0. Becausethe percent error can be positive or negative for each parameter, theQ/R values which are greater than 1.0 will remain in the analysessince the effects of these values will be negligible when averagedwith the remaining data.
Table 1. Site characteristics, rainfall - runoff s umma ry
,
3 3Drainage area, Percent Average Range
Site ^acres paved Q/R Q/R
100 0.87 0.20-1.21
31 0.40 0.10-0.91
27 0.49 0.02-1.09
37 0.48 0.10-0.90
37 0.42 0.08-0.68
a. April through Octobermetric units: acres x 0,405 = hg
in. x 2.54 = cm
The site characteristic referred to as percent paved in Table 1 is
a measure of that part of the drainage area which is normally concreteor asphalt. The driving lanes and most shoulder areas, as well as
exit and entrance ramps that are part of the total drainage area areconsidered paved. Since the predictive procedure is meant to beapplied to all highway areas which may or may not have characteristicsthe same as those listed in Table 1, a general site characteristicsclassification was developed.
Mi lwaukee1-794 2.1
Mi lwaukeeHwy . 45 106.0
Harrisburg1-81 18.5
Nashvi 1 le
1-40 55.6
Denver1-25 35.3
The Milwaukee 1-79** site is unique in that the elevated bridge deckthat comprises this drainage area is completely paved with impactbarriers containing each set of lanes. This type of site is charac-teristic of many urban freeway systems which have a number of bridgedecks passing over interchanges or depressed areas.
The Milwaukee Hwy kS site, the Nashville \-k0 site, and the Denver 1-25
site characterize those highway drainage areas which contain pavedroadway and shoulder areas and grassy or nonpervious side areas. Thesesites also contain mountable curbs with inlets spaced along the road-way surface. The percent of the drainage area that is paved generallyranges from 30 to kO percent.
The final type of drainage area that can be encountered is the rural,flush shoulder type of highway which is characterized by the Harris-burg l-8l site. This site has flush shoulders which receive the runofffrom the paved traffic lanes and which conveys the runoff to inletsspaced along the grassy side ditches of this rural site. Generallythis type of site has a smaller part of the drainage area that is pavedranging from 20 to 30 percent.
These general site characteristics will be classified as follows for
all remaining predictive procedure applications:
Type 1 sites: Urban, elevated bridge deck, 100 percent pavedwith impact barriers containing each set of
lanes; Milwaukee 1-79**.
Type II sites: Mountable curb with paved and nonpaveddrainaqe area; Milwaukee Hwy. k5, Nashville1-40, Denver 1-25.
Type III sites: Rural sites with flush shoulders, paved andnon-paved runoff through ditches; Harrisburq1-81.
The predictive procedure calculates the volume of runoff for a
given rainfall event using equations which were developed for eachsite type from the monitorina data at the five drainage areas listed
in Table 1. Using this table, the 1-79** site had an average Q/R ratioof 0.87 which means that for each inch (2.5** cm) of rain that fell on
that site, an average of 0.87 inches (2.2 cm) of runoff were measuredat the outlet of the drainage area. Similarly for the Nashville 1-40
site, 0.48 inches (1.22 cm) would be discharged. The differencesin these values can be accounted for by the site characteristics of
each drainage area with the \-k0 site being 37 percent paved and 1-79**
being 100 percent paved.
The range of Q/R values presented in Table 1 varies from 0.02 to 1.2.
The variation in these ratios between sites is caused by the differ-
ences in rainfall intensities and durations and most s iqni f icantly
the differences in the characteristics of the drainage areas such as
percent paved and soil cover within the unpaved area. The variationsin the ratio at a particular site are related to four possiblesources:
1. Rainfall intensity and duration.2. Prestorm history.
3. Monitoring instrumentation.h. Variation in vehicular splash-off.
Two rainfalls at a particular site may have the same volume but occurover s iani f icantly longer durations which effects the amount of
runoff. For example, one inch (2.5^ cm) in 30 minutes should producemore runoff than one inch (2.5^ cm) in 5 hours due to the latterstorm having more opportunity to infiltrate the nonpervious portionsof the drainaae area.
The prestorm history relates the conditions of the drainage area priorto the storm under consideration. Thus, the 5 hour storm abovewould Droduce more runoff if it had been preceeded by a large amountof rain a day earlier than if there were no rain for an extendedperiod prior to the storm. The prestorm history is thus related to
the soil moisture conditions of the nonnaved portions of the drainaaearea and their ability to provide a base for infiltration of a part
of the potential runoff volume.
The prestorm history of the all-paved 1-79^ site has little effecton the runoff volumes. Although the surface storage or surfacewetting component can effect the volume of runoff as a function of theprestorm history, it will be considered constant for each event at
this site (Type 1) and not included in the analysis.
Another source of variation in the Q/R ratios could be attributableto the accuracv of the monitorinn instrumentation. Small rainfalland flow volumes can approach the detectable limits of the raingaugesand stage recorders used at the monitoring sites. This may causea variation of as much as 20 percent. These variations must be
regarded as implicit to the data collection effort and will be assumedto be relatively constant for the events monitored in this project.
A final source of variation in Q/R ratios may be due to variationsin vehicular splash-off. Splash-off varies with the velocity (speed)
and quantity of traffic. Generally, the traffic volume and velocityare highest during daylight hours and lowest at night. Thereforea storm occurring during daylight hours will probably produce less
runoff (more splash-off) than the same storm occurring at night. In
urban areas, traffic volume is greatest during the early morning andlate afternoon (commuter traffic) while traffic velocity is oftenlowest at the peak of the "rush-hour". A storm which occurs justprior to or immediately following peak "rush-hour" traffic wouldprobably produce less runoff (more splash-off) than the storm occurring
6
at other times of the day. The effect of splash-off on Q/R ratio
is probably greatest for Type I and Type II sites.
Determination of Runoff Volumes
Tvpe I Sites - Two approaches were initially investigated for pre-
dicting the volume of runoff from the Milwaukee 1-79^ site. The
first approach used an average Q/R ratio of 0.87 (Table 1 ) as a
coefficient which when multiplied times the rainfall volume produced
the volume of runoff. Thus, an equation of the following form was
produced:
Q = 0.87 R
where Q = runoff volume
R = rainfal 1 volume
This form of the equation is similar to the rational formula approach
which predicts that a constant fraction of the rainfall is converted
into runoff for all rainfall events. The data from thissite showed
the volume of runoff to vary from storm to storm as previously
mentioned. In order to account for this variation, the runoff volumes
were correlated to the rainfall volumes using simple regression
techniques. The resulting equation, significant at the 95 percent
confidence limits, was:
Q= 0.969 R " 0.019 0)where Q_ = runoff volume (inches)
R = rainfall volume (inches)
The correlation coefficient for this equation was 0.95 with an R 2 value
of 0.91 which indicates that 91 percent of the variation in thetotal
runoff at this site can be attributed to total rain using equation 1.
Other forms of the equation using nonlinear techniques produced less
significant relationships. Equation 1 produced runoff volumes and
Q/R ratios which vary with the volume of rainfall as listed in Table 2.
This form of the equation is used in the predictive procedure for all
Type 1 sites.
Table 2. Predicted Q/R ratios for various rainfall volumes.
Rainfall - R
(inches) 0.1 0.2 0.5 1.0 2.0
Predicted Runoff-Q(inches) 0.078 0.175 0.466 0.950 1.92
Q/R 0.78 0.78 0.93 0.95 0.96
metric units: in. x 2.5^ = cm
7
Type II Si tes - Type II sites are characterized as those sites with both
paved and non paved (pervious) areas. Because of these pervious areas,dry days are an essential parameter in evaluating the relationshipbetween rainfall and runoff volumes. Long dry periods prior to a rain-
fall will allow the non paved portions of the drainage area to absorbmore of the rainfall than conditions where rainfall has been recent
or significant enough to saturate those areas. Thus, long dry periodsgenerally reduce runoff volume and lower the runoff coefficient (Q/R
ratio)
.
Two methods of quantifying the prestorm history were attempted in thedevelopment of the predictive procedure. The first method was to
count the number of days of dry weather prior to the event underconsideration. This "dry days" (DD) value is a measure of the lengthof time prior to the event when no rain fell on the drainage area. Thesecond method measures the number of days prior to the rainfall eventin which the cumulative amount of rainfall equaled one inch (DDI).
DDI takes into consideration the amount of rain that fell in the pre-storm period as well as the amount of time that was necessary to
accumulate one inch (2.5^ cm) of rain. Other volumes of rainfall wereconsidered but the one inch value accounted for more of the large rain-storms than smaller values, while larger cumulative values resulted in
extremely large DDI values for arid sites such as Denver. The two
methods of quantifying the prestorm period may be significantly differentin their estimates.
Table 3 presents a hypothetical rainfall record to illustrate the
difference in dry day estimates using the DD and DDI methods. If the
storm prior to the storm under consideration is greater than or equal
to one inch (2.5^ cm), DD and DDI have the same value (storm on April 3).
However, if rainfall events less than one inch (2.5*t cm) occur in the
rainfall record, the two values become significantly different (storms
on April 5,7,9 and 11). The cumulative value DDI provides an estimateof the length of time that was required to accumulate one inch ofrainfall prior to the event under analysis.
Table 3- Dry day calculations for DD andDDI using a hypothetical rainfall record.
Storm Rainfal 1
,
DD, DDI,
date in. days days
April 1 1.05 — —April 3 0.30 2 2
April 5 0.45 2 k
April 7 0.20 2 6
April 9 0.50 2 8
April 11 0.10 2 6
metric units: in. x 2.5^ = cm
8
Regression analyses were used to evaluate both methods of charac-terizing prestorm history (DD and DDl) for each of the three Type II
sites. The results of the regression analyses using both linear andlog transformed data are presented in Table k. All correlationcoefficients (R values) for the resulting equations are significantat the 95 percent confidence level when compared to a table of criticalvalues for correlation coefficients (2). The regression analyses alsoshow that equations using DD or DDl variables for individual sitesare not significantly different in explaining the data variance forrunoff volume (R2 values differ by less than 0.0*0. This indicatesthat equations using the DD variable will predict runoff volume aswell as equations utilizing the DDl variable. Because regressionanalyses indicate no advantage in using the DD or DDl variable, theDD variable was used for the predictive procedure since it is the commonway of relating prestorm conditions.
The log form of the equation using the DD variable was used for Type II
sites, because this form of the equation explains slightly more of thedata variation (R2 values) than does the linear form (Table k) . Also,the regression coefficient of the DD variable in the linear equationfor Milwaukee Hwy. *t5 site is a small positive value (0.001). Thisequation will predict larger runoff volumes as the number of dry daysincreases, which is counter to what is commonly expected in hydrology.
The form of the predictive equation to be used for Type II sites is
a combination of the Milwaukee Hwy. k$ and Denver sites as determinedthrough a multiple regression analysis. The Nashville data were notincluded in this task because the data from that site tended to skewthe resulting equation away from the high significance levels that
were obtained in the individual correlations. The Nashville data
could be highly correlated as is shown in Table k. However, when
these data were combined with data from the other two sites in one
regression analysis, the Nashville data were different than the other
sites and the resulting equation was unacceptable. Appendix H (page
193) presents a series of scatter diagrams (Figure H-l, H-2, H-3, W-k)
plotting flow versus total rainfall and dry days to show how the
Nashville data differs from the Milwaukee Hwy. k$ and Denver sites
and how the Nashville data tends to cluster. The reason for the
variance in the Nashville data can be explained by two factors. The
storm history for this site occurs such that a dry period of five to
ten days is followed by four or five rainfall events in a short period.
This same pattern is repeated two or three times as can be seen in the
monitoring data of Appendix Table A-3 on pages 106 to 108. Another
factor is the problem of rainfall measurement which was evident in
the early portions of the monitoring program. Because of the length
of the drainage area and since there was only one raingauge at this
site in the initial phase, there were instances where flow was
measured although rainfall was not recorded on the gauge. This could
Table 4. Results of regression analyses3 usingDD and DD1 values for Type II sites.
Mi lwaukee Hwy 45
DD
Denver 1-25
linear 0. = 0.746 R + 0.001 DD - 0.199 0.88 O.78log = 0.366 RK *3 DD - 06 0.9* 0.89
DDI
Ulog = 0.413 R 1 *^ DDI -0.007 0.94 0.89linear 0_ = 0. 768 R- 0.005 DDI : _0. 099 0.91 0.82
DDlinear Q = 0.509 R - 0.002 DD - 0.026 0.91 O.83log = 0.565 R 1 ' 34 DD -0.175 .91 0.83
DDI
lii
log = 0.713 R'* z:?H DDI -0.183 .89 0.80linear Q - 0.482 R - 0.002 DDI +0.033 0.92 0.84
= 0.713 R K25A DDI -0-183 0.89
Nashville 1-40
DD
linear Q « 0.557 R - 0.01 DD + 0.022 0.92 O.85log = 0.522 rI-028 dd -0.107 0.87 0.75
DDI'
linear Q = 0.555 R - Q.003 DDI + 0.019 0.91 O.83log - 0.592 R 1 * 02 DDI -0.109 0.87 0.76
a Al 1 equations are significant at the 95 percent confidence limits
Note : Q_ = runoff (inches)
R - rainfall volume (inches)
DD = dry days to last rain
DDI = cumulative days to one inch of rain
metric units: in. x 2.54 = cm
10
account for the variability in the data. Further details of themonitoring aspects of this site can be found in Volume IV of thisdocument series (1).
Using rainfall volume (R) and dry day (DD) data for Milwaukee Hwy. hSand Denver, the Type II site equation to predict runoff volume (Oj
was developed:
The Type II site equation is further explained in Section I I I of thisreport and is significant at the 95 percent confidence limits (2).
Tyep I I I Sites - The Harrisburg 1-81 site was used to characterizeType III sites. Data were investigated in the same manner as Type II
sites with the results of the DD and DDI variables being the same asType II and the log equation showing more significance when comparedto the linear equation. Using rainfall volume (R) and dry day (DD)
data for Harrisburg, the Type III site equation to predict runoffvolume (Q) was developed using regression analysis:
»- 0,8* .'••*..-•» £3 ^The Type III site equation is further explained in Section I I I of thisreport and is significant at the 95 percent confidence limits (2).
Runoff Duration
Once the total volume of runoff has been calculated using site specificequations, the duration of this runoff must be predicted to determinethe average runoff rate. A total of one inch (2.5^ cm) of runoff mayoccur at a particular site over a duration of one hour to possiblysix hours. The average intensity of one inch (2.5^ cm) per hour
should wash off more pollutant than a one-sixth of an inch (0.^2 cm)
per hour event, assuming uniform rainfall intensity. The predictiveprocedure developed in this report requires an average runoff rate
to remove pollutants from the drainage area. Ideally, discrete rates
at individual points within the runoff event should be used to remove
the pollutants, however, this methodology is too sophisticated for
the purposes of this procedure.
Data for the duration of runoff at each monitoring site are presentedin Appendix A, Tables A-l to A-5. These data were used to developan equation relating rainfall duration (RD) to runoff (flow) duration
11
(FD) . Each site's data were analyzed using linear regressionanalysis to determine an equation relating the durations. Table 5presents the resulting equations for each of the five sites, a com-
bination of the Type II sites, (Milwaukee Hwy. 45, Nashville andDenver) and all sites combined.
The equations of Table 5 are all significant at the 95 percent con-fidence level (2) and give a good indication of the typical durationof runoff for a given rainfall event. The last column of the tablepresents the predicted runoff duration for a two hour rainfall. Thesedata show that the shortest runoff duration is predicted for theMilwaukee 1-794 site (Type I site), while the longest runoff durationis predicted for the Harrisburg site (Type III site). Runoff dura-tions predicted for Milwaukee Hwy 45, Nashville and Denver (Type II
sites) are very similar and fall between the predicted values forMilwaukee 1-794 and Harrisburg. This would be expected consideringthe drainage (hydraulic) characteristics for these three site
types.
Table 5. Correlation analysis results for rainfall-runoff durations.
Correlation RunoffRunoff duration coefficient duration (FD)
Si te equation R for RD = 2 hrs
Milwaukee 1-794 FD = 1.12 RD + 0.69 0.95 2.93Mi lwaukee Hwy 45 FD = 1.50 RD + 1.31 0.91 4.31
Nashville 1-40 FD = 1.33 RD + 1.60 0.89 4.26
Denver 1-25 FD = 1.16 RD + 1.72 0.90 4.04Harrisburg 1
— 3
1
FD = 1.65 RD + 6.27 0.75 8.57Hwy 45, 1-40, FD = 1.36 RD + 1.63 0.90 4.35
1-25 combined
All sites combined FD = 1.62 RD + 2.10 0.77 5.34
Note: RD = rainfall duration in hoursFD = runoff duration in hours
The determination of the average runoff rate is critical to the
accuracy of the washoff equation of the predictive procedure, there-fore further investigation of the effects of prestorm history was
undertaken. The data of the Type II and III sites was analyzed to
determine if longer runoff durations were associated with fewer dry
days. This relation was thought to occur since the pervious (non-
paved) areas would contribute more to the total runoff volume as the
number of dry days decreased. Since these areas have significantlylonger time of concentrations than paved portions of the drainage area,
possibly runoff duration would increase for equal duration rainfalls.
12
The analyses were run for two separate groups of data correspondingto a certain number of dry days thought to show a significantdifference in rainfall/runoff durations. The first dry day estimatesinvestigated were those events with dry day periods equal or greaterthan 5 days and those less than 5 days. Regression analyses wererun on each set of data for each site to determine if there were largedifferences in the ratios of the flow duration-to-runoff duration.The regression equations that resulted showed very little differencebetween each of the groups of data. The next analysis was to use thedry day period of 10 days to determine if any differences resulted.Regression analyses for these data showed greater differences than the5 day estimate and the resulting equations are presented in Table 6.All equations are significant at the 95 percent limits (2).
Table 6 presents the calculated runoff duration for a two hour rain-fall. The Harrisburg site shows the longest runoff duration forthis storm. The difference in the duration for DD greater than 10
and less than 10 is more than 3 hours. The Nashville site shows the
smallest difference between the DD estimates, but this characteristicmay be a function of the raingauge problems discussed earlier. Theseequations will be used to categorize the runoff duration for the sitetypes which contain nonpaved portions of the drainage area.
Table 7 shows the results of combining the three Type II sites whilethe Type I and III sites use the individual equations for the Milwaukeel~79*» and Harrisburg sites, respectively. The equations for the twodry day groups in Table 7 are identical for Type I sites because a
completely paved drainage area was assumed to be unaffected by pre-storm history.
Table "J. General equations relating rainfalland runoff durations.
Dry days greaterthan or equal to Dry days less than
Type of site 10 days 10 days
Type I FD = 1.12 RD + 0.69 FD = 1 . 12 RD + 0.69Type II FD = 1.06 RD + 1.79 FD = 1 .27 RD + 2. 16
Type Ml FD » 1.92 RD + 4. 18 FD = 1.48 RD + 8.28
Note: FD = runoff durationRD = rainfall duration
The duration of runoff predicted with these equations is used withthe previously calculated runoff volume to produce the average
13
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volume of runoff per hour for a given rainfall event.
POLLUTANT ACCUMULATION EQUATION
The pollutant accumulation equation of the predictive model is asfo 1 1 ows
:
P = P + (K, x HL x T) (2)o I
Where; P = pollutant level after build-up, lb (kg)
P = initial surface pollutant load, lb (kg)
K, = pollutant accumulation rate, lb/mi-day(kg/km-day)
T =* time of accumulation, days (20 day maximum)HL = highway length, mi (km)
The pollutant accumulation equation predicts the highway surfacepollutant load (P) which accumulates during a selected time period(T) at a specified rate (K.). Surface pollutants are those whichaccumulate on the surface of all-paved sites (e.g. elevated bridgesections) and on the paved and unpaved surfaces of highway sectionswhich have right-of-way areas (e.g. gravel shoulders, grassy areas,etc.). For purposes of the predictive model, pollutant load refersto that fraction of the total highway load which is available for
wash-off.
The monitoring data from this study were "end-of-the-pipe" wash-offdata which are not a direct measure of pollutant buildup. Therefore,monitoring data could not be used to establish whether pollutantbuildup on highways is linear, exponential or some other mathematicalfunction. The pollutant accumulation equation (2) assumes that
pollutants build up linearly during the accumulation period. Thisequation is similar to that used in the Storage, Treatment, Overflow,Runoff Model (STORM) (3) which is based on a linear pollutant build-up relationship developed from a study on urban runoff in Chicago (k) .
The surface pollutant accumulation variable, Kj , is the rate at whichthe carrier pollutant, total solids, accumulates in pounds per mileper day (kg/km/day). A carrier pollutant is that pollutant whichexhibits the highest degree of association with all other pollutantsbeing considered. In many mathematical models, the predicted quantityof carrier pollutant is used to estimate the quantity of all otherpollutants being modeled. This method eliminates the need for eachpollutant to be predicted separately by the model, greatly reducing
the number of calculations required. Total solids was chosen as the
carrier pollutant for the predictive model developed in this study
because total solids showed the highest correlation with the othermonitored quality parameters when regression analyses were performed.Volume IV, of this document series, "Characteristics of Runoff From
Operating Highways Research Report", presents the results of these
15
regression analyses (1). In this report the term total solids (TS)
refers to total suspended solids (TSS) plus total dissolved solids(TDS). Under the new nomenclature of Standard Methods (5), totalsolids as used in this study is defined as total residue.
Highway planners usually know in advance the length of the highway andthe design average daily traffic. For this reason, K^ , is expressedas pounds per mile - day (kg/km-day) . The unit, miles, refers tothe actual length of highway section in the contributing drainage area.This is in contrast to curb mile or lane mile, which is a multiple ofthe highway length. The highway designer then inputs to the modelthe highway length (HL) and the appropriate Ki factor. K] is sitespecific and its value may be based upon actual monitoring data, if it
exists, or its value can be estimated from average daily traffic. K,
selection based on average daily traffic is discussed at the end ofthis section.
The initial surface pollutant load (P ) is the pounds of pollutantson the surface at the beginning of the accumulation period. Theaccumulation period can be intervals within an actual rainfall record,intervals within a series of design storms, or the period prior to a
single design storm event.
If an accumulation period follows a large storm event, the initial
surface pollutant load will be considered zero (Po=0). The definitionof a large storm is based upon that used in the Stormwater ManagementModel (SWMM) for the rate of pollutant removal during a storm event
(6). For purposes of the predictive procedure, a large storm event is
defined as one with greater than one inch (2.5*1 cm) of total rainfalland having at least one hour in which the average intensity is 0.5inches per hour (1.27 cm/hr) . For such a storm, it is assumed in SWMMthat approximately 90 percent of the surface pollutants available forwash-off have been removed. For the remainder of this report, any
storm meeting the above rainfall requirements is referred as to a largestorm event.
For succeeding rainfall events in a continuous rainfall record, PQ is
the surface pollutant load remaining after wash-off from the previousrainfall event. For rainfall records which do not follow a large
storm event or for design storms, the initial surface pollutant load
must be estimated using a site specific Kj value, site length and a
specified period of build-up. An analysis of monitored wash-off data
and rainfall records during the subject study suggests that dry periodsgreater than 20 days over-estimate the surface load. The initial
surface pollutant load for a site with a K, of 80, and a site length of
5 mi les would be:
80 .
] b/ x 5 mi. x 20 days = 8,000 lbs (3,629 kg)mi -day
16
However, the model user may choose any accumulation period based upon
site specific data or design criteria. For any site, a maximum accumu-
lation period is governed by wind, traffic, curb type, sweeping and
other site specific factors which limit the pollutant load within a
highway system. This analysis will assume 20 days to be the maximum.
The flow diagram of Figure 1 depicts the steps leading to an estima-
tion of the pollutant level after build-up for any period of accumu-
lation. This diagram also shows the various methods of estimating
the initial surface pollutant load, PQ .
Surface Pollutant Accumulation Variable (K^) Development
K] was developed from monitored data using one of the following
methods depending upon available data records:
Method 1. Uses a period between two large storm events in
which all of the storm events were monitored.
Assuming the two large storm events wash off
essentially all of the surface pollutants, then
the wash-off data in pounds (kilograms) from
all the monitored events should account for all
pollutants accumulated during this period.
Method 2. Uses a period between two large storm events in
which only some of the storm events were monitored.The monitored wash-off data are used to estimatepollutant wash-off for unmonitored events.Estimated and actual wash-off data should providea reasonable value for total surface pollutantsaccumulated between the two large rainfall events.
Method I is preferred for estimating K, because it is based entirelyon monitored data. The required monitoring data and a sample calcula-tion of a K^ value using Method I for the Milwaukee 1-794 site arepresented in Table 8.
The storm event on 7/30/76, 1.59 inches (4.04 cm) of rainfall, waslarge enough to wash off essentially all surface pollutants. Thepounds of total solids accumulated during this period is the sum of
all total solids monitored, ending with the 1.05 inches (2.67 cm) ofrainfall on 8/28/76 which is assumed to wash off all remaining surfacepollutants. Kj is calculated by dividing pounds of total solidswashed off by highway length in miles (kilometers) and the number ofdays during the accumulation period. Figure 2 displays this accumula-tion, wash-off relationship. The second method for developing Kj wasused for sites where runoff quality for all storm events between two
large rainfalls was not monitored, however runoff volume and durationwas available. The first step was to express all pollutant wash-offdata for monitored events in terms of runoff intensity, lbs/(in./hr)(kg/(cm/hr) ) . Runoff rates were chosen as the units for data normal i-
17
INITIAL SURFACEPOLLUTANT LOAD,
b
pq
=LARGE STORM
EVENT
Pq
= K1
x 20
DAYS x HL
DESIGN STORMOR NO
LARGE STORMEVENT
Po = lb
v LEFTy
AFTER WASH-OFF
CONTINUOUSRAINFALL RECORD
POLLUTANT LEVELAFTER BUILD-UP
lb
KEY
PQ = INITIAL SURFACE POLLUTANT LOAD
P = POLLUTANT LEVEL AFTER BUILD-UPK-\ - POLLUTANT ACCUMULATION RATET = TIME OF ACCUMULATIONHL = HIGHWAY LENGTH
Figure 1. Estimation of pollutant level after build-up.
18
Table 8. Example of rainfall record and monitoring data neededto develop Kj using Method I.
Maximum intensityTotal du ring any one
rainfal Is, hour of rain fall, Total solidsDate inches in./hr monitored, lb
7/30/76 1.59 1.02 -_
8/5/76 0.05 — 14
8/13/76 0.64 — 948/25/76 0.14 — 298/28/76 1.05 0.74 135
272 lb
accumulated
Kj = 272 lb/(0.15 mi (highway length at l-794)/29 days) =
62.5 Ib/mi-day (17.6 kg/km-day)
.
metric units: in. x 2.54 = cmlb x 0.454 = kg
zation because rate of runoff is the control ing mechanism which carriespollutants within the drainage system. Monitored events with associ-ated pounds of pollutants per runoff rate, were then grouped into
three rainfall categories as listed in Table 9: 0.05 to 0.10 inches
(0.13 to 0.25 cm), 0.11 to 1.00 inches (0.28 to 2.54 cm) and greaterthan 1.00 inch (2.54 cm). Any rainfall less than 0.05 inches (0.13 cm)
is considered a trace of rain which will not produce runoff ofsufficient volume and rate to effect pollutant wash-off. An averagepounds per runoff rate from each of the three rainfall categorieswas then applied to the appropriate unmonitored rainfall event for
which runoff rate was calculated. The pounds of total solids,monitored and estimated, are then summed for the entire period. An
example of K^ development using Method 2 is presented in Table 3. As
in Method I, the assumption is used that the two large storm eventswash off essentially all of the surface pollutants. The summed poundsof total solids (monitored and estimated) should therefore account for
all pollutants accumulated during this period. Using the sum of pre-
dicted and monitored total solids wash off, (Table 10), the Kj factorcan be estimated as follows:
K] = 1400 lbs v (0.15 mi. (highway length at 1-794 x 153 days)= 62.7 lbs/mi -day (17-7 kg/km-day)
If the three rainfall categories are not used, pollutant wash-off pre-
dictions for small rainfalls, 0.05 to 0.10 inches (0.13 to 0.25 cm)
are high and pollutant wash-off predictions for large rainfalls,> 1 . 00 inch (2.54 cm), are low. The range of mean values, total solids
per runoff rate, for the three rainfall categories of Table 9 indicate
19
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22
why errors in estimating pollutant wash-off occur if only one averageis used.
The minimum runoff duration that can be used in calculating averagerunoff intensity is 0.5 hours. Short duration flows, less than 0.5hours, are often the result of small storms producing less than 0.1
inches (.25 cm) of flow. Using the high average runoff rates calcu-lated from such storms gives more weight to the pollutant wash-offability of a small storm than occurs. For this reason, all stormsused in the predictive procedure were considered to have a duration ofat least 0.5 hours, eliminating overestimates of total solids wash-offfor short duration storms. For example, a storm producing 0.05 inches(.12 cm) of runoff in 0.17 hours (10 minutes) would have an averagerunoff rate calculated using an adjusted runoff duration of 0.5 in./hras follows:
Average runoff rate = 0.05 inches of flow * 0.5 hours= 0.10 in.hr (0.25 cm/hr)
Otherwise the average rate calculated from the actual runoff duration(0.17 hr) would be 0.29 in./hr (0.73 cm/hr) and would cause a largeoverprediction of pollutant wash-off.
Comparison of predicted total solids wash-off to monitored total
solids wash-off of Table 10 shows that values comparable to estimatedtotal solids are obtained using the rainfall category breakdown andshort duration storm adjustment for wash-off of unmonitored events.
K^ factors developed from monitored data during the subject study are1 isted in Table 1 1
.
Method 1 (all events monitored) and Method 2 (all events not monitored),give comparable results at 1-79** and Hwy 45. Use of Method 2 appearsto give reasonable estimates of pollutant accumulation (K^) whenMethod 1 cannot be implemented. However, Method 2 does not providegood Kj estimates when dry periods of greater than 20 days are con-tained within the period of analysis. The factor, pounds of pollutantsper runoff rate, does not account for dry days. Using Method 2,
estimated total pounds of pollutant wash-off for an unmonitored eventwith a 0.5 in./hr (1.27 cm/hr) runoff rate and one dry day will be
the same as an unmonitored event with a 0.5 in./hr (1.27 cm/hr) runoff
intensity and forty dry days. Method 2 wi 1 1 under-predict where long
dry periods are involved.
Denver, having an arid climate, is an example of a site where Method 2
cannot be used with confidence. During the summer monitoring periodof 1976, only two large storms appeared in the rainfall record. Thisperiod was used to calculate K^ , 1 39 • 3 lb/mi-day (53 • ** kg/km-day)
.
Within this rainfall record was a 42 day dry period. K^ is probably
23
Table 11. Kj values for monitoring sites
Averagedai ly
Ki value, Ib/mi-day1 976 1977
All events Al 1 eventstraffic, All events not Al 1 events not
Site vehicles moni tored monitored monitored monitored
1-794 53,000 63-5 62.7 96.3 100.0Hwy 45 85,000 172.5 120.2 367.7 368.9Harrisburg1-81 24,000 * 270.2 * 62.8
Nashvi 1 le
1-40 88,000 J. 210.8 * 347.3Denver1-25 149,000
J.189.3
j-90.0
Kj = lb/mi -day = (0.28 kg/km-day)* - The rainfall record did not contain a period between two
large storms for which all events were monitored.
underestimated as this site has an average daily traffic almost threetimes that of 1-794 in Milwaukee. During 1977, there was only onelarge storm event in the Denver rainfall record. The calculated Kj
value of 90.0 lb/mi-day (25.4 kg/km-day), was estimated using thelargest available rainfall, 0.65 inches (1.65 cms) over a 13.5 hourduration, at the start of the analysis period. Because of the long
dry periods and lack of large storms in the rainfall record, the sitespecific Ki values for Denver were not acceptable for use in thepredictive procedure, rather, a general K^ for Type II sites was used.
Construction activity occurred near the monitoring sites at Hwy. 45(Milwaukee) and 1-40 (Nashville) in 1977. K^ values for this periodreflect this construction activity, and these values were not used
in the development of the predictive model. At Harrisburg, 1976 wasthe first year of highway use after construction and this probablyinfluenced the high K] value for this site. The Kj value appears to
reflect some residuals from highway construction, and therefore, was
not used in the development of the predictive model.
The 1976 K1value for Nashville, 210.8 lb/mi -day (59.4 kg/km-day), is
based upon the only available data for the year, the month of October.
Findings at Milwaukee 1-794 indicate little variation in accumulationrates during summer periods. At 1-794 the K] calculated using Method
for the period 6/18/76 - 7/30/76 was 64.1 Ib/mi-day (18.1 kg/km-day)
and the Ki for the period 7/31/76 - 8/28/76 was 62.5 lb/mi -day (17.6
kg/km-day). Therefore, the Nashville 1976 K-j value is probablyrepresentative of the entire summer period. The 1976 Kj value is used
24
at Nashville for predictive procedures because of the constructionactivity which occurred during 1977. Based upon average daily traffic(Table 11), the 1976 Kj value at Nashville appears reasonable.
The K] values used in predictive model for nonconstruction events are1 isted in Table 12.
Table 12. Predictive procedure Kj values.
_Site lb/mi -day Value obtained by:
Milwaukee I-794 75.6 Weighted average for Kj
values (Method 1), 1976-77Milwaukee Hwy 45 172.5 1976 K] value (Method 1)
Harrisburg 1-81 62.8 1977 Ki value (Method 2)
Nashville I-40 210.8 1976 Ki value (Method 2)
metric units: lb x 0.454 = kg
mi x 1 .609 = km
Wherever possible the Method I K] value is used to the exclusion of
Ki values estimated using unmonitored events (Method 2). It is felt
that the K^ values in Table 12 best represent the total solids accumu-lation within the monitored highway systems under normal maintenanceand operation.
K^ Values in the Literature
The total solids accumulation rates for streets and highways whichappeared in a literature review of water quality impacts of highway
operations and maintenance (7) are presented in Table 13«
When making comparisons of the above total solids accumulation data,
two things must be kept in mind: 1) city streets generally accumulatemore total solids because of increased sources from which total solids
may be obtained, and 2) sweeping or a combination of sweeping and
flushing usually accounts for more total solids than an analysis of
surface runoff. The K^ values developed in this report reflect total
solids available to be washed off in surface runoff.
Previously developed K^ factors (Table 12), which ranged from 62.8
lb/mi-day at the Harrisburg site to 210.8 lb/mi-day at the Nashville
site (17.7 to 59-4 kg/km-day) were derived from runoff analysis data.
These Kj factors appear comparable to the total solids accumulation
values reported in the literature (Table 13).
25
Table 13. Published K^ values
Total solidsaccumulation rate, Sol ids col lection
lb/mi -day Location techniques Ref.
1580 Chicago City Sts. Sweeping (4)
1180 11 U.S. City Sts. Combined sweepingand flushing
(8)
10.4 Seattle Highway Runoff analysis (9)
382 Washington, D.C. Vacuuming and (10)
Hwy. flushing69.4 Seattle Highway Runoff analysis (11)
26.6 Seattle Highway Runoff analysis (12)
metric units: lb x 0.W = kg
mi x 1.609 km
A total sol ids a
was calculated f
study in Dal las,
The Texas studysection at this
The total sol ids
75-6 lb/mi -day (
accumulation rat
53,000 vehiclesvehicles per day
ccumulation rate of 54.5 lb/mi-day (15.4 kg/km-day)rom runoff analysis data reported in a highway runoffTexas (13) using an average Q/R of 0.90 and Method 1.
site is characterized as Type 1. The elevated highwaysite drains slightly more than two acres (0.8 ha).
accumulation rate at the I -794 site (Milwaukee) was
21.3 kg/km-day) which appears comparable to the Texase if the differences in average daily traffic ofper day at Milwaukee are compared to the 30,000at Texas.
Kj Selection Based on Site Characteristics
A comparison of total solids accumulation values (K^)
characteristics for four of the monitoring sites is p
Table 14.
with sitepresented in
These accumulation rates represent those that are derived from "normal"highway operation and maintenance. As explained previously, Kj
factors for years which had construction activity on or near themonitoring site were not used. The rainfall record at the Denver siteprevented a reliable estimate of Kj , and therefore, those total solids
accumulation rates will not be used in the regression analyses to
develop equations relating Kj to site characteristics. The selectionof a Kj value for sites other than those listed above require some
type of relationship for the predictive procedure to calculate a site
specific Kj . Regression analyses using the data from the sites
monitored during this project were used to develop an equation to
predict Kj . The results of regression analysis using K-\ as the depen-dent variable, and average daily traffic (ADT) and dustfall as the
26
independent variables are presented in Table 15.
Table 14. Comparison of total solids accumulation rates andsite characteristics.
Total sol ids Average dai lyaccumulation, traffic, Dustfal
1
,
BarrierSite lb/mi -day vehicles/day g/mVday type
Harrisburg1-81 62.8 24,000 0.06 Flush
Mi lwaukeeI -79^ 75.6 53,000 0.43 Barrier
Mi lwaukee MountableHwy 45 172.5 85,000 0.21 curb
Nashvi 1 le Mountable1-40 210.8 88,000 0.31 curb
metric units: lb x 0.454 = kg
mi x 1.609 = km
Table 15. Regression analysis results for K, (dependent variable),and ADT and dustfal 1 (independent variables).
Variablerelationship
Independentvariable
Sign ofregressioncoefficient
Linear
Semi log
Log
ADT + 0.93 0.87Dustfal
1
+ 0.17 0.03
ADT, Dustfal
1
+ ,- 0.96 0.93
ADT + 0.87 0.75Dustfal
1
+ 0.41 0.17ADT, Dustfal
1
+ ,- 0.95 0.91
ADT + 0.90 0.81
Dustfal
1
+ 0.14 0.02
ADT, Dustfal
1
+ ," 0.97 0.95
Because of the small number of cases (n-4) , nothing can be said
statistically about the correlation of K, with average daily trafficand dustfal 1. The purpose of performing the regression analysis was
to provide the best equation with the available data. However, averagedaily traffic appears to show a strong relationship with K-j values
27
when these two parameters are graphed (Figure 3). The computed Kj
for Dallas (13) was included as an additional point for comparison.Based upon the graph, ADT within the range of 25,000 to 90,000 vehiclesper day appears to give reasonable estimates of Kj
.
Regression analyses using the monitored dustfall data obtained duringthis study, show dustfall to be a poor estimator of K^ (low R2 values).In fact, when analyzed in combination with ADT, the regression analysesshow a negative relationship to Kj (negative regression coefficient).This means that as the dustfall increases the accumulation of total
solids decreases. This relationship is contrary to the positiverelationship which would be expected between dustfall and K^ . Theseresults are probably due to the small number of sites (n=4) whichcould be used in the regression analyses. Therefore, no equation is
used in the predictive procedure to predict Kj from dustfall alone(because of low R2 values) or in combination with ADT (because ofnegative regression coefficients).
Barrier type is difficult to quantify, but probably has some effecton total solids accumulation. With a flush shoulder on a highway,there is nothing to restrict total solids from being blown off.
Mountable curbs provide some restriction while side barriers help toretain a greater proportion of the total solids than mountable curbsor flush shoulders. Because of the limited data, an evaluation of theeffect of curb type on Ki values estimated for the study sites cannotbe made.
For those cases in which a site specific K« value is not available,the predictive procedure uses ADT to calculate the appropriate totalsolids buildup rate using the following equation;
K, = (ADT* 89
) 0.007 (3)
WASH-OFF COEFFICIENT (f<2
) DEVELOPMENT
The coefficient K£ is used in the predictive procedure to remove a
portion of the carrier pollutant from the surface of the drainage area.
The general wash-off equation that is used in the Corps of EngineersSTORM model (3) and EPA Stormwater Management Model (H) is used in
this procedure in the following form:
PD
= P (1-e" K2r
) (k)
Where: P_ = pounds (kilograms) of pollutant dischargedP = surface load at start of the runoff event
(Equation 2)
K2 = wash-off coefficientr = average runoff rate, in./hr (cm/hr)
28
EQUATION OF CURVE :
ACC = ADT°-89 x 0.007
< OJ-i -a=5 I
s: —=> Eo \O -Q< —CO •>
Q ^-n— <_>
—I Oo <CO v—
'
< h-
£2
22&-
200-
175-
150-
125-
100-
75-
50-
25-
HARRISBURG
©DALLAS
MILWAUKEE-HWY. 794
MILWAUKEEHWY. 45
1 \ 1 1
1—
10,000 30,000 50,000 70,000 90,000
AVERAGE DAILY TRAFFIC (ADT) , VEHICLES/DAY
Figure 3« Selected total solids accumulation rates versusaverage daily traffic.
29
The terms of Equation k were determined in the following manner. P
is the calculated pounds of the carrier pollutant, total solids, on the
surface of the drainage area from Equation 2. The average runoff rate
(r) is calculated using Equation 1 and the equation for flow duration.K2 is selected based upon the drainage area configuration (Type I, II
or III). Equation k utilizes the average runoff rate because it
showed the best correlation to discharge loads monitored at each site.
A sensitivity analysis based upon a comparison of predicted andmeasured total solids was used to develop K2 values for each site.
For a series of monitored events at a particular site, the build-upand wash-off equations (2 and k) were applied using different K2 valuesto determine the best fit for the total of all events. For the pre-liminary analysis, a range of K2 values from 1 to 20 was selectedbecause this range was thought to include the wash-off potential that
could be expected for highway sites. A K2 of 1 will remove 22 percentof the available pollutant land when r is equal to 0.25 inches perhour (0.63 cm/hr) , while a K2 of 20 removes 99 percent with the samer value.
Table 16 presents the results of applying a K2 value of 1 and 20 to
actual runoff and dry day data for the Milwaukee 1-79^ site. The K]
value used in this analysis was 63 lb/mi-day (17.2 kg/km-day) as
developed previously. This sensitivity analysis shows that a K2 valueof one washes off 777-3 lb (352.6 kb) of total solids, while a K2
value of 20 approximately doubles the pounds washed off.
Figure k shows a graph of the measured total solids discharged for
nine monitored events and the corresponding computed values during1976 at Milwaukee 1-79^ To determine the operating range of K2,
values (1, 5, 3 and 20) were selected for analysis in order to underand over predict the measured data. From the Figure, it can be seen
that the K2 values of 1 and 20 did not provide good estimates of thetotal solids washed off, while K2 values of 5 and 8 provided betterestimates. This same procedure was used for the other monitoringsites. The monitored rainfall events in which total solids weremeasured at each site were compared to predicted total solids usingK2 values that ranged from 1 to 20. Those values that were closestto the sum of the measured values were then graphically compared to
individual events as shown in Figures k to 11. The range of K2 valuesthat were selected for each site are presented in Table 17-
For purposes of the predictive procedure, a single K2 value wasrequired for each of the three site categories. The values in Table17 are generally in the range of 5-8 with the exception of Harrisburg.Based upon the comparison of individual K2 values with actual monitor-ing data, the K2 value of 6.5 was selected as representative of theMilwaukee Highway 45, Nashville and Denver sites which contain sometype of curb or barrier, structured drainage and grassy right-of-way(Type II). For the Harrisburg site a value of 12 was selected as
30
Table; 16. K2 sensitivity at Mi lwaukee 1-794.
K2=1, K2=20,Runoff, Dry pounds pounds
Date in. days washed-off washed-off
5/10/76 0.187 5 8.1 46.1
5/15/76 0.07^ 5 6.2 37.45/28/76 0.240 13 43.3 132.8
5/30/76 0.290 2 5.0 8.7
6/13/76 0.138 14 39-3 134.46/18/76 0.213 5 60.3 55.66/24/76 0.021 6 6.4 19.56/27/76 0.027 3 8.6 27.4
7/28/76 0.093 31 54.6 280.0
7/30/76 0.341 2 167.7 70.78/5/76 0.053 6 24.4 37.28/13/76 0.107 8 51.0 77.78/25/76 0.062 12 34.6 88.38/28/76 0.225 3 114.1 63.1
9/1/76 0.091 4 42.7 32.3
9/9/76 0.124 8 61.0 75.09/9/76 0.031 10 17.1 47.2
10/5/76 0.064 16 42.9 148.5Total » — ™ — 777.3 1371.9
metric units: lb x
in. x
0.454 = kg
2.54 = cm
Table 17. Range of l<£ values for each monitoring site,
Site *2
Milwaukee 1-794
Mi lwaukee Hwy 45Harrisburg 1-81
Nashville I -40
5-85-8
4-205-8
31
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II II < II II
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III Va M 75 a
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(s6>| = pQvo * sq|)
savsanosnvioi
32
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cm ai cm^ 2 ^
vl i00 »»
co
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33
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san 'sanosnvioi
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OfHZBKKKXKSeKCK
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00CM
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(sB>| = frgfO x sqi)
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35
QLUccD
lO y, 00II < II
cnUJ CM* ^ ^
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(s6>| = frg^o x sq|)
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36
1800 —|
1600 —
1200 —
coCO_i ^CO -*
Q II
_J "*800 —
400
01 K:
= 4
MEASURED
K2= 20
5/16 10/20-21
1976 STORM EVENTS
Figure 9- Harrisburg K~ selection, 1 976
37
s s-*
MEASURED
V*',>%v. K
2=20
COCO -~
CO -*
Q ii
!j'*'
o 33
°'
h- .QO =
300-
200
100-
15:?:sm Sm
knk16/6 6/9
»S
» v|i sli «in 1 , 1 Ml r
1 1i 1 1 1
6/14 6/17 ' 6/25 6/25
1977 STORM EVENTS
Figure 10. Harrisburg «2
selection, 1977
33
in co co
n < ii
CN £ CM^ 2 *
IHH au
lllllllllllllllllllllllllllllllllllllllllllllllllllllllllll
IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIHIIII!iiiinnlllll™^^
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minium
t—i—i—i—i—i—
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(s6>| = ^gt-o x sqi)
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39
o
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en
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Den
representative of a rural, flush shouldered site which uses grassyditch conveyance for the surface runoff. A K2 value of 5.0 wasselected for the Milwaukee 1-79^ site. This value will be used forall elevated bridge decks which are completely paved. A summary ofthese values for each site type is presented in Table 18.
Table 18. K2 values used in predictive procedure.
Site type K2
Type I 5.0Type II 6.5Type I I I 12.0
The Type III site requires the highest K2 value in order to wash offlarger amounts of the pollutant load per runoff volume than othersites. The flush shoulder and the nonpaved drainage conditions whichcharacterize this site type probably account for the high value in
that more pollutant wash-off is required per event to remove thepollutant load contained within the grassy areas adjacent to the road-way. The Type I site has the lowest K2 value which indicates that it
is easier to wash off pollutants from a site which is all paved thana site which has both paved and unpaved areas (Type II and III).
The data used in the selection of these K2 values are listed in AppendixB, Tables B-1 - B-4. These tables present the predicted and measuredtotal solids values for each K2 in the selected range of values on
Table 17. It must be pointed out that the values in these tables arebased upon nonwinter conditions and that more emphasis is placed upon
nonconstruction years (1976 at Milwaukee Hwy. 45 and 1977 at Nashville).Small differences between individual total loads for each K2 value wereneglected in assigning the three site specific K2 values.
CONSTITUENT LOADINGS
The final component of the predictive procedure transforms the poundsof total solids washed off into pounds of lead, COD, suspended solidsor any other of the 16 quality constituents available in the model.
The resulting loadings may be used to evaluate the impact of thesedischarges on the receiving water or to evaluate the loads as requiredby special model application. A general approach for determining therelative impact of these loadings on the receiving water is discussedin Section V (page 88).
The prediction of heavy metals, nutrients, solids and other parametersfor individual storm events relies upon the correlation of measuredtotal solids loads to the measured loads for each of the remainingparameters. Tables 19 and 20 present the results of each of theselinear regressions in the form of an equation for predicting eachparameter as a function of the total solids load for each site. For
ko
Table 19. Constituent equations developed for Type I
and Type I I I sites.
Paramete r
Type 1
Milwaukee 1-794Type III
Harrisburg 1-40
SS s 0.53TS - 3.2* 0.32TS - 36.8*
VSS S 0.191TS + 0.2* 0.061TS-3.3*
TVS S 0.221TS + 13.3 0.32TS - 32.3*
TKN S 3.3xlO" 3 TS + 0.16 3.lxlO_3TS+ 0.55*
BOD S 0.023TS + 1.5* N/A
TOC = 0.057TS + 0.80* O.O68TS - 5.85*
COD as 0.202TS + 5.47* 0.087TS + 0.65*
TN s 1.37xl0" 3 TS + 0.12 1.83x10 TS+0.054*
TPOi, s l.OxlO" 3 TS + 3-OxlO"11
2.15xlo"3TS - 0.245
Cl~ = 0.034TS + 2.59 0.135TS + 2.6*
Pb s 5.6xlO~ 3TS - 0.02*4 4.1xlO_Zt
TS - 0.029*
Zn B-4
8.4x10 TS + 0.014 2.67xl0"4TS-0.0ll*
Fe
Cu
0.015TS + 0.59
-4 -2,2.9x10 TS+7.3xlO 4
0.014TS - 1.61*
-c "3*7.4x10 5TS+8. 78x10
Cd S 1.4x10 TS-1.4xl0~ 3 4.0xlO_5TS+0.007
Cr B-4 -3
1.6x10 TS+1.2xlO-4
2.3x10 TS-0.028
Hg S-7 -4
-7-6x10 TS+8.8x10 -5.8xlO"6TS+0.015*
Note: TS = total solids (lb)
* - Variables correlate at the 95 percent confidence level,
metric units: lb x 0.454 = kg
41
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•JC o o o•k -js -ic -a -a vo *- i— *-CO 1AU\ OON CN X X X
LA O CM O OO OfAf-SCM
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(0 a> (/) V) 1 (A (/)(/) 1 1 CO I 1 00 1 1 1 1
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ro o
42
example, the determination of the COD load for the Milwaukee 1-79**
site is as fol lows:
COD (lb) = 0.202 TS + 5.^7where: TS = total solids in pounds
The asterisk in Tables 19 and 20 next to some equations indicates sig-
nificance at the 95 percent confidence limits (1). This indicatesthat one can be 95 percent confident that the sample was not selectedfrom a population for which no correlation exists (i.e. that thecoorelation coefficient R is equal to zero). An equation may stillprovide good estimates even if the variables in that equation do notshow a significant correlation at the 95 percent confidence level. In
fact, an equation developed using regression techniques is the bestfit model of the data being analyzed. For this reason, all theequations developed to predict various pollutant parameters from totalsolids will be used in the predictive procedure. The asterisks wereused in Tables 19 and 20 only to indicate the relative strength ofthe correlation between the variables in each equation. However, mostof the variables did show a correlation at the 95 percent confidencelevel
.
The predictive procedure will use a separate constituent equation foreach general site type. Individual equations are required to accountfor differences between sites in drainage and solids retentioncharacteristics. Table 21 provides the calculated loading for each ofthe five monitoring sites using individual constituent equations fromTables 19 and 20 and a general site equation for Type II areas.Suspended solids, COD, lead, iron, and TPO4, are listed to evaluate theindividual loadings. A given range of total solids loads varying from
500 to 10,000 pounds is used to determine the constituent load for theabove parameters. The numerical average of the three locations com-prising Type II sites, as well as the results for a general Type II
site equation from Table 20 are also listed to provide an indicationof the accuracy of each approach. The site specific equations providezero loadings for the Milwaukee Hwy. A5 site for some parameters whenless than 500 pounds of total solids are available. The regressionequations used to develop these relationships are based upon actual
monitoring data which did not include a runoff event with less than
490 pounds of total solids. In the same manner, the TPOjj loads arenot accurate at the 2,000 to 10,000 pound load range since no actual
data points of this magnitude were available to produce the predictiveequation. To account for these differences, the average equation of
Table 22 will be used in the predictive procedure for the Type II
si tes.
The use of the general Type II site equation as compared to individual
Milwaukee, Nashville and Denver equations was checked by comparingthe general equation to the site specific equation using the t test.
The two equations did not differ at a 95 percent confidence level and
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Table 22. Constituent equations developed for Type II sites
Parameter Equation
SS 0.63TS - 188
VSS 0.152TS +13.5
TVS 0.263TS + 243
TKN 5.^6x10"3ts +1.28
BOD 3.0xlO"2TS +28.3
TOC 5.6x10"2TS + 25.2
COD 0.193TS + 275-3
TN 1.3xlO" 3TS + 0.713
TPO4 2.25x10" 3TS - 0.32
CI 0.042TS + 87
Pb 1.02x10"3tS + 0.04
Zn 5.84xlO"^TS + 0.103
Fe 1.96xl0_2TS - 5-0
Ni N/A
Cu 3.l6x10-2fTS + 0.064
Cd 4.l6x10~ 5TS + 0.021
Cr 4.3x10~5tS + 0.036
Hg 2.44x10" 6TS + 1.006x10" 6
Note: TS - total solids (lb)
metric units: lb x 0.454 = kg
45
are used in the model for Type II sites. Large differences in the
equations for suspended solids, especially at the Milwaukee 45 site,
presents difficulties at low total solids levels. For those events
in which the total solids load is less than 300 pounds (136 kg), thesuspended solids equation results in negative numbers or zero load.
Construction activity at this Milwaukee site during 1977 is the primaryreason for the large discrepancy between sites.
Tables 23, 24 and 25 list the calculated and measured lead, andsuspended solids values for some of the sites using the Type I site
specific and Type II general equations. These data points are deter-mined using the actual measured total solids load for determiningeach of these parameters. Later discussions in Section IV show howthe computed total solids, lead and suspended solids data using themodel compares to the corresponding measured data.
Applying the Type I site equation to Milwaukee 1-794 monitored total
solids data (Table 23) produced calculated values for suspended solidsand lead which Were comparable to measured values on an event basis.
A comparison of the sum for all events of calculated and measuredvalues shows that the Type I site equation over-predicts lead by 3
percent. Similar results were obtained for the Milwaukee Hwy. 45 site(Table 24) using the general Type II site equation. Summing the valuesfor all events showed that suspended solids and lead were over-pre-dicted by 7 and 6 percent respectively.
The Nashville data (Table 25) present a worst case example where a
general equation (Type II site) is applied to construction year data.The data show that calculated suspended solids are generally lower thanmeasured suspended solids on an event basis, especially for thoseevents which had low monitored total solids wash-off (less than 100 lb
(45 kb)). However, when calculated and measured values for suspendedsolids were summed for all events, calculated suspended solids wereonly 8 percent lower than measured. Although considerable variationbetween calculated and measured suspended solids wash-off occurredon an event basis, the general equation still provided reasonableestimates of suspended solids for the construction year. Overall pre-dicted lead values were 14 percent lower than measured values. A14 percent difference is acceptable for model prediction.
46
Table 23. Milwaukee 1-794 calculated suspended solids and
lead loadings using Type I site equations.
Total solids,lb measured
SuspendedMeasured
sol ids, lb
CalculatedLead, lb
Date Measured Calculated
6-18-76 87 62 43 0.69 0.467-23-76 45 15 21 0.14 0.237-30-76 a * -- * —8-5-76 14 2 4 0.02 0.058-13-76 94 40 47 0.85 0.508-25-76 29 10 12 0.10 0.148-28-76 135 97 63 1.00 0.739-9-76 98 36 49 0.37 0.529-19-76 28 8 12 0.11 0.136-8-77 35 21 15 0.19 0.176-10-77 12 3 3 0.03 0.046-11-77 173 69 88 0.66 0.946-17-77 119 49 60 0.61 0.646-28-77 12 4 3 * —6-28-77 62 42 30 k —6-30-77 84 39 41 0.40 0.457-3-77 27 10 25 * —7-17-77 115 21 58 * ~~
8-13-77 143 * 73 A
8-28-77 96 48 48 * —9-23-77 5 3
— * --
9-23-77 28 13 12 ft --
9-24-77 62 34 30 k --
Total 623 669 5.17 5.00
Note: * No data available.metric units: lb x 0.454 =f kg
47
Table 24. Milwaukee Hwy 45 calculated suspended solidsand lead loadings using Type II site equations.
Total solids,
lb measuredSuspended
Measuredsolids, lb
CalculatedLead , lb
Date Measured Calculated
6-18-76 5886 4306 3520 6.67 6.047-28-76 943 518 406 1.43 1.007-30-76 2371 1290 1306 2.76 2.468-5-76 470 127 108 0.20 0.528-14-76 2120 805 1148 2.04 2.208-25-76 4029 601 2350 1.56 4.158-27-76 2490 1636 1381 4.27 2.589-9-76 3498 2010 2016 4.67 3.619-19-76 1109 492 511 0.80 1.175-4-77 742 135 279 0.40 0.806-5-77 6149 3891 3686 8.40 6.316-5-77 8670 6151 5274 7.30 8.886-6-77 490 82 121 0.20 0.546-8-77 1123 428 519 0.80 1.196-11-77 3524 1402 2032 1.80 3.636-17-77 4849 2974 2867 4.60 *».99
6-27-77 3388 859 1946 2.50 3.496-30-77 3173 1416 1811 3.20 3.28
Total 29123 31281 53.60 56.84
metric units: lb x 0.454 = kg
48
Table 25. Nashville 1-40 calculated suspended solids andlead loadings using Type II site equations.
Total solids,lb measured
SuspendedMeasured
solids, lb
CalculatedLead, lb
Date Measured C«alculated
4-21&22-77 1559 .922 794 2.23 1.634-22-77 1265 626 609 1.27 1.334-28-77 156 77 0.22 0.206-13-77 134 28 0.11 0.186-14-77 605 278 193 0.78 0.666-19-77 1943 1272 1036 4.73 2.026-22-77 1246 555 597 1.05 1.31
6-23-77 2944 1630 1667 4.10 3.046-24-77 1486 777 748 1.52 1.56
2.866-25-77 2767 1835 1555 4.059-5-77 427 220 < 81 0.46 0.489-6-77 78 30 0.09 0.129-13-77 170 66 0.19 0.21
9-13-77 595 135 187 0.52 0.659-14-77 368 161 44 0.42 0.429-14-77 3378 1504 1940 1.89 3.489-16-77 597 249 188 0.33 0.659-19-77 555 327 161 0.80 0.61
Total 20273 10692 9800 24.76 21.39
metric units: lb x 0.454 = kg
49
SECTION II I
INPUT REQUIREMENTS AND OUTPUT FORMAT
This section of the report contains the detailed listing of the pre-dictive procedure, as well as the description of the output. Recom-mendations for use of the procedure in both single event and long termapplication are included.
GENERAL INPUT REQUIREMENTS
The predictive procedure requires input data describing the highwaysystem under consideration and the rainfall record being evaluated.The procedure can be manually operated using a calculator or run on a
small computer for each application. The description that followswill concentrate on the desktop approach since the computer applicationfollows the same procedure but relies upon the computer to manipulateconstants and equations. Appendix C (page 119) lists the computerprogram and Appendix D (page 13*0 lists the step-by-step input
requi rements.
Site Description
The highway system that is to be analyzed must be defined as to its
total drainage area. Total drainage area for an existing system can
be that area comprising both paved and nonpaved surfaces whose runoffcontributes to a single storm sewer or drainage ditch discharge. Thus,the evaluation of an existing highway system may be comprised of a
series of small drainage areas within the discharge point. This typeof evaluation is important in those applications which are meant to
compare actual flow measurements with the output of the model. For
more general evaluations, the total area contained within the highwayright-of-way will be sufficient for analysis.
The length of the highway system must also be defined, ie. the actual
distance of the highway section from one end of the drainage areato the other end. This is in contrast to curb mile or lane mile,which is a multiple of highway length.
Curb Configuration
This parameter describes the general type of drainage configurationused to remove the paved area runoff from the surface of the highway.Within the development of the model, the monitoring data showed con-
siderable differences in hydraulics between sites which were found tobe related to these drainage structures. Harrisburg 1-81 is charac-terized by flush shoulder areas which disperse the paved runoff to
drainage ditches alongside the highway and then by overland flow to
the inlets of the storm sewer system. Milwaukee Hwy kS, Nashvillel-AO and Denver 1-25 have mountable curbs which channel the runoffalongside the curb to inlets of the storm sewer system. Figure 12
50
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51
presents typical cross^sections for Harrisburg l-8l and Milwaukee Hwy
45. A more complete description of each of these sites is available
in Vol. IV, "Characteristics of Runoff From Operating Highways,Research Report", of this document series (1).
For purposes of analysis, three types of curb or drainage types areused in the predictive procedure; the flush shoulder which charac-
terizes the rural Harrisburg site, mountable curbs with inlets alongthese curbs for removal of the paved surface runoff, and the mountable
curb with impact barriers adjacent to each set of lanes as is charac-teristic of the elevated bridge deck at the Milwaukee 1-79** site.
These three configurations are used throughout the predictive procedureappl ication.
Average Daily Traffic
ADT or average daily traffic refers to that volume of vehicles whichpasses through the highway system on a per day basis. The averagevalue as used is determined over a long period of time to account for
weekday, weekend and seasonal variations. ADT is used to select the
proper accumulation rate for the runoff quality predictions. Theaverage volume of traffic is used since the build-up of pollutantsbefore a rainfall event is a result of an unknown time period to the
last storm exhibiting 100 percent wash-off. Further descriptions ofADT and pollutant accumulations have been discussed in the Kj componentdevelopment.
CONTINUOUS APPLICATIONS
The application of the predictive procedure or model can be utilizedfor single rainfall events or for a continuous simulation of annualor seasonal rainfall. The continuous mode of operation using thecomputer model will be explained first since the single event proce-dure relies upon the same data requirements.
An effective evaluation of highway runoff water quality includes thesimulation of a number of continuous rainfall events. The time periodspanned by these rainfall events depends largely on the climatic regionof the highway area. An arid climate might require several to manyyears of rainfall data to evaluate highway runoff water quality, whilea single year of continuous rainfall data might be sufficient in
humid areas, depending upon the objective of the simulations. Sincethe procedure is not meant to evaluate winter snow melts and frozenground conditions, only nonwinter periods should be evaluated. Anexample would be in the Milwaukee area where frost and snow coverconditions are possible between November and March of each year. In
areas of the country where frost and snow cover is not present, thesimulation can be used for the entire year.
52
Rainfall Selection
The rainfall data required for the model are total volume of rainfalland the corresponding duration for each event. The date of each eventis also required so that the days of pollutant accumulation betweenstorms can be evaluated. These data are usually available from the
local weather service, or listings of hourly rainfall may be obtainedfrom the U.S. Department of Commerce. (Address: U.S. Department ofCommerce, National Climatic Center, Federal Building, Asheville, N.C.
28801).
The selection of a period of record to be used to evaluate the waterquality affects of the highway can become quite complicated if ten or
twenty years of record are available. In order to assist the highwayengineer in selecting which period should be utilized for the simula-tion, the following suggestions are provided:
1. Analyze with the predictive procedure as many continuous,
complete periods as budget and time allow.
2. Based on a ranking of total rainfall volumes for eachperiod, average rainfall per storm and number of storms
with average intensities greater than one inch (2.54 cm)
per hour, select one or two periods of high, medium andlow rankings in each category. This provides a representa-tion of the rainfall record from which one or two periodscan be quickly analyzed.
3. The highest loadings to the receiving water may not be
associated with those periods of highest rainfall but
rather those of highest intensity after long buildup
periods. Selection of extreme periods should reflectthese criteria.
An example of the selection of one or two years of rainfall record foranalysis with the predictive procedure can be found in Table 26. Tenperiods of rainfall for a hypothetical area are listed according tototal rain, number of storms, average rain per storm and storms withaverage intensities greater than one inch per hour. According to thedata in this Table, 1969 contained the highest volume of rainfall andnumber of storm events, while 1976 contained the lowest volume of rain-
fall and lowest average rain per storm. The year 1974 contained thehighest average rainfall per storm. The average values for each ofthese columns provide an indication of typical conditions and the yearwhich most closely represents typical conditions is marked by anasterisk.
Based upon the data of Table 24, a typical and extreme year can beselected for each of the categories. The extreme year representingthe number 1 ranking in each category can be used to evaluate the most
53
Table 26. Nonwinter rai nfal 1 summaries for a
hypo thetical area.
Number ofTotal ra in, Number of Average rain events
Year (in .) events per event, in. >1 in./hr
1968 15.8 38 (5) 0.42 2
1969 16.4 (1) 48 (1) 0.34
1 970* 14.3 (5) 35 0.41 1
1971 15.9 41 0.39 3
1972 11.4 32 (10) 0.36 (5) 1
1973 12.8 39 0.33
1974 15.6 35 0.45 (1) 2
1975 11.1 38 0.29
1976 10.2 (10) 37 0.28 (10) 1
1977 16.0 37 0.43 3
Average 13.95 38 0.37 1.3
Note: (1) = extreme - high
(5) = typical
(10) = extreme - low" = typical year
metric units: in. x 2.54 = cm
significant water quality impact. The category of average volume ofrain per storm would best represent this need and, therefore, 1974 wouldbe selected. The year 1977 also might be selected for the samepurposes since it has the second highest volume of rain per storm but
also has three storms of average intensity greater than one inch. Atypical year such as 1970 and 1972 can be used to represent the entireperiod of record. Thus, if time or budget do not allow the analysisof all 10 years, then a typical year could be analyzed to provide an
indication of the annual load. For the example in Table 26, thetypical year selected would best be represented by 1970 since it has
values very close to the average values for the period of record forcategories of total rain and average volume of rain per storm. Also,there were not a large number of storms with an average intensity ofgreater than one inch per hour during 1970.
The period of rainfall selected for analysis is used to evaluate theresponse of the existing or proposed system for the total period andfor individual rainfall events within the period. Later descrip-
54
tions of individual storms will require a look at the long term rain-
fall to set prestorm conditions as close to the actual prestorm surfaceload as possible.
MODEL OPERATION AND INPUT REQUIREMENTS
The detailed input data formats are listed in Appendix D (page 13*0to provide the model user with a step-by-step procedure. A moregeneral approach is discussed in this section of the report. Figure13 represents the flow diagram for the model operation.
Site Characteristics
The first requirement of the model is to specify the size and type ofsite that is to be analyzed. The site description is as follows:
Description Uni ts
1. Size of total drainage area Acres (ha)
2. ADT Vehicles per day
3. Type of siteTYPE I
- All paved, bridge or overpassType II - Paved and unpaved with curbs
and inlets along the pavedarea.
Type III - Rural sites with flushshoulders, grassy ditch con-veyance to inlets.
k. Site length, i.e., distance from one Miles (km)
end of drainage area to the other end.
Rainfal 1 Record
The rainfall record selected for analysis is input to the predictiveprocedure in the following manner:
Description Units
1. Number of rainfall events to be evalu-ated (1 to N).
2. Month, day and year of each event.
3. Rainfall for each event. Inches (cm)
k. Duration of each rainfall. Hours
5. Dry days prior to each rainfall. Days
The rainfall record relies upon the total volume of rain for each
event and the date on which the rainfall started. Thus, even though
the rainfall may extend over two days, the starting date is the
reference point. The dry day variable refers to the number of days
55
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56
prior to this event in which accumulation occurred. For example,a rainfall on Saturday, July 22th that was preceded by a rainfall
that ended on Sunday, July 16th, would have six days of accumulation.
Model Operation
The actual operation of the model or predictive procedure is as follows:
Runoff Volume - The volume of runoff in inches (cm) is determined by
the solution of one of following equations depending on site type.
Type I
0_ = 0.969 R -0.0187
'T^R^DD' ' 0858 * 0.470
Type I I I
Q = R 1 - 892 DD" -^ * 0.845
where; Q_ = runoff in inches (cm)
R = rainfall in inches (cm)
DD = dry days to last rainfall (minimum = 1 dry day)
For example, a TYPE II site having 0.25 inches (0.63 cm) of rain (R)
after 6 dry days (DD) would produce the following total runoff (Q)
:
= R1 ' 369 DD" * 0858 * 0.470
0_= (0.25K369) (6"" ' 0858
) (0.470)
Q = 0.06 inches of runoff (0.15 cm)
Runoff Rate - The duration of the rainfall event is used to determinethe length of the runoff period for that event. The followingequations (dependent on site type) describe this procedure:
TYPE I
FD = 1.12 RD + 0.69
TYPE I I
Dry days ^ 10
FD - 1.06 RD + 1.7.9
Dry days < 10
FD - 1.27 RD + 2.16
Type III
Dry days £ 10
FD = 1.92 RD + 4.18Dry days < 10
FD = 1.48 RD + 8.28
57
where; FD = runoff duration in hours
RD = rainfall duration in hours
Thus for the example above, if the 0.25 inches (0.63 cm) of rainfall
lasted for 50 minutes, then the duration of runoff for TYPE II andsix dry days would be:
FD - 1.27 RD + 2.16
=3-22 hours
The average runoff rate (r) for this event would be calculated as
fo 1 1 ows
:
r = Q/FD0.06 in.
r =3.22 hr
= 0.019 in./hr (0.05 cm/hr)
Runoff Qual i ty - The next block of equations in the model is relatedto pollutant quality determinations. The first step in this processis to determine the pollutant accumulation rate for the site beingmodeled. The general equation for this variable is defined as:
Kt = (ADT ' 89) * 0.007
where; Kj = pollutant accumulation rate, lb/mi -day (kg/km-day)ADT = average daily traffic, vehicles/day
Continuing with the example application, if the site has an ADT of
30,000 vehicles per day, the Kj value that results would be:
Kj = (30,000°- 8 9) * 0.007= 67-6 lb/mi -day (19.1 kg/km-day)
The calculated Kj value is next used to predict the pounds of total
solids on the surface of the drainage area at the start of the stormevent.
P = Po + (Kj x HL x T)
where; PQ= initial surface pollutant load, lb
P = pollutant level after build-up, lb
K] - pollutant accumulation rate, lb/mi -day (kg/km-day)HL = highway length, mi (km)
T = time of accumulation, days (20 day maximum)
53
The variable PQ is a function of the amount of pollutants left on thesurface after the last runoff event. In the long term program, thisloading is carried through the calculations on an interative basis by
subtracting the pounds washed off from the previous pollutant load.
The build-up rate, K^ is then used for each dry day to add to thisinitial level until the pollutant level at the storm start is deter-mined.
The initial estimation of a PQ is made easier if the first storm to
be evaluated follows a storm of average rainfall intensity greaterthan 0.5 inches (1.27 cm) per hour. This large storm is assumed to
wash-off essentially all of the available total solids load on thesurface. The succeeding dry days are then used to build up the availa-ble surface load to the start of storm conditions. Further descrip-tions of P and K-\ are included in the components description of thisreport.
Continuing with the example application, if the accumulation period is
six days, the highway length is 6.5 miles, K^ is 67.6 lb/mi-day (19.1
kg/km-day) (predicted from an ADT of 30,000 vehicles/day) and PQ is
zero (assume accumulation period started with a large storm), thepollutant level after build-up (P) would be:
P = P + (K] x HL x T)
= 0.0 + (67.6 x 6.5 x 6)= 2,636 pounds (1,196 kg) of total solids available at
the start of event number one
If a large rainfall event does not occur at the beginning of the initial
accumulation period, PQ must be estimated. One method is to evaluatelocal climatic data to determine the average number of dry days between
rainfall events. PQ can then be estimated using average number of
dry days, K] and highway length. In the example above, if a large
storm did not precede the initial accumulation period, and if the
average number of dry days between storms was determined to be four,
an estimate of P could be calculated as follows:
P = K| x HL x T= 67.6 x 6.5 x k
- 1,758 lb (797 kg)
The next step in the predictive procedure is to remove a portion of
the surface load P using the average runoff rate (r) calculatedpreviously.
The standard wash-off equation that is used in the model is:
P D= P (i- e-K2<-)
where; Pp = pollutant load discharged (total solids), lb (kg)
P = pollutant load at the storm start, lb (kg)
59
I<2 = wash-off coefficientr = average runoff rate - in./hr (cm/hr)
K2 is selected based upon site characteristics as follows:
K2 = Type 1 Type I I Type I I I
5.0 6.5 12.0
Continuing with the example for a Type II site, the pounds of total
solids removed would be determined as follows based upon the valuespreviously calculated for P and r:
Pd:U3m^-(6. 5)(o.o. 9))
= 306 lb of total solids discharged (139 kg)
The surface load after the storm would be calculated as follows:
Po " P " P D~ 2,636 - 306= 2,330 lb of total solids remaining (1,057 kg)
PQ for event number two is 2,330 lb (1,057 kg). Another cycle ofcalculations can now begin.
The determination of any of the remaining 16 parameters available in
the model is based upon the equations of Tables 19 and 20. Thus, todetermine the pounds of lead for this storm, the equation is:
Pb = 1.02 x 10"3 (P D ) + 0.04= 1.02 x 10~3 (306) + 0.04= 0.35 lb of lead discharged (0. 1 6 kg)
The remaining quality parameters can be calculated using the appropriateequations.
The above example of the predictive procedure provides the methodologyfor calculating the pounds of total solids and lead for one storm event.The event under consideration may be a design storm or single rain-fall event which was monitored for flow and quality. In either case,the prestorm history for determining the surface total solids load
at the start of the storm is as important as the rainfall data. Con-tinuous records of rainfall are important in any application andshould be used to set the individual prestorm conditions.
60
SECTION IV
PREDICTIVE PROCEDURE RESULTS
Previous descriptions of the ability of the predictive procedure to
match measured data have been presented for individual components.Within this portion of the report, the entire model is applied to
the sites which were used to develop the relationships of the model
as well as one site independent of this project's data. The final
portion of this section presents a discussion of the limitations of
the predictive procedure.
MODEL APPLICATIONS
The use of rainfall records from each of the five monitoring sites
within this project, as well as the data from another study at
Dallas, Texas (13) will be used to evaluate model accuracy. Thoseevents which have been monitored for various quality parameters will
be compared with the model output to determine its accuracy. Summariesof the input data and output results are listed in the Appendix G
(page 1^6) for all storms used in the continuous simulation. Input
data for each site is volume of rainfall, duration of rainfall, andnumber of dry days prior to each event. The K^ values calculated in
Section II for each site will be input to the model for all sites exceptDenver where the model will calculate Ki . The rainfall record at
the Denver site prevented a reliable estimate of K^ . Differencesbetween user supplied and calculated K] values will be discussed.
Milwaukee I-79**
The predictive procedure was run for the nonwinter rainfall events in
1976 and 1977- Each simulation was started, after a significant rain-fall event occurred which was assumed to wash off the surface pollutantload to base conditions. There are a total of 33 events used forcomparison of the predicted and monitored runoff quality. Appendix G
(pages 146 to 15*0 presents the model output data for these eventswith the rainfall volume and duration plus the K^ value determinedin Section II as the model input. The model output provides rainfalland runoff summaries, as well as surface loadings before and aftereach event
.
Table 27 lists the measured and computed quality results for total andsuspended solids, TP04, COD, lead and iron. Figures 14 and 15
graphically represent the total solids and lead comparisons. Thetotal solids data for these events shows reasonable accuracy whenthe two year total of 1862 lb (845 kg) is compared to the predictedvalue of 1640 lb (7^5 kg). Individual events within this periodhowever, show a substantial variation between predicted and monitoredvalues. For example, the first three events in 1976 show largevariations in the predicted versus monitored total solid values.
61
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The total solids comparisons of Figure 14 show five storms which
account for the majority of the differences between the predicted and
actual data. These storm events occurred on 6/18, 7/28, and 7/30/76
and 6/11 and 8/13/77- The first two dates show the model to over-
predict while the remaining dates underpredict the measured loadings.
The event which occurs on 6/8/77 represents the first nonwinter
monitored event of 1977 and thus, is subject to the initial pollutant
estimation of zero pounds preceeding this event after total washoff
from a storm of 0.54 inches (1.3 cm) in one-half hour. The first two
events of 1977 are reasonably close to the measured and reflect the
accuracy of this total washoff assumption.
The suspended solids loadings of Table 27 show the total pounds dis-
charged for monitored events to be 570 lb (259 kg) and the predictedto be 658 lb (299 kg). Suspended solids data was not collected for
events that occurred on 7/30/76 and 8/13/77- Zero predicted suspendedsolids result when the surface total solids load is less than 6 lb
(2.7 kg) as in events of 6/8 and 6/10/77-
The total phosphorus loads (TP04) listed in Table 27 show very closecorrelation with the total monitored load being 1.03 lb (0.46 kg)
versus a predicted load of 0.86 lb (0.39 kg). Small variations are
present in the individual storms. Similar trends are found for COD,
lead and iron loadings. The lead data is graphically presented in
Figure 15 where those events that show the greatest variation are
the result of the large differences in monitored and predicted total
solids. Table 27 shows the monitored totals to be 5-17 lb (2.34 kg)
and the predicted to be 4.55 lb (2.06 kg).
Mi Iwaukee Hwy. 45
The Milwaukee Hwy. 45 monitoring data contains 18 events which will
be compared to the predicted values in the following figures. A
total of 39 events were simulated during 1976 and 1977 with the
results of the model output presented in Appendix G (pages 155-162).
Table 28 presents data for total and suspended solids, lead, iron,
COD and TP0. , while Figure 16 and 17 present graphical comparisonsof total solids and lead. The 1976 simulation period uses the K^
value associated with nonconstruct ion , 172-5 lb/mi-day (48.6 kg/km-day)
,
while the 1977 simulation period (construction) requires a K1 of
367.7 lb/mi-day (103.6 kg/km-day) as determined in Section II. Usingthese K-\ values for the appropriate years, the predicted total solidsfor the two years was 63,478 lb (28,794 kg) while the measured valueswere 55,024 lb (24,981 kg). A few of the individual runoff eventswithin the simulation period show large variations, but the overallaccuracy of the model is relatively good.
67
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69
MEASURED
P5 COMPUTED
6/18 7/28 8/5 8/14 8/25 8/27
1976 STORM EVENTS
c/) g, 13000
1000
MEASURED
YA COMPUTED
5/4 6/5 6/5 6/6 6/8 6/11 6/17 6/27 6/30
1977 STORM EVENTS
Figure 16. Measured versus computed total solids atMilwaukee Hwy. k$ , 1976 and 1977.
70
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1976 STORM EVENTS
8/27 9/9 9/19
MEASURED
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5/4 6/5 6/5 .
' 6/6 ' 6/8 ' 6/11 6/17 6/27 6/30
1977 STORM EVENTS
Figure I7 Measured versus computed lead at Milwaukee Hwy. k5,
1 976 and 1 977.
71
Nashville I -kO
A total of 18 events at this site were used to compare the monitoredand predicted quality estimates of the model. A total of kd events
were used in the analysis. The results of the model output for Nash-
ville are presented in Appendix G (pages 163 to I69). The Nashvillesite was affected by construction during the entire 1977 monitoring
period so the K-| value of 3^7-3 lb/mi-day (37-9 kg/km-day) corresponding
to this activity was applied (Section II). A simulation for 1976 was
not performed because only four events were monitored that year.
Table 29 lists the computed and actual values for six parameters, while
Figures 18 and 19 present the total solids and lead comparisons. The
total solids measured for the 18 events totalled 20,273 lb (9204 kg)
while the computed values equalled 23,325 lb (10,580 kg). The TPO^
and COD comparisons show the greatest variation in computed and actual
data. The overall predictions for all parameters, however, are
acceptable.
Harrisburg 1-81
A total of 67 rainfall events were simulated with the predictive proce-
dure at this site with 12 of these events having been monitored for
quality parameters. The 1976 events use the K1 value associated withthe construction aspects of that year while 1977 uses the nonconstruc-tion Type III, K^ . The model output is listed in Appendix G (pages 170
to 130) while Table 30 lists computed and measured values for totaland suspended solids, lead, iron, COD and TPOij. Figures 20 and 21
present lead and total solids values in graphical form. The monitoredtotal solids for the period was 3655 lb (1659 kg) while the predictedload equalled 3689 lb (1673 kg). The remaining parameters are alsoextremely close on a total pounds basis with a few individual eventsshowing large variations. The quality comparisons were much moreaccurate than anticipated, considering the complexity of the drainageconfiguration and base flow conditions at this site.
Denver 1-25
The application of the predictive model to the Denver 1-25 site is
somewhat different because the arid climate and resulting long dryperiods complicated the calculation of a site specific K] value. Themodel calculates a K, value based on the ADT data and associated sitecharacteristics. The model output data is listed in the Appendix G
and shows the calculated K] value to equal 378.9 lb/mi-day (106.
8
kg/km-day). Table 31 lists a few selected parameters comparing the
computed and actual values. Figures 22 and 23 present a graphicalrepresentation of the total solids and lead data. The accuracy ofthe model for this site is poor for all parameters because of thedifficulties discussed in the K] components discussions. Except forsuspended solids, the model consistently overpredicts the measuredvalues totalled for those parameters.
72
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76
Table 30. Model results for Harrisburg 1-81, 1976 and 1977
Total
Actualsolids, lb
PredictedSuspendedActual
sol ids, lb
PredictedTP0A, lb
Date Actual Predicted
5/16/76 189 373 20 83 0.27 0.56
7/7 227 627 86 164 0.27 1.10
7/7 1344 1427 509 420 3.13 2.87
9/10 204 287 11 55 0.19 0.37
10/20 632 312 21 63 1.97 0.43
4/24/77 337 37 56 0.22 0.00
6/6 30 18 0.6 0.01 0.00
6/9 53 38 0.4 0.004 0.00
6/14 275 201 8 27 0.07 0.19
6/17 105 63 5 0.04 0.00
6/25 92 157 19 13 0.11 0.09
6/25 167 149 31 10 0.15 0.08
Total 3655 3689 7*7 535 6.43 5.64
COD, lb Lead,
lb I ron , 1 b
5/16/76 ;V 0.04 0.12 0.62 3.62
7/7 20 55 0.06 0.23 4.06 7.17
7/7 * 26 0.61 0.56 20.86 18.37
9/10 21 26 0.06 0.09 0.52 2.41
10/20 77 28 0.35 0.10 2.11 2.15
4/24/77 29 4 0.09 0.00 1.95 0.00
6/6 2 2 0.01 0.00 0.03 0.00
6/9 2 4 0.01 0.00 0.01 0.00
6/14 13 18 0.06 0.05 0.14 1.20
6/17 6 6 0.03 0.00 0.11 0.00
6/25 10 14 0.03 0.04 0.61 0.59
6/25 33
2T2
13
f70
0.05
1 .40
0.03
1 .22
0.95
31.97
0.48
Total 36.59
Note: *Not monitoredMetric units : lb x 0.454 = kg
77
2 2
8 S-j *.< oK- xO </>
I- £
1900-
1700
1500-
1300-
1100-
900-
700-
500'
300<
100'0'
500-
| MEASURED
!5 COMPUTED
ll 1 I i?_5/16 7/7 7/7 9/10 10/20-21
1976 STORM EVENTS
MEASURED
Va COMPUTED
6/14 6/17 6/25 6/25
1977 STORM EVENTS
Figure 20. Measured versus computed total solids atHarrisburg l-8l, 1976 and 1977.
78
Q <*
_i ox
0.90
0.80
0.70
0.60
0.50
0.40
0.30
0.20
0.10
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5/16 7/7
| MEASURED
5 COMPUTED
9/10 10/20-21
1976 STORM EVENTS
Q II
< «•LU ID_J *.
o
.09-
MEASURED
£ COMPUTED
6/14 6/17 6/256/9
1977 STORM EVENTS
6/25
Figure 21. Measured versus computed lead at Harrisburg l-8l,
1976 and 1977.
79
Table 31. Model results for Denver 1-25, 1976 and 1977.
Total
Actual
sol ids , lb
PredictedSuspendedActual
solids, lb
PredictedTP0Z,, lb
Date Actual Predicted
8/2/76 7*1 20 361 1.03 0.00
9/25 529 443 370 91 0.86 0.68
9/26 2293 1437 1652 717 3.12 2.91
V1 1/77 756 211 566 0.68 0.15
4/12 389 65 248 0.43 0.00
4/15 733 286 501 0.77 0.32
4/19 1628 512 927 134 1.55 0.83
5/7 421 732 324 273 0.45 1.33'
6/6 119 355 79 35 0.15 0.48
6/9 149 226 79 0.15 0.19
6/23 64 210 31 0.15 0.15
7/5 1823 6548 1366 3937 2.66 14.41
7/20 807 1540 445 782 0.92 3.15
7/24 235 1420 122 707 0.25 2.88
1/15 329 1113 192
7263
513
7189
0.49 2.19
Total 11016 15118 13-66 29.67
COO, lb Leac1, lb 1 ron , 1
b
8/2/76 302 279 0.63 0.06 13.72 0.00
9/25 213 361 0.72 0.49 13.81 3.68
9/26 700 553 3-53 1.51 62.32 23.16
V1 1/77 317 316 0.81 0.26 17.66 0.00
V12 179 288 0.36 0.11 9.71 0.00
V15 312 331 0.79 0.33 18.49 0.61
V19 738 374 1.69 0.56 36.61 5.03
5/7 170 4-17 0.57 0.79 10.28 9.36
6/6 50 344 0.13 0.40 3.06 1.55
6/9 120 299 0.13 0.27 2.90 0.00
6/23 40 316 0.05 0.26 1.26 0.00
7/5 590 1539 1.18 6.72 38.94 123.33
7/20 387 573 0.84 1.61 14.29 25.19
7/24 108 549 0.26 1.49 3.31 22.84
7/25 112
4133"
490
7029
0.30 1.19 6.07 16.82
Total 11.99 16\ 04 252.43 231.97
Metric units: lb x 0.454 = kg
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82
Dallas 1-45
The predictive procedure was applied to another study area independent
of the subject project. The rainfall record and monitoring data wereused as an indication of the models ability to predict pollutant loads
from a site which was not used to develop the model parameters. This
site will then serve as a verification of the model for a Type I site.
The model used the ADT of 33,000 to calculate a Kj value of 73-5lb/mi-day (20.7 kg/km-day) . The simulated rainfall record for 1977
contains 12 rainfall events with 8 events sampled for various qualityparameters. Appendix G (pages 1 89 to 192) lists the model output for
these events. Table 32 provides the monitored and predicted compari-sons while Figure 2k and 25 present total solids and lead values.
The monitored total solids load equalled 468 lb (212 kg) while the
predicted value was 627 lb (285 kg). All of the remaining computedparameters generally over-predicted except COD. These over-predictionsare related to the methods used to determine total flow volume for
this site. Because the detailed hydraulics data for this sitewas not available at the time, individual flow records were estimatedassuming 90 percent of the rainfall resulted in runoff (Q/R = 0.9).This estimation has a significant impact on events that occurred on7/21 and 8/28 where 90 percent of the total variation between predictedand monitored total solids occurs.
if the data as calculated by an average Q/R is used for evaluation,and two listed events are dropped, then the model can be consideredverified for a Type I site. As further data becomes available, theremaining site types can be similarly verified.
MODEL LIMITATIONS
The ability of this predictive procedure to represent the runoffprocess from various highway systems will be determined in futureapplications. The predictive procedure or model is a tool forevaluating the effects of increased traffic, changes in the highwayconfiguration or other design aspects on the loading of solids orother constituents to the receiving water.
Because of the complex interactions of rainfall, runoff and trafficon a highway, the predictive model has certain limitations whichmust be recognized before its use can be fully evaluated. The mostobvious limitations are as follows:
1. The model assumes the highway area to be uniformlycharacterized by the three site types that are listed.Significant variations in a site may have widely varyingresul ts .
83
Table 32. Model results for Dallas 1-45, 1977.
Total sol ids, lb Susperided solids, lb COD, lb
Date Actual Predicted Actual Predicted Actua' I Predicted
6/15/77 56 23 10 9 39 10
6/23 23 74 2 36 27 20
7/9 55 79 19 38 27 21
7/21 172 309 112 161 157 68
7/26 62 65 26 31 38 19
7/27 49 13 24 4 20 8
8/14 25 15 7 5 21 8
8/28 26 48 4 23 16 15
Total rar 52T 20? W 345 169
Lead, lb I ron , lb
6/15/77 0.07 0.10 0.44 0.93
6/23 0.10 0.39 0.30 1.70
7/9 0.06 0.42 0.21 1.77
7/21 0.56 1.71 2.79 5.23
7/26 0.17 0.34 0.98 1.56
7/27 0.13 0.05 0.61 0.78
8/14 0.05 0.06 0.16 0.81
8/28 0.04
1 .18
0.25
3.32
0.11
5.60
1.32
Total 14.10
Metric units: lb x 0.454 = kg
84
CO (/>
9 i?-i
ii
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400 —
300 —
200 —
100
| MEASURED
I5 COMPUTED
6/15 6/23 7/9 7/21 7/26 7/27 8/18 8/28
1977 STORM EVENTS
Figure 2k. Measured versus computed total solids at Dallas \-k5, 1977.
85
o
1.90 -
1.70 -
1.50 -
1.30 -
~ 1.10</>
0.90 -
3 0.70 -
| MEASURED
^ COMPUTED
6/15 6/23 7/9 7/21 7/26 7/27 8/18 8/28
1977 STORM EVENTS
Figure 25. Measured versus computed lead at Dallas 1-45, 1977,
86
2. The predicted pounds of total solids washed off during a
rainfall event are dependent upon the model prediction ofthe surface load at the start of the storm. If the surfaceload is underestimated, the pounds discharged will be low.
3. The use of average runoff rate to remove surface pollutantsis the quickest and easiest method. Peak runoff intensitiesduring the runoff hydrograph may be more accurate, but aretoo complex for this procedure.
k. Long dry periods and overlapping storms present predictiveproblems in determining the prestorm surface load.
5. Construction activities are difficult to simulate unlessmonitoring data is available to determine K^ values.
87
SECTION V
RECEIVING WATER IMPACT
The intended use of the predictive procedure eventually may involve thedetermination of the impact of highway runoff on a receiving body of
water such as a lake or stream. The complex determination of theresulting water quality is beyond the scope of this analysis, however,the relative comparison of urban runoff to highway loads can be used
to evaluate the anticipated impact.
ANNUAL HIGHWAY LOADS
In order to compare the impact of highway runoff with other pollutantsources, the data collected from the monitoring program at the five
sites has been reduced to an average pounds (kg) of pollutant per acre(ha) of contributing area per inch (cm) of runoff. This data \s
summarized in Table 33 for nonwinter conditions at all sites. Theunit lb/ac-in. of runoff allows the comparison of different types ofdrainage areas with different perviousness and runoff characteristics.Other units of this table include concentrations in mg/1 and loadingsin pounds per acre (kg/ha).
In order to generate an annual load for the monitoring sites, the
weighted average annual rainfall volume for the sites was calculated as
approximately 32.5 inches (82.5 cm), and the percent of imperviousnessas a weighted average was approximately 34 percent. Using this data,approximately 12 to 14 inches (30-36 cm) of runoff will be generatedduring the nonwinter periods of the year over these sites on an
average basis. The average drainage area size is 43.5 acres (17.6 ha).
The data of Table 33 provides the Ib/acre-in. (kg/ha-cm) of runoffcolumn so that the annual load from the highway system can be expressedas a yearly load as shown in Table 34.
Table 34.. Yearly highway runoff loads.
Pollutant loading, Pollutant loading,
Parameter lb/acre- in. runoff 1 b/acre-yr
TS 260 3,380SS 59 767B0D
55.4 70.2
Pb 0.22 2.86Fe 2.3^ 30.4COD 33 429
metric units: lb/acre x 1.12 = kg/halb/acre/in. x 0.441 = kg/ha/cm
88
Table 33. Summary of highway runoff quality data forall six monitoring sites - nonwinter 1976-77.
Pol lutantconcentration, Pol lutant loading, Pollutant loading,
Parameter mg/1 Ib/ac-event lb/ac- in. runoff
TS 1.1*7 51.8 260
SS 261 14.0 59
VSS 77 3.7 17
TVS 242 9.4 55
B0D5
24 0.88 5.4
TOC 41 2.1 9.3
COD 147 6.9 33
TKN 2.9 0.15 0.68
TN 1.1 0.07 0.26
TPO^ 0.8 0.05 0.18
cr 386 13.0 88
Pb 0.96 0.06 0.22
Zn 0.41 0.02 0.04
Fe 10.30 0.5 2.34
Cu 0.10 .0056 0.02
Cd 0.04 .0017 0.009
Cr 0.04 .0028 0.009
Hg 3.22 x 10"3 5-9 x 10-7 7.3 x 10- 2*
Ni 0.02 0.27 2.25
metric units: lb/ac x 1.12 = kg/ha
lb/ac-in. x 0.441 = kg/ha-cm
89
The yearly loads listed above are generalized for 13 inches (33 cm) ofrunoff per year. Similar calculations can be made for any of theparameters listed in Table 33.
IMPACT EVALUATIONS
An early task of the subject project was to sample a small receivingstream (Pine River) near the Harrisburg I
— 8 1 site prior to, duringand after a rainfall event. The purpose of this effort was to quantifythe effects of the highway runoff on the stream. Samples of both theinstream quality and bottom sediment material were acquired upstreamand downstream of the highway runoff discharge point. Analysis of thesamples showed nearly uniform contamination of the bottom materialfor various parameters which were thought to be sensitive to highwayrunoff. Factors such as upstream sources of highway runoff, residual
effects from construction activities and overall contamination of thestream forced curtailment of further monitoring efforts. Appendix F
contains the results of this sampling effort. Future phases of FederalHighway Administration research will be directed toward the collectionof such data and the quantification of receiving water impacts.
Concentrations and loadings for each parameter are provided in this
section so the user of the predictive procedure can compare the magni-tude of various loads to a receiving body and then evaluate the impactof the highway. Table 33 allows for a comparison of highway loading
data to other pollutant sources affecting receiving water quality in
a number of different ways. The most common (but least accurate)method is comparison of highway runoff concentrations with urban runoff,
industrial, municipal, and nonpoint discharge concentrations.
In order to obtain a suitable average concentration which is represen-tative of the entire country, the data listed in the "Urban StormwaterManagement Technology" publication of EPA is used. This publicationlists average concentrations of stormwater for 15 cities with combinedsewer overflow (CSO) and II cities with storm sewer data. Table 35presents an average of these values for both CSO and stormwater.These values represent the average concentrations for the 11 and 15
cities presenting data.
Comparison of this data with the highway runoff data (Table 35) showthe storm sewer BOD and COD to be almost equal to highway concentra-tions. The CSO data are much higher in concentration as are thesuspended solids data for storm sewers. A simple comparison of con-centrations for these three sources to receiving water shows thehighway runoff constituents to have generally lower concentrationsfor these parameters. Comparisons such as these are inconclusive since
the sampling methods have a significant influence on the magnitude of
the concentrations. The highway data is a result of flow proportionedcomposite samples which reflect the entire discharge period. Other
90
BOD5
119
COD 265
Total suspendedsol ids 408
Table 35. Average concentrations for combined sewer,storm sewer and highway discharges.
Combined Storm water (15) , Highway,Parameter sewer (15), mg/1 mg/1 mg/1
27 2k
176 147
639 261
values may be the result of discrete samples taken during the first
flush portion of the overflow which would tend to overpredict thecomposite concentration, or discrete samples taken late in the runoff
period which would tend to underpredict the composite concentration.Many other factors enter into the comparison of concentrations. For
purposes of the receiving water impact, however, the runoff concentra-tion may not effect the receiving water. The pounds of pollutantdischarged is a more significant determination. For example, if
discharge A has half the concentration of discharge B, but A has twice
the total flow of B, then both discharges release equal loads to the
receiving body. Thus, concentration alone is meaningless unless some
estimate of flow is available to determine total load and resultingconcentrations in the receiving water body.
A more meaningful representation of the impact potential of various
discharges can be determined by expressing the loads on an annual
basis. This procedure accounts for variation in rainfall, flows and
concentrations of individual events by generating the annual loading.
To overcome these discrepancies the runoff or discharge data is
normalized to the pounds per acre per year values presented previously
in Table 34. A comparison of loads can now be made which better
represents the relative impact potential of highway runoff. Data
available from the literature can then be used to compare various
pollutant sources.
An EPA report (16) entitled, "Nationwide Evaluation of Combined Sewer
Overflows and Urban Stormwater Discharges, Volume III" presents
annual loads for municipal and urban sources which are listed in
Table 36. Since ultimate BOD values were not determined in this
project, a comparison of BOD values between these data sources requires
the transformation of BOD5 highway data to ultimate BOD. The ultimate
BOD value for highway runoff in Table 36 was obtained by assuming
BODc to be 70 percent of the ultimate.
Highway runoff values are significantly lower than published urban
runoff values in all categories listed. Similarities exist between
91
UltimateBOD
Suspendedsol ids COD
61 50 92
470 6690 938
15.7 186 88
Table 36. Annual loads for highway, municipal and urbanrunoff sources - lb/ac-yr.
Municipal effluent (16)
Urban runoff (16)
Highway runoff
metric units: lb/ac/yr x 1.12 = kg/ha/yr
municipal effluent and highway runoff data for ultimate BOD and COD.
CONCLUSIONS
The data presented in this section has been limited to BOD, COD and
suspended solids. These parameters are the only readily availabledata on urban runoff normally published as an annual load. For
purposes of determining receiving water impacts these parameters pro-vide an indication of the impact potential of highway runoff.
Parameters such as lead or other heavy metals, which are normally not
evaluated, may be more significant for highway runoff.
The brief data comparisons of this section indicate that highwayrunoff is similar in loading per unit area for the example parametersto storm sewer discharges but much less significant than combinedsewer loads. Urban runoff loads which contain both storm and combineddischarges exhibit much more impact potential for the three parameters
92
SECTION VI
CONCLUSIONS AND RECOMMENDATIONS
Equations were developed relating runoff volume to rainfall volumefor three highway site types as follows:
Type I - urban elevated bridge decks with 100% paved areas
0_ = 0.969 R - 0.019
Type II - urban paved and nonpaved drainage areasQ = 0.470 R 1 .369 dd -0.086
Type III - rural flush shouldered highways= 0.84'; Rl-892 nn -0.654Q = 0.845 R'-w dd
where; Q = runoff volume (inches)
R= rainfall volume (inches)DD = dry days to last storm event
2. Pollutant accumulation rates developed in this project are com-parable to other highway studies.
3. Pollutant accumulation rates are related to traffic using thefollowing equation;
Kt = 0.007 (ADT0.89)
where; K-| = pollutant accumulation rate (lb/mi-day)ADT = average daily traffic (vehicles/day)
4. Total solids showed the highest correlation with other monitoredparameters and is therefore used as the carrier pollutant in the
predictive procedure to estimate 16 other parameters.
5. The wash-off coefficient (K2) used in many surface water qualitymodels and used in this procedure can be written as:
P - P (l-e"K2 r
)
where; P = pounds washed-offPQ = pounds at start of rainfall event
K2 = wash-off coefficientr average runoff rate, in./hr
The wash-off coefficient developed for the three highway site
types used in this study are:
K£ 5.0 for urban elevated bridge decks with 100 percent
paved areas (Type I)
l<2 = 6.5 for urban paved and nonpaved drainage areas (Type II)
K2 = 12.0 for rural flush shouldered highways (Type III)
93
6. The predictive model is better suited to continuous simulationsusing daily rainfall records covering periods of at least onemonth than design events, due to its inability to predict surfaceloads prior to individual events.
7. Highway runoff shows less impact potential than urban stormwateras measured by BOD and suspended solids.
RECOMMENDATIONS
1. Verification of the predictive procedure should be continuedusing data from other sites.
2. Measure the actual pollutant buildup on highway surfaces to
determine the relationships between site characteristics andaccumulation rates.
3. Investigate the effects of highway runoff on receiving waters in
a separate, comprehensive study.
k. Investigate the pollutant characteristics of nonpaved runoff forvarious vegetative and side-slope configurations. Additionalhydraulic information from nonpaved areas could lead to thedevelopment of a more sophisticated procedure to predict total
runoff volume in which paved and nonpaved runoff are estimatedseparately.
3k
SECTION VI I
REFERENCES
1. Gupta, M. K. , Agnew, R. W., Gruber, D. A., and Kreutzberger , W.
,
"Characteristics of Runoff from Operating Highways, Research
Report", Volume IV of FHWA Contract No. DOT-FH-11-8600, with
Envirex Inc., Environmental Sciences Division, Milwaukee, Wl
,
July, 1978.
2. Rohlf, F. J., and Sokal , R. R. , "Statistical Tables", W. H. Freemanand Company, San Francisco, 1969.
3. Abbott, J., "Guidelines for Calibration and Application of theUrban Storm Water Runoff Program (STORM)", Preliminary Report:The Hydrological Engineering Center, Davis, California, March,
1976.
k. "Water Pollution Aspects of Urban Runoff", American Public WorksAssoc, FWPCA, Water Pollution Control Research Series WP-20-15,January, 1969.
5. "Standard Methods for the Examination of Water and Wastewater",Fourteenth Edition, APHA-AWWA-WPCF, 1975.
6. Brandstetter , A., "Assessment of Mathematical Models for Stormand Combined Sewer Management", Environmental ProtectionTechnology Series, EPA-600/2-76-175a , 1976.
7. Burgess, S., Horner, R., and Waddle, T., "Review of the Literatureon Water Quality Impacts of Highway Operations and Maintenance",A Study Prepared for the Washington State Highway DepartmentRunoff Water Quality Research Project, 1977-
8. Sartor, J. D., and Boyd, G. B. , "Water Pollution Aspects of StreetSurface Contaminants", Report to U.S. Environmental ProtectionAgency, 1972.
9. Sylvester, R. 0., and DeWalle, F. B., "Character and Significanceof Highway Runoff Waters", Report to Washington State HighwayCommission, 1972.
10. Shaheen, D. G., "Contribution of Urban Roadway Usage to WaterPollution", Report to U.S. Environmental Protection Agency, 1975.
11. "Freeway Runoff from the I -90 Corridor", Municipal i ty ofMetropolitan Seattle, Report to Washington State Highway Commission,
1973.
12. "Water Quality Investigations Along SR 90, Phase IV, Municipalityof Metropolitan Seattle, Report to Washington State HighwayCommissions, 1973-
95
13. Moe, R. D., Bullln, J. S., Polasek, J. C„ Mlculka, J. P., andLougheed , M. J., "Characteristics of Highway Runoff In Texas",Preliminary Draft Prepared for: U.S. Department of Transporta-tion, Federal Highway Administration, Office of Research and:
Development, 1 977
•
14. Hub.er, W. C, Heaney, J. P., Medina, M. A., Peltz, W. A.,
Sheikh, H., and Smith, G. F., "Storm Water Management Model
User's Manual Version II", EPA-670/2-75-017, 1975-
15. "Urban Stormwater Management and Technology: An Assessment",Lager and Smith, EPA 670/2-7^-0^0, December 197^-
16. "Nationwide Evaluation of CSO and Urban Stormwater", Manningand Sullivan, EPA 600/2-77"064c, August 1977.
96
SECTION VI I I
GLOSSARY
Carrier pollutantPollutant exhibiting the highest degree of association with all
other pollutants; used in the model to estimate the quantity ofall other quality parameters.
CSOCombined sewer overflow.
Dry daysThe number of days in which less than 0.05 inches of rain fell
prior to the event under consideration.
Highway lengthThe actual length of highway section in the contributing drainagearea; this is in contrast to curb mile or lane mile, which is a
multiple of the highway length.
JiLThe variable used to represent the rate at which surface pollutantsaccumulate, expressed as pounds per mile - day.
The constant used in the wash-off equation which removed thesurface pollutant load; the wash-off coefficient.
NonpavedThat portion of the highway drainage area that is partially orcompletely pervious.
PavedThat portion of the highway drainage area composed of concreteor asphalt traffic lanes, exit ramps or shoulder areas.
Pre-stormThat period of time prior to the rainfall event under considerationwhich contains the pollutant build-up period, measured in days.
97
Q/RRunoff (Q) to rainfall (R) ratio; relates the total volume of runofffrom both the paved and nonpaved portions of the drainage area to
the amount of rain causing the runoff.
Type I
Urban highway site, elevated bridge deck, 100 percent paved with impactbarriers containing each set of lanes.
Type i
I
Urban highway site, mountable curb with paved and nonpaved drainagearea.
Type 1 I I
Rural highway site with flush shoulders, paved and nonpaved runoffthrough ditches.
98
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APPENDIX E
MODEL VARIABLES AND DESCRIPTION
VARIABLE LIST AND DESCRIPTIONVariable
name Description
ACRES Area of siite in acres.
ADT Average daily traffic.
BBOD Value for BOD5 (mg/1)
BOD Value for BOD5 (pounds)
BCD Value for Cadmium (mg/1)
BCL Value for Chlorides (mg/1)
BCOD Value for COD (mg/1)
BCR Value for Chromium (mg/1)
BCU Value for Copper (mg/1)
BFE Value for Iron (mg/1)
BPB Value for Lead (mg/1)
BSS Value for Suspended Solids (mg/1)
BTKN Value for total KJELDAHL Nitrogen (mg/1)
BTN Value for total N02 + NO3 (mg/1)
BTOC Value for Total Organic Carbon (mg/1)
BTPO Value for Total Phosphorus (mg/1)
BTVS Value for Total Volatile Solids (mg/1)
BVSS Value for Volatile Suspended Solids (mg/1)
BZN Value for Zinc (mg/1)
CD Value for Cadmium (pounds)
CL Value for Chlorides (pounds)
COD Value for COD (pounds)
CR Value for Chromium (pounds)
CU Value for Copper
DDAYS Dry Days
FE Value for Iron (pounds)
ITYPE
I MOI DAY
I YEAR
Type of site (Value of 1, 2 or 3)
Month of rainfall eventDay of rainfall eventYear of rainfall event
continued
136
Variablename
APPENDIX E
MODEL VARIABLES AND DESCRIPTION (continued)
VARIABLE LIST AND DESCRIPTION
Description
Kl IN
Kl
K2
User supplied K^ (pounds of total solids/mile-day)Accumulation rate, (pounds of total solids/mile-day)Washoff constant depending upon I TYPE
LENGTHNUM
Length of site in milesNumber of rainfall events to be analyzed
PB
PENIT
PINITPOLBUP
PWSHOF
Value for Lead (pounds)
Initial total solids load or remainder fromprevious washoff
Initial total solids load
Total solids buildup from the equation P=PQ +
(Kj x HLxT)
Total solids washoff
R
RAINRDURATREMA
ROFFCFROFFIN
Average runoff rateRainfall in inches
Rainfall duration in hours
Total solids remaining (total solids buildup -
total solids washoff)Runoff in cubic feetRunoff in inches
SS Suspended solids (pounds)
TITLETKNTNTOCTOTRANTPOTPWSHF
TRUNCFTRUNINTVS
Title of simulation (user input)
Total KJELDAHL Nitrogen (pounds)
Total Nitrogen (pounds)
Total Organic Carbon (pounds)
Total rainfall (inches) for the entire record
Total Phosphorus (pounds)
Total solids washed off (pounds) for the entire
recordTotal runoff (cubic feet) for the entire record
Total runoff (inches) for the entire record
Total Volatile Solids (pounds)
continued
137
APPENDIX E
MODEL VARIABLES AND DESCRIPTION (continued)
VARIABLE LIST AND DESCRIPTIONVariable
name Description
VSS Volatile Suspended Solids (pounds)
XDD Dry day value used to calculate total runoff
XYD Dry day value used to calculate pollutant build-up
Y1 Runoff Duration (hours)
ZN Value for Zinc (pounds)
138
APPENDIX F
RECEIVING WATER IMPACT SAMPLING
PURPOSE
The sampling effort carried out at the Harrisburg 1-81 site receivingstream (Pine River) was intended to provide a preliminary overview ofthe existing instream conditions before and after a runoff event. Theresults of this sampling effort provide direction for the receivingmodel and data needs for future sampling efforts.
Sampling of the receiving stream on July 1, 1976 included a prestormand poststorm effort at the same locations to quantify changes in
biological, chemical or physical stream characteristics. Locationsof the sampling points are approximately 50 ft (15.2 m) upstream ofwhere the highway runoff channel entered the stream and 150 ft (45.7rn) downstream. Runoff from the highway discharges to the streamthrough an unlined channel and appeared to be completely mixed by thetime it reached the downstream sampling site.
Prestorm (dry weather) samples were taken approximately two hoursbefore the start of the July 7th rainfall which produced 0.49 inches(1.24 cm) of rain in 30 minutes. Four days of dry weather preceededthis storm, insuring no highway runoff loads were contributed to thestream during the prestorm monitoring. The flow within the streamduring this sampling interval was 0.2 cfs (0.006 m3/sec) as comparedto a high of 9-0 cfs (0.25 m3/sec) during the wet weather sampling.
RESULTS
Samples of the stream and bottom materials were acquired on a grabsample basis during the prestorm period in order to determine theeffects of the single discharge point of highway runoff. Sedimentsamples were taken at upstream and downstream locations to determineif highway runoff altered the chemical or physical properties ofbottom sediments at the downstream location as compared to upstream.Table F1 presents the bulk analyses for these sediment samples. Dryweather samples show little difference between upstream and downstreamlocations. Wet weather samples show more significant variation betweenlocations for certain parameters. Comparison of the analyses for the
upstream site between dry and wet weather shows significant changefor oils, total phosphorous and COD. Iron concentrations were high
at all locations, however, iron is a regular component of surfacewater in that area.
The general conclusion regarding sediment analysis is: no significantimpact at this sampling site can be determined. Long term build-upof metals or oils and grease on the downstream portions have not
been seen nor can any impact be related to the small volume of highway
runoff present.
139
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weather grab samples were taken to provide background concentrations.During the runoff period of the July 7th storm, grab samples were againtaken at the same locations. Similar samples were also taken afterthe surface runoff and high in-stream flows had subsided. These sampleswere used to determine possible decrease of in-stream concentrationsafter the runoff had subsided. Table F-2 shows the analysis resultsfor these grab samples. Again, little variation is encountered betweensites or samples except for the noticeable increase in BOD after the
storm. The relatively low concentrations of BOD and the increase in
concentrations upstream of the discharge, however, negate furtherinvestigation as to the source or impact of this load.
Table F-3 presents the results of the in-stream samples taken during
the dry and wet weather monitoring periods. Also included are compo-site sample results from the highway runoff on July 7th. On a simpleconcentration basis, the relative quality between upstream and down-
stream locations is consistent. Mass balance analysis of wet weatherdata, however, is required in order to determine the relative magni-
tude of the loads in the system.
Mass balance analysis using the wet weather data was calculated withstage measurements at a downstream gage and the highway runoff values.
Although detailed (multiple sample) composites of the stream flow were
not taken, the following mass loadings using the rough composites will
provide some insight into the relative contribution of the highway
runoff.
1. Highway runoff flow data 7/7/76- duration of overflow 150 minutes- average flow 1.13 cfs (0.032 m3/sec)- total flow to river 10,145 ft3 (287-3 m3)
2. Downstream receiving streamflow
- duration of sampling 150 minutes- average stage reading 2.165 feet (0.660 m)
- average flow 7.25 cfs (0.205 m3/sec)- total flow 65,235 ft3 (1,847.5 m3)
3. Upstream receiving streamflow
- assume downstream flow - highway = upstream- 65,235 - 10,145 = 55,090 ft3 (1,560.1 m2)
Using Table F-3, the kilogram loadings of Table F-4 are, derived by
multiplying total flow times composite quality (wet weather values).
Columns 1, 2 and 3 of Table F-4 present the measured kilograms of each
parameter from the instream and highway runoff composites. Column 4 is
141
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the sum of columns 1 and 2 which should be approximately equal to the
downstream load of column 3 if the system is relatively free of otherwithdrawals or loads. Column 5 is the difference between columns k
and 5 while column 6 is the percent change.
The results presented in Table F-k indicate an extremely wide variationin loads. Suspended and volatile solids loads indicate either a largeamount of scour within the stream or lateral inflows while t°tal solids
indicates settling or outflows between sampling points. Only TOCshows a fairly reasonable consistency. The monitoring sites werechecked to insure no other in-flows or branches in the stream occurred.The only conclusion to be drawn from the data is that the upstreamloads are all less than the downstream loads. However, the highwayrunoff contribution alone is not of sufficient magnitude to evenapproach a reasonable "mass balance". Other types of monitoringincluding benthos description were attempted but the results were alsoinconclusive.
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APPENDIX H
FLOW, TOTAL RAIN AND DRY DAYS SCALLERGRAM FOR TYPE II SITES
Scatter plots are presented in this appendix to:
1. Show the tendency of the Nashville dry day and rainfall
data to cluster.
2. Show why the Nashville data tended to skew the Type II
equation for predicting runoff volume with total rain anddry days since the last storm event.
The scatter plots in this appendix (Figures H-l to H-k) present all
log transformed data using log scales on both the X-axis and Y-axis.Data points on each graph are represented by the letter "A" for
single data points and by the letter "B" if two values fall on the
same data point. These scatter plots were produced using thescatter plot option of the Statistical Analysis System Program (SAS)
during regression analyses of monitored data.
Figure H-1 shows the scatter plot of Nashville dry day and flow data.
The data form two distinct clusters. One cluster forms around the
X-axis and represents the numerous data points for rainfall eventswhich occur after no dry days. The second cluster which appears in
the center of the plot represents rainfall events occurring after a
dry day period. This scatter plot shows the atypical rainfall patternof Nashville as compared to the Milwaukee and Denver sites. At
Nashville a dry period consisting of 5 to 17 dry days was normallyfollowed by a rain period lasting 3 to 6 days. Data collected at theMilwaukee and Denver sites did not consistently exhibit theseextended rainfall periods.
Figure H-2 which plots the Nashville rain and flow data shows a strongrelationship between these two parameters. Figure H-3 also displays a
strong relationship when these same two parameters are plotted for
the combined Milwaukee Hwy. 45 and Denver data. However, when thedata from all three sites is plotted (Figure H-k) , the strong relation-ship observed on the previous two plots is lost due to a skewingof the data to the lower portion of the scatter.
The scatter plot analyses explain, at least partially, why thesignificance of a Type II equation predicting runoff volume from total
rainfall and dry days decreased when the Nashville data was combinedwith the Milwaukee Hwy. k$ and Denver data.
193
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FEDERALLY COORDINATED PROGRAM (FCP) OF HIGHWAYRESEARCH AND DEVELOPMENT
The Offices of Research and Development (R&D) of
the Federal Highway Administration (FHWA) are
responsible for a broad program of staff and contract
research and development and a Federal-aid
program, conducted by or through the State highway
transportation agencies, that includes the Highway
Planning and Research (HP&R) program and the
National Cooperative Highway Research Program
(NCHRP) managed by the Transportation Research
Board. The FCP is a carefully selected group of proj-
ects that uses research and development resources to
obtain timely solutions to urgent national highway
engineering problems.*
The diagonal double stripe on the cover of this report
represents a highway and is color-coded to identify
the FCP category that the report falls under. A red
stripe is used for category 1, dark blue for category 2,
light blue for category 3, brown for category 4, gray
for category 5, green for categories 6 and 7, and an
orange stripe identifies category 0.
FCP Category Descriptions
1. Improved Highway Design and Operation
for Safety
Safety R&D addresses problems associated with
the responsibilities of the FHWA under the
Highway Safety Act and includes investigation of
appropriate design standards, roadside hardware,
signing, and physical and scientific data for the
formulation of improved safety regulations.
2. Reduction of Traffic Congestion, andImproved Operational Efficiency
Traffic R&D is concerned with increasing the
operational efficiency of existing highways by
advancing technology, by improving designs for
existing as well as new facilities, and by balancing
the demand-capacity relationship through traffic
management techniques such as bus and carpool
preferential treatment, motorist information, and
rerouting of traffic.
3. Environmental Considerations in HighwayDesign, Location, Construction, and Opera-
tion
Environmental R&D is directed toward identify-
ing and evaluating highway elements that affect
• The complete seven-volume official statement of the FCP is available from
the National Technical Information Service, Springfield, Va. 22161. Single
copies of the introductory volume are available without charge from Program
Analysis (HRD-3), Offices of Research and Development, Federal Highway
Administration, Washington, D.C. 20590.
the quality of the human environment. The goals
are reduction of adverse highway and traffic
impacts, and protection and enhancement of the
environment.
4. Improved Materials Utilization andDurability
Materials R&D is concerned with expanding the
knowledge and technology of materials properties,
using available natural materials, improving struc-
tural foundation materials, recycling highway
materials, converting industrial wastes into useful
highway products, developing extender or
substitute materials for those in short supply, and
developing more rapid and reliable testing
procedures. The goals are lower highway con-
struction costs and extended maintenance-free
operation.
5. Improved Design to Reduce Costs, Extend
Life Expectancy, and Insure Structural
Safety
Structural R&D is concerned with furthering the
latest technological advances in structural and
hydraulic designs, fabrication processes, and
construction techniques to provide safe, efficient
highways at reasonable costs.
6. Improved Technology for HighwayConstruction
This category is concerned with the research,
development, and implementation of highway
construction technology to increase productivity,
reduce energy consumption, conserve dwindling
resources, and reduce costs while improving the
quality and methods of construction.
7. Improved Technology for HighwayMaintenance
This category addresses problems in preserving
the Nation's highways and includes activities in
physical maintenance, traffic services, manage-
ment, and equipment. The goal is to maximize
operational efficiency and safety to the traveling
public while conserving resources.
0. Other New Studies
This category, not included in the seven-volume
official statement of the FCP, is concerned with
HP&R and NCHRP studies not specifically related
to FCP projects. These studies involve R&Dsupport of other FHWA program office research.
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