Final Report Area Ten

8/6/2019 Final Report Area Ten

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THE UNIVERSITYOF BIRMINGHAM

SCHOOL OF ENGINEERING

CIVIL ENGINEERING

REPORT PREPARED AS A PART OF THE INDUSTRIAL

PROJECT

ON THE M.Sc. WATER RESOURCES TECHNOLOGY AND

MANAGEMENT COURSE

BY

Junaid Patel

FOR

Atkins Consultants Ltd.

The Impact of Highway Runoff

on Water Quality Downstream of a

Highway Outfall

September 2004

The material in this report was prepared as part of the M.Sc. course in Water Resources

Technology and Management and should not be published without the permission of

The University of Birmingham and Atkins Consultants Ltd. The University of

Birmingham accepts no responsibility for the statements made in this document.


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Industrial Project Table of Contents

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Table of Contents

GLOSSARY OF TERMS__________________________________________ 4

EXECUTIVE SUMMARY__________________________________________ 5

1. INTRODUCTION ____________________________________________ 9

2. AIMS AND OBJECTIVES ____________________________________ 11

3. LITERATURE REVIEW ______________________________________ 12

3.1. CONSTITUENTS OF POLLUTANTS IN HIGHWAY RUNOFF______________ 12

3.1.1. Sediments _________________________________________ 12

3.1.2. Hydrocarbons_______________________________________ 12

3.1.3. Metals ____________________________________________12

3.1.4. Other Compounds ___________________________________ 13

3.2. SOURCES OF POLLUTANTS IN HIGHWAY RUNOFF__________________ 14

3.2.1. Vehicles ___________________________________________ 14

3.2.2. Existing Installations__________________________________14

3.2.3. Atmospheric deposition _______________________________ 14

3.2.4. Accidental Spillage___________________________________ 15

3.2.5. Highway Maintenance ________________________________ 15

3.3. FACTORS AFFECTING HIGHWAY RUNOFF QUALITY_________________ 16

3.3.1. Traffic Flow (AADT) __________________________________16

3.3.2. Climate and Local Land Use ___________________________16

3.3.3. Precipitations Characteristics___________________________ 17

3.4. EFFECTS OF HIGHWAY RUNOFF ______________________________ 18

3.4.1. Sediments _________________________________________ 19

3.4.2. Metals ____________________________________________19

3.4.3. Hydrocarbons_______________________________________ 19

3.4.4. Other Compounds ___________________________________ 20

3.5. CONTROL OF HIGHWAY RUNOFF______________________________ 21

3.5.1. Source Control ______________________________________21

3.5.2. Sustainable (urban) Drainage Systems (SuDS)_____________ 21

3.6. CURRENT LEGISLATION AND PRACTICE_________________________ 22

3.6.1. Environment Agency and Water Quality Assessment ________ 22

3.6.2. Highways Agency Environmental Assessment _____________ 23


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Industrial Project Table of Contents

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4. METHODOLOGY___________________________________________ 24

4.1. TASK 1:BUILDING THE DATABASE_____________________________ 24

4.1.1. Data Manipulation ___________________________________ 26

4.1.2. Effect of Traffic Flow >30,000 on Pollutant Loadings_________ 28

4.2. TASK 2:RELEVANT ENVIRONMENTAL QUALITY STANDARDS __________29

4.2.1. Upstream Concentrations _____________________________ 31

4.3. TASK 3:ACCOUNTING FOR DISSOLVED OXYGEN __________________ 33

4.3.1. Streeter-Phelps Equation______________________________ 35

4.4. TASK 4:APPLYING THE METHODOLOGY_________________________ 39

4.4.1. DMRB Examples ____________________________________ 39

4.4.2. Area 10 Examples ___________________________________ 434.4.3. Sensitivity Analysis___________________________________50

5. CRITICAL REVIEW OF DMRB ASSESSMENT ___________________ 56

5.1. CURRENT ASSESSMENT____________________________________56

5.2. PROPOSED ASSESSMENT___________________________________57

5.2.1. Rainfall Characteristics and Antecedent Dry Period__________ 57

5.2.2. Pollutant Build up and Wash Off ________________________ 59

5.2.3. Relationship between Traffic Flow and Pollutant Loading _____61

5.2.4. Event Mean Concentrations (EMCs) _____________________63

6. CONCLUSIONS____________________________________________ 64

7. RECOMMENDATIONS ______________________________________ 66

8. ACKNOWLEDGEMENTS ____________________________________ 68

9. REFERENCES_____________________________________________ 69

10. APPENDIX________________________________________________ 73


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Industrial Project Glossary of Terms

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Glossary of Terms

Area 10 Highways Agency region in the North-West

ADP Antecedent Dry Period

%ile Percentile

AADT Annual Average Daily Traffic

BOD Biochemical Oxygen Demand

COD Chemical Oxygen Demand

CSO Combined Sewer Overflow

Cb,d,RO Concentration background, downstream and

runoff respectively

DMRB Design Manual for Roads and Bridges

DO Dissolved Oxygen

DWS Drinking water standard

EA Environment Agency

EMC Event Mean Concentration

EQO Environmental Quality Objective

EQS Environmental Quality Standard

FIS Fundamental Intermittent Standards

GQA General Quality Assessment

NH3 Un-ionised ammonia

NH4+ Ionised ammonia/ammonium

NH4-N Ammoniacal Nitrogen

RE River Ecology

RO Runoff

RQO River Quality ObjectivesSuDS Sustainable urban Drainage Systems

UID Unsatisfactory Intermittent Discharge


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Industrial Project Executive Summary

5

Executive Summary

Introduction

It has been recognised that runoff from roads can have a negative impact on

the water environment. The Highways Agency has a right to discharge runoff

from roads to watercourses, but not do not have the right to pollute them. The

Environment Agency has a statutory duty to protect and monitor water quality.

The current state of knowledge in predicting the polluting potential of highway

runoff in the UK is summarised in CIRIA report 142 (1994). This has been

reproduced in the Design Manual for Roads and Bridges (DMRB 11.3.10) which

recommends a three stage approach for the environmental assessment. The

intermediate stage involves calculating spillage risk and the water qualitydownstream of the discharge for copper and zinc. The methodology centres on

the loading rates of pollutants compiled in CIRIA report 142 from studies

performed in the 70s and 80s. The loading rates are used in the methodology to

perform a mass balance to provide the concentration of a specific determinand

downstream of a highway outfall.

Aims and Objectives

The aim of this project is to expand the assessment of pollutants in highwayrunoff as recommended in the DMRB (11.3.10) stage 2 assessment of routine

pollutants for surface waters. Much of this refinement centres on Table 5.1 (pg

107, CIRIA, 1994) which relates unit loading (kg/ha/yr) of a specific determinand

to traffic flow (annual average daily traffic, AADT). The DMRB assessment is

currently based on zinc and copper, determinands thought to occur in

dangerous (excessive) concentrations in highway runoff.

To extend the DMRB assessment, consideration of the following additionalparameters has been suggested:

Other heavy metals such as cadmium (Cd), chromium (Cr), cobalt

(Co), iron (Fe), lead (Pb), manganese (Mn), and nickel (Ni).

Other compounds such as: Total Suspended Solids, COD, NH4-N

and hydrocarbons.

Dissolved Oxygen (DO).

More precise understanding of loading rates for determinands whereAADT >30,000.


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Methodology and Key Results

Table 5.1 from CIRIA report 142 has been successfully expanded to

accommodate the additional determinands except for manganese and cobalt.

Loading rates have been derived from current literature (post CIRIA 142, 1994);

preliminary values achieved are encouraging, as corresponding values occur

elsewhere in the literature. The CIRIA methodology has been implemented

using the new loading rates and corresponding EQSs to identify priority

pollutants. A summary of key results implementing the expanded methodology

is given in the table below for DMRB examples sites in the North West (Area

10).

Ratio of downstream concentration to corresponding EQS

DMRBExamples

Highways Agency Area 10 examples

Determinand 1 2 3

LangwoodBrook

A 56, NearHaslingden

RiverMersey

M60 Jn5

WorsleyBrook

M60 Jn12

RiverBoyd

M4 Jn18

Lead 1.15 4.41 2.62 0.54 0.50 0.47 3.32

Chromium 0.48 1.66 1.10 0.50 0.50 0.36 0.68

Nickel 0.42 0.36 0.38 0.50 0.50 0.35 0.20

Cadmium 0.50 0.56 0.49 0.50 0.50 0.41 0.30

Iron 0.75 1.66 1.86 0.51 0.50 0.71 1.21

TotalSuspendedSolids

2.12 4.86 2.88 0.54 0.53 2.22 3.67

COD 1.51 4.31 2.57 0.52 0.50 0.66 3.25

BOD 1.09 2.23 1.42 0.51 0.50 0.73 1.65

NH4-N 1.15 3.34 2.03 0.50 0.50 0.39 2.5

Hydro-carbons

4.19 11.74 6.69 0.61 0.51 1.27 8.94

Table 1: Summary table indicating EQS failure for a number of examples. Red shadingindicates failure. Failure is defined as exceedance of the corresponding EQS.

The CIRIA methodology has been applied in order to evaluate the DO

concentration downstream of an outfall. In addition a novel method of evaluating

DO downstream has been implemented accounting for the time varying natureand dependency on BOD of DO.


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Conclusions

In this study the following determinands have been identified as priority

pollutants, i.e. those which occur in concentrations in highway runoff which

consistently exceed thresholds set for fisheries protection. In descending order

of priority:

1. Hydrocarbons

2. Total Suspended Solids

3. COD

4. NH4-N

5. BOD

6. Iron

Similarly, for the hardness related pollutants Lead and Chromium have

been found to occur in excessive concentrations.

Nickel and cadmium have been shown to occur in concentrations which

do not exceed corresponding EQSs.

These preliminary findings indicate that the DMRB water quality assessment

should be expanded to accommodate these additional determinands.

The most significant factor influencing downstream failure of EQSs resulting

from outfall discharge has been shown to be the size of the road catchment in

comparison to the flow in the receiving water.

The CIRIA methodology has been shown to be inappropriate for the evaluation

of DO downstream of an outfall. A novel method for the evaluation of DO

downstream of an outfall (which accounts for the time variant nature of DO and

its dependency on BOD) has been presented and implemented successfully.

In this study, a linear relationship between pollutant loading rates and traffic flow

(AADT) has been assumed for all determinands in order to follow the CIRIA

methodology. However, current literature suggests that the relationship between

pollutant loading and traffic flow is a tenuous one. It is suggested that each

determinand has a varying dependence on traffic flow and this should be taken

into account in subsequent investigations.


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Recommendations

In this study preliminary results for pollutant loading rates have been presented

based on world wide studies. More data is required in the highway runoff

database specifically for the following determinands: manganese, cobalt, nickel

and iron. In particular, data is required for UK studies as climate, region, and

land use have been shown to affect pollutant loading rates.

EQSs for fisheries protection have been used for all determinands except for

hydrocarbons where a DWS has been used. This must be updated in order for

the assessment to be consistent. Furthermore, to expand the use of EQSs it is

suggested that a tiered approach is implemented in the DMRB stage 2

assessment so that a river is not only defined by its RE class but also its

drinking water category and habitats category.

Additional refinements of the CIRIA methodology have been suggested

regarding:

Rainfall characteristics

Pollutant build-up

Relationship between Traffic Flow and Pollutant Loading

Event Mean Concentrations (EMCs)

It is appreciated that although some suggestions can be incorporated into CIRIA

methodology others may lead to a departure from the CIRIA methodology.

Highway discharge can result in high-load, short-duration events, which can

have a disproportionate impact on river ecology. Highway runoff has therefore

been discussed in terms of unsatisfactory intermittent discharges (UIDs)

because of the similarities with other UIDs (e.g. CSOs). An integrated approach

for all UIDs has been suggested however it is appreciated that such an

approach would lead to a departure from the CIRIA methodology.


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Industrial Project Introduction

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1. Introduction

It has been recognised that runoff from roads can have a negative impact on

the water environment (CIRIA Report 142, 1994). The Environment Agency has

a statutory duty for the protection and monitoring of water quality in England

and Wales. Pollution control is implemented by means of discharge consents

which dictate the quality and quantity of a specific discharge. Discharge

consents are not normally applied to highway drainage (Highways Act, 1980),

however they may be applied in exceptional circumstances for a site specific

discharge.

The current state of knowledge in predicting the polluting potential of highwayrunoff in the UK is summarised in CIRIA report 142. This has been reproduced

in the Design Manual for Roads and Bridges (DMRB 11.3.10) which

recommends a three stage approach for the environmental assessment for the

routine runoff of a new highway. The intermediate stage of assessment involves

calculating the probability of spillage and water quality downstream of the

discharge. The methodology centres on the loading rates of pollutants compiled

in CIRIA 142 (table 5.1: Typical pollutant build up rates, pg 107, 1994) fromstudies performed in the late 70s to the early 90s. Table 5.1 (reproduced below)

presents loading rates which vary with traffic flow for various pollutants.

Pollutant load (kg/ha/yr)

Traffic Flow

(AADT)

Total

Solids

COD

(kg O2)NH4-N Copper Zinc

Total Soluble Total Soluble

30000 10 000 700 4.0 3.0 1.2 5.0 2.5

Table 2: Table 5.1: typical pollutant build up rates(pg 107, CIRIA 142)


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Industrial Project Introduction

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The loading rates are used to perform a mass balance (DMRB 11.3.10 Annex

III) to calculate the concentration of a specific determinand downstream. This

calculation is currently only performed for copper and zinc, as these metals are

considered potentially ecologically significant at the range of concentration

present in runoff. The details of the information required (and the sources) to

perform the calculation are given below. Once the downstream concentrations

are calculated they are compared to the River Ecosystem (RE) Classification

(HMSO, 1993) thresholds to observe whether or not the downstream

concentrations have exceeded the RE values. If the RE values have been

exceeded a more detailed water quality assessment is required.

Figure1: Information required to calculate downstream concentration of pollutant.

By implementing a mass balance calculation, this solution ignores the time

variant nature of the actual event. This method has been implemented to

represent the worst case scenario resulting from the introduction of a highway

outfall. This method therefore requires a number of initial (simplifying)

assumptions with respect to the:

Precipitation characteristics.

The upstream flow (duration) and concentration.

The pollutant build up rate.

The stage 2 assessment is therefore intended to be an estimate of the worse

case scenario that may arise from the introduction of a new highway with outfall,

or retrofitting an outfall to an old highway.

Flow (95 %ile)

(Q, m3/s m3/d)

(Regulator)

Upstream Concentration (Cb)

(k, mg/l kg/ m3)

(Regulator)

Runoff = Rainfall x

Percentage runoff x

Catchment area

(Q, m3/d)

(Wallingford

Pollutant Loading

(Table 5.1) (M, kg)

Flowd= Flow+ Runoff

(Q, m3/d)

Downstream

Concentration (Cd)

(kg/ m3 mg/l)

(Calculated)


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Industrial Project Aims and Objectives

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2. Aims and Objectives

The aim of this project is to refine the assessment of pollutants in highway

runoff as recommended in the DMRB (11.3.10) Stage 2 assessment of routine

pollutants for surface waters. Much of this refinement centres on Table 5.1 (pg

107, CIRIA, 1994) which relates unit loading (kg/ha/yr) of a specific determinand

to traffic density (AADT). The DMRB assessment is currently based on zinc and

copper concentrations downstream of highway outfalls.

To extend the DMRB assessment, consideration of the following additional

parameters has been suggested:

Other heavy metals such as cadmium (Cd), chromium (Cr), cobalt

(Co), iron (Fe), lead (Pb), manganese (Mn), and nickel (Ni).

Other compounds such as: Total Suspended Solids, COD, NH4-N

and hydrocarbons.

Dissolved Oxygen.

More precise understanding of loading rates for determinands where

AADT >30,000.

To perform this extension of the current protocol the following tasks must be

completed:

1. A database must be compiled from recent studies (post CIRIA 142, 1994)

to include the above mentioned additional determinand loading rates with

corresponding traffic flows.

2. A database must be compiled to introduce relevant threshold values or

equivalent environmental quality standards (EQS) for the above mentioned

determinands. This will involve investigating standards beyond the current

remit of the river ecosystem (RE) classification.

3. A methodology to account for dissolved oxygen must be produced. This

may require investigation into current practice worldwide, or implementation

of a novel method which is of suitable complexity for the DMRB (Stage 2)

assessment.

4. The revised methodology will be implemented on specific outfalls in the

Highways Agency Area 10 (North West). The findings will be discussed andrecommendations on the refinements are to be made.


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Industrial Project Literature Review

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3. Literature Review

3.1. Constituents of Pollutants in Highway Runoff

A vast number of specific pollutants occur within highway runoff, the followingcategories have been devised to classify the pollutants into groups which shall

be referred to in this report.

3.1.1. Sediments

Sediments or settleable solids occur in highway runoff. They are often

categorised by particle size into coarse and fines (> or < 63m respectively).

Highway sediments consist predominantly of fines which can further subdivided

into clay, organic and inert material (Pontier et al, 2004). These fines play an

important role in transport processes for hydrocarbons and metals, often

referred to as particulate-bound metal elements PME (Yuan et al, 2001).

3.1.2. Hydrocarbons

Thousands of hydrocarbon species occur in highway runoff, each with different

properties and toxicities therefore some authors prefer the category name oils

and grease (Mitchell et al, 2000). Hydrocarbons are a reference to organic

compounds containing only carbon and hydrogen derived from the

petrochemical industry (CIRIA 142, 1994). Approximately 70% of hydrocarbons

are associated with sediments. Poly nuclear/cyclic Aromatic Hydrocarbons

(PAHs) derived from unburned fuel have a higher affinity for sediments than

other hydrocarbons.

3.1.3. Metals

Like hydrocarbons a vast array of metals occur in highway runoff, studies have

generally concentrated on highly toxic metals or metals which are known to

occur at concentrations which could potentially have an ecological impact.

These studies generally include copper, zinc, iron, cadmium and lead; more

recently exotic metals such as the platinum group elements have been

investigated (Whiteley, 2003). Metals can exist in a number of forms: dissolved,

particle bound, and can also occur as compounds or complexes.


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3.1.4. Other Compounds

A number of studies have observed the pollutant loads from a variety of

different substances, which do not fall into the above categories. A summary of

these others compounds follows: pathogens, organics (BOD and COD),nutrients including nitrogen and ammoniacal nitrogen, phosphorous, salts,

persistent organic pollutants, herbicides and pesticides. These compounds and

complexes can be dissolved or particulate and have a wide variety of effects on

the environment.


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3.2. Sources of Pollutants in Highway Runoff

The sources of pollution can be broadly categorised into:

Temporary (e.g. highway maintenance),

Seasonal (e.g. application of de-icing salts),

Accidental, acute effects (e.g. hazardous (chemical) spillage).

Chronic effects (e.g. tyre wear) (Sansalone 1996, Legret 1999).

3.2.1. Vehicles

Vehicles have been cited as the major contributors to highway pollutant build up

in non-urban settings (Davis et al, 2000). Tyre wear is a source of zinc and

cadmium. Brake wear is a major source of copper. Engine wear is a source of

aluminium and copper. Vehicular component wear is a source of iron, chromium

and zinc. Engine leakage (and un-combusted fuel) is the major source of

hydrocarbons (Sansalone et al, 1996).

3.2.2. Existing Installations

These include permanent fixtures such as lighting posts, safety barriers, signs

and other objects which are integral to the highway network. Such installations

can contribute significantly to the pollutant load from highway runoff. Legret et al

(1999) found that galvanized safety barriers were the main source of zinc in

highway runoff.

3.2.3. Atmospheric deposition

Atmospheric deposition can contribute a significant amount to the pollutant load

in highway runoff, this can occur during storms or as a dustfall during dry

periods (Barrett et al, 1995). This is confirmed in Davis (2000) study in which

both wet and dry deposition have been considered in the total pollutant loading

of urban and rural highways. Kim et al (1999) have shown that this deposition

has a strong seasonal and anthropogenic component.


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3.2.4. Accidental Spillage

Accidental spillage is a source of pollutants in the UK. The majority (70%) of

these events involve the spillage of hydrocarbons. Such a spillage event is

usually contained because of the efficiency of the emergency services (DMRB11.3.10). However if spillage pollutants reach a watercourse this will result in a

high-intensity short-duration pollution event, which can have a major impact on

river ecology.

3.2.5. Highway Maintenance

A number of highway maintenance practices can contribute to the pollutant load

in highway runoff. The following practices have associated environmental

impacts:

De-icing

Roadside vegetation control

Construction activities (re-surfacing etc.)

De-icing salts are used in the winter months in the UK to prevent icing of roads.

Sodium chloride is usually used for de-icing, this usually has associated

chemicals including: iron, nickel, lead, zinc, chromium and cyanide (CIRIA 142,

1994).

Vegetation control on road side verges etc. is achieved using mechanical

cutting methods in conjunction with herbicides in order to reduce intensive

labour. Such methods contribute to BOD loading and pollutants, which are listed

in the Dangerous Substances Act (76/464/EEC), to highway runoff.

Construction works on highways are generally associated with the production of

dissolved and particulate solids. In addition the use of heavy plant can introduce

hydrocarbons in highway runoff (Preene et al).


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3.3. Factors Affecting Highway Runoff Quality

3.3.1. Traffic Flow (AADT)

CIRIA report 142 identifies traffic flow (AADT) as the major factor in pollutant

loading. This is confirmed in table 5.1 (pg 107) of the report, which relates traffic

flow to pollutant loading for specific determinands. However, it is appreciated

(DMRB 11.3.10.3.5) that traffic flow may not be the only factor contributing to

pollutant loading. In fact several authors have reported findings, which show

weak or insignificant correlation between traffic flow and pollutant build up. More

recently Kayhanian (2003) in a comprehensive 4-year study based in California

has unequivocally concluded that No simple linear relationship exists betweenhighway runoff pollutantand AADT, including determinands which are know

to be associated with vehicle deposition.

3.3.2. Climate and Local Land Use

Local land use has been identified as an important factor in influencing pollutant

build up (Driscoll et al, 1990). This has lead to a categorisation of sites under

investigation according to surrounding land use. Wong (1997) has presented

eight land use categories with specific corresponding impervious surface area

and runoff coefficients. However such a categorisation may not be well suited in

the UK. Mitchell et al (2000) has presented a tiered approach to land use

categories, which has been developed for world wide sites including sites from

the UK.

Seasonal variations have been suggested as a factor in variation of highway

runoff quality. Such variations are significant in countries with extreme winter

conditions which require the use of studded tyres and excessive de-icing salts

(Backstrom et al., 2003).


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3.3.3. Precipitations Characteristics

Precipitation characteristics have been identified as a factor in highway runoff

quality. The first flush phenomenon has been widely reported in the literature.

However, specific studies suggest that precipitation characteristics influence theobservation of this effect. It is thought that low intensity rainfall which does not

develop into sheet flow on the highway surface will not provide the minimum

shear stress for sediment transport and therefore will not result in the first flush

(Sansalone et al., 1996). Therefore, when considering highway runoff quality

and loading, the following must be taken into account:

antecedent dry period

rainfall intensity and duration

runoff volume

Road surface material has also been identified as a factor in highway runoff

quality. Surface types include concrete and asphalt; increasingly, porous

surfaces techniques are being implemented and have been shown to improve

highway runoff quality by retention of fine particles (Pagotto et al., 2000).


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3.4. Effects of Highway Runoff

Highway runoff has the potential to degrade surface and groundwater sources,

which can have a detrimental impact on drinking water abstraction, recreational

users and the water environment; specifically river ecology. When considering

the effects of highway runoff the type and size of the receiving water course, the

ability for dispersion and ecological diversity all determine the potential effects

of highway runoff. The toxicity, (strength or potency) of a specific pollutant must

be considered for its selection as a determinand to characterise highway runoff

quality.

The issue of water status of rivers has become increasingly important with the

introduction of the Water Framework Directive (WFD, 2000), which aims at

achieving good status of all surface and groundwaters. River ecology will be

used as the main indicator in determining good water status. The new scheme

is based on macro-invertebrate communities which are susceptible to pollutants

which occur infrequently or in trace concentrations and which may be missed by

chemical sampling.

Makepeace et al(1995) and more recently Beasley (2002) have presented the

most extensive review of the impact of pollutants on macroinvertebrates in

urban watercourses. The results are obtained by developing a microcosm

environment and subjecting the species to varying concentrations of a pollutant.

Thresholds are developed for chronic and acute toxicity and no observable

effects, for macroinvertebrates and fish for varying pHs and hardness. A

common indicator used to measure the impact of pollutants is the LC50 notation,

which represents the lethal concentration at which 50% mortality is observed, it

is usually preceded with a duration, which indicates the time for which a specific

concentration was maintained. This results in a spectrum of impairment for

specific species. However, it must be stated that studies of pollutant toxicity are

in their early stages and data concerning specific pollutants is limited.


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3.4.1. Sediments

The role of sediments as a medium for transporting pollutants has been

described (see section 3.1.1). Organic sediments have an associated oxygen

demand, which is detrimental to all aerobic species. Clay and other inertsediments can cause turbidity, which in turn inhibits visual feeders, reduce light

penetration and photosynthesis of (oxygen producing) aquatic plants. In

addition inert sediments can cause smothering and suffocation of

macroinvertebrates on the river bed, gill abrasion, and fin rot in fish (Mitchell et

al., 2000).

3.4.2. Metals

Metals in highway runoff limit the development of macroinvertebrate

communities by interfering with biological mechanisms such as growth rate,

enzyme activity and reproduction (Beasley et al, 2002). This interference occurs

at the bottom of the food chain, and subsequently affects higher orders of

species within the food chain; and therefore modifies the fine balance of river

ecology. Each determinand (pollutant) has a range of effects on river ecology

depending on additional factors such as pH, hardness, temperature etc. Metals

can be grouped into those which are highly toxic and are included in the List I

substances (determinands to be considered in this investigation include Cd) in

the Dangerous Substances Directive (76/464/EEC) and must be eliminated; and

metals which are less toxic (List II) and must be minimised (determinands to be

considered in this investigation include Cr, Pb, Ni). As a general rule metals

become increasingly toxic in the following order: Zn


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3.4.4. Other Compounds

Other compounds as listed in section 3.1.4 have a variety of effects which

include: (1) oxygen demand and therefore oxygen depletion and (2) biological

interference of organism at the macroinvertebrate level.

In the case of ammoniacal nitrogen an additional risk exists because of its ease

of oxidation, enabling rapid depletion of DO and the resulting nitrogen can result

in eutrophication.


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3.5. Control of Highway Runoff

3.5.1. Source Control

Source control is a method in which alternate modes of transportation and car

pooling are examples of steps which can be taken in reducing the pollutants at

source. The implementation of source control to reduce source pollution would

no doubt be difficult, however it is currently being discussed indirectly in a bid to

reduce traffic on motorways and inner cities. In the case of herbicide and de-

icing salt application the Highways Agency have more direct influence, source

control is therefore a more realistic option.

3.5.2. Sustainable (urban) Drainage Systems (SuDS)

The control of pollutants in highway runoff has been addressed in a number of

CIRIA reports (C142, C522, C523 and C609). The implementation of

sustainable (urban) drainage systems (SuDS) to minimise the impact of

pollutants from road runoff, have also been suggested to accompany the

conventional systems such as gully pots and oil separators. A number of SuDS

techniques have developed for a number of situations, however swales and

constructed wetlands are the two main techniques used in conjunction with

highways.

Swales are linear grassed drainage features, which can be designed to provide

a number of functions: conveyance, infiltration, treatment by detention and

treatment by filtration. Trapped sediments can be incorporated into the soil and

assimilated by the vegetation. Constructed wetlands consist of shallow ponds

which contain a high proportion of vegetation (reeds). Wetland vegetation issuitable for the biological removal of a number of pollutants. Although the

design and capability of constructed wetlands are well documented the

Highways Agency is not convinced that reed beds provide a universal solution

(Wilson, 1999).

Recent study suggests that the use of grass swale drainage, infiltration basins,

and wetlands all improve highway runoff quality significantly achieving >70%removal of key pollutants (Sansalone et al, 1999).


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3.6. Current Legislation and Practice

The Environment Agency has a statutory duty for the protection and monitoring

of water quality in England and Wales (Water Resources Act, 1991). Pollution

control is implemented by means of discharge consents, which dictate the

quality and quantity of a specific discharge. Discharge consents are not

normally applied to highway drainage (Highways Act, 1980). However they may

be applied in exceptional circumstances for a site specific discharge.

CIRIA report 142 has presented a complete review of studies for build up rates

of pollutants on roads in relation to annual average daily traffic (AADT). Since

the collation of these studies there is an awareness that further study is required

especially with regard to lead and cadmium build up rates, the use of which has

been declining since the early 1990s.

3.6.1. Environment Agency and Water Quality Assessment

Currently the EA assesses water quality according to the general quality

assessment (GQA) which is based on samples (chemical) of dissolved oxygen

(DO), Biochemical Oxygen Demand (BOD), and Ammonia (NH3)taken monthlyover a 3-year period. Future improvements in water quality are dictated by

water quality objectives (WQO) according to the River Ecosystem (RE)

Classification (HMSO, 1994) which include additional chemical standards

including Copper and Zinc. With regard to highway pollution Copper and Zinc

have generally been deemed as the only heavy metals to cause environmental

impact due to the concentrations found in road runoff.

The GQA has been extended to determine the biological (ecological) quality of

a watercourse. This is based on the size and types of macro-invertebrate

populations which exist in a particular watercourse. The existence of such

macro-invertebrate communities depends on the geomorphology of the river

and therefore biological quality is described by the difference between observed

and expected taxa (species group) for the water under natural conditions. The

expected state is predicted using RIVPACS (River Invertebrate Prediction and

Classification), which takes into account factors such as geomorphology. The


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23

observed taxa are converted into a score using the BWMP (Biological

Monitoring Working Party) system. This scoring system is weighted in favour of

species which are pollution sensitive, presence of these species is therefore an

indication that the water quality is good. The BMWP score is divided by the

number of observed taxa to provide the ASPT (average score per taxon) which

is a stable and reliable index of organic pollution. These observed ASPT scores

are expressed as a fraction of the RIVPACS ASPT score to give Ecological

Quality Indices (EQIs) where the maximum value a river can take is unity

representing very good water quality. EQI standards have been implemented in

the GQA assessment to define water quality in the original GQA river grades a

to e, representing very good to poor quality (EA, 2002).

3.6.2. Highways Agency Environmental Assessment

Currently the Design Manual for Roads and Bridges (DMRB) recommends three

stages of environmental assessment. Initially, this should include a desktop

study to provide an appreciation of the water quality constraints and

consequences resulting from a new or improved road. The stage 2 assessment

should identify the likely impacts on water quality and fisheries including

calculations for probability of spillage and water quality downstream of the

discharge (Annex 3 DMRB). Finally, the stage 3 assessment should identify

individual discharges. Their potential impacts should be evaluated in detail

using specialist mathematical modelling.


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Industrial Project Methodology

24

4. Methodology

4.1. Task 1: Building the Database

This initial stage involved developing a database, which could be used to

extract values from recent studies to develop a relationship between traffic

volume (AADT) and loading rate (kg/ha/yr) for a specific determinand. This

presented a number of obstacles in accepting studies into the database. These

obstacles are given below:

Studies were not relevant to the UK, i.e. external to Europe and therefore

different environmental regulations were in place, which may have had

an impact on the loading rates suggested in such studies. In terms of hydrology, study sites may not represent UK conditions

especially in terms of antecedent dry period. Where this data was

available it was recorded in the database.

Type of highway was also recorded in the database as this has been

shown to have an impact on highway runoff quality.

Surrounding land use for studies varied, this was recorded in the

database as this has been shown to be a significant influencing factor inhighway runoff quality.

Traffic flow data was not always available, such studies were therefore

not included as this data was required to build the traffic flow-loading rate

relationship.

A major obstacle in producing the database was that highway runoff is

usually monitored with equipment which measures concentrations (mg/l).

These values are then used to back estimate the source loadings(kg/ha/yr) with knowledge of the runoff volume, impervious area, and the

runoff coefficient. Where this information was given loading rates could

be calculated, however, in the majority of studies event mean

concentration (EMCs) have been used as the descriptive unit of choice

for highway runoff quality as this is measured on site.


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25

The studies which met the above criteria were entered into a database which

contained the following information:

Author

Year of study Location of study

Duration of study

Site description including local land use

Road surface type

Traffic flow: Annual Average Daily Traffic (AADT)

Determinands measured in study

Rainfall data if given Additional Notes

The study profiles included in the database are given below:

Author (s) Study Profile

Barret et al

(1996)

Results from a 2 year study of 3 sites in Austin, Texas (Mo Pac

Express Way). Determinands included some heavy metals and

organics.Wu et al

(1996)Water quality monitoring (1 year) from 3 sites in North Carolina.

Legret et al

(1999)

A 1 year study (1995-1996) of a single motorway site in Northern

France. Determinands included heavy metals and organics.

Drapper et al

(1999)

A 1.5 year study in Queensland Australia of 3 sites in urban, and

residential settings.

Shinya et al

(1997)

A 1 year study in Japan of a single site including all metals

required (see section 2) and organics.

WRc (2002)

A Long Term water quality monitoring study of 6 sites in the UK,

an exhaustive list of metals, herbicides, hydrocarbons and

organics was investigated.

Table 3: Profiles of studies including in the database.

It should be noted that cobalt loading rates were not presented in any of the

studies and only one result of manganese loading rates was found in a single

study (Shinya et al., 1997). In total 17 separate sites were included in the


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26

database, this may seem small in comparison to other databases which have

been compiled (Mitchell et al., 2000 have presented a database with 160

studies) however, as has been mentioned before, difficulty was encountered in

finding (or calculating) loading rates (kg/ha/yr), as EMC values are more often

quoted.

4.1.1. Data Manipulation

The data of pollutant loading and traffic flow was used in order to produce a

relationship between the variables. Even though all studies in the database may

have met the initial criteria, not all the data from the studies was accepted to

form a relationship between traffic flow and pollutant loading. This was because

including all the data usually resulted in a scatter plot with little correlation (see

Figure 1). Outliers were removed initially; consequently it was found that these

outliers may belong to a specific study which was anomalous throughout e.g.

Shinya et al. present loading rates for an AADT of 75,000 which is remarkably

low in comparison to other sites with similar AADT for all determinands. In

addition, some studies (pg 61; WRc, 2002) recognised that certain sites were

anomalous, these were also removed. The relationship between AADT and

loading rate (copper) is given below prior to, and after removal of anomalous

values.

Relationship Between AADT and Copper Loading Rate

R2

= 0.0062

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

0 20,000 40,000 60,000 80,000 100,000AADT

CopperLoading(kg/Ha/yr)

Figure 1: Relationship between loading rate and AADT prior to removal of outliers. Notethe R

2value indicates weak (no) correlation.

Outliers

Identified

by WRc

Shinya et al


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27

Relationship Between AADT and Copper Loading Rate

R2 = 0.7485

0

0.1

0.2

0.3

0.4

0.5

0.6

0 20,000 40,000 60,000 80,000 100,000AADT

CopperLoading(kg/Ha/yr)

Figure 2: Relationship between loading rate and AADT after removal of outliers. The R2

value now indicates significant correlation.

A summary table providing statistics indicating significance of individual

relationships for each determinand is given below:

Determinand

SampleNumber

(n) R22

1

2

r

nrt

=

Significance

Lead (Pb) 6 0.73 3.28 0.999

Chromium (Cr) 5 0.7 2.62 0.996

Nickel (Ni) 5 0.15 0.73 0.766

Cadmium (Cd) 5 0.45 1.56 0.94

Iron (Fe) 3 0.04 0.21 0.415

Manganese(Mn)

Only one measurement was found for this determinand

Cobalt No data was collated for this determinand


6 0.72 3.2 0.999

COD 9 0.7 4.08 1.000

BOD 5 0.8 3.47 0.999

NH4-N 4 0.91 4.4 1.000

Hydrocarbons 10 0.4 2.31 0.989Table 4: Significance of relationships derived for individual determinands.

The established convention recognises a value >0.95 to be significant, i.e. a

relationship between the variables exists (Clarke, 1983) .


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The derived linear relationships were then used to construct the updated table

5.1for the additional determinands (see Table 5 below).

Pollutant Load (kg/ha/yr)

AADT

Determinand 30,000

Lead (Pb) 0.15 0.20 0.30 0.45

Chromium (Cr) 0.07 0.08 0.09 0.15

Nickel (Ni) 0.07 0.08 0.08 0.08

Cadmium (Cd) 0.005 0.01 0.01 0.015

Iron (Fe) 1.0 3.0 7.0 15.0

Manganese (Mn) - - - -

Cobalt - - - -

Total SuspendedSolids 600 700 850 1250

COD 250 350 550 1100

BOD 30 40 55 85

NH4-N 1 4 9 20

Hydrocarbons 5 10 15 25Table 5: Table 5.1 (CIRIA, 1994) extended for additional determinands.

The above results are encouraging as corresponding values are observed for

determinands presented in CIRIA report 142 (COD, NH4-N c.f. Table 2).

4.1.2. Effect of Traffic Flow >30,000 on Pollutant Loadings

The relationship between traffic flow and pollutant loading is at most a tenuous

one (see section 3.3.1Traffic Flow (AADT)). The use of traffic flow (AADT) as

the primary variable in pollutant loading is far from ideal but has been

implemented in order to follow the CIRIA 142 report methodology. Other

approaches to characterise pollutant loading will be discussed in later sections

(5.2) which will cover the effect of traffic flow >30,000 on pollutant loading.


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4.2. Task 2: Relevant Environmental Quality Standards

Currently the DMRB assessment includes Environmental Quality Standards

(EQSs) from the River Ecosystem (RE) classification (see Appendix), however

thresholds for many determinands do not exist (or have not been determined)

for the RE classification. Therefore threshold values have been taken from

elsewhere, this problem has been recognised by Mitchell et al.(2000) and the

WRc (2002). The table below provides the source from which the relevant

EQSs have been taken.

Determinand Source

Lead (Pb) Dangerous Substances DirectiveChromium (Cr) Dangerous Substances Directive

Nickel (Ni) Dangerous Substances Directive

Cadmium (Cd) Dangerous Substances Directive

Iron (Fe) France - Agence RMC

Manganese (Mn) Not required (Agence RMC)

Cobalt Not required (Russian River Authority)

Total SuspendedSolids France - Agence RMC

COD France - Agence RMC

BOD Original RE Classification

DO Original RE Classification

NH4-N Original RE Classification

HydrocarbonsSurface water intended for Abstraction of Drinking

Water Directive (Russian River Authority)Table 6: Source of Environmental Quality Standards

EQSs for lead, chromium, nickel, and cadmium have been taken from the

Dangerous Substances Directive (76/464/EEC). This directive aims to prevent

and minimise the discharge of dangerous substances to water. EQSs (limits)have been set for freshwater, estuary and marine water. Cadmium occurs in the

list 1 of dangerous substances, the discharge of which must be prevented.

Lead, chromium and nickel occur in list 2b of dangerous substances related to

hardness, the discharge of which must be minimised. List 2b is further

subdivided into freshwaters, which are suitable for salmonid and cyprinid

fisheries which correspond to RE 1/2 and RE 3/4 objectives respectively. These

determinands therefore were simply placed in the RE classification system.


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EQSs for Iron, Total Suspended Solids and COD were taken from the Agence

RMC (Agence de l'eau Rhne Mditerrane & Corse), which is Frances

equivalent to the Environment Agency. The Agence RMC have determined

EQSs for determinands unavailable in the RE classification system (Chocat,

1997). The Agence RMC river classification system is similar to the RE

classification system. In addition, watercourses (and corresponding EQSs) are

subdivided into regions and industrial pasts. For UK rivers Agence de l'eau

Artois-Picardieregion was chosen as this was the most appropriate description

of rivers in the UK.

EQSs for Manganese and Cobalt were not required because loading rates were

unobtainable (see previous section) and therefore the downstream levels of

Manganese and Cobalt could not be calculated.

COD, BOD and NH4-N EQSs were taken from the original RE classification

system. The total ammonia EQS was most appropriate for comparison with

NH4-N.

The hydrocarbon EQS has been taken from the Surface Water Intended for

Abstraction of Drinking Water Directive (75/440/EEC), because the Freshwater

Fish Directive (EC, 1978) offers only qualitative advice. This poses a

complication, as this directive has significantly different aims from the RE

classification system, in addition this directive has completely different

categories of water quality based on the water treatment system being

implemented. In order to use this value the imperativevalue for drinking water

category 1(DW1) has been related to RE1, imperativevalue for drinking water

category 1(DW2) has been related to RE2/RE3, imperativevalue for drinking

water category 1(DW3) has been related to RE4. It is appreciate that this

situation is far from ideal, however the only other standard found was that of

from Russias fisheries protection which indicates a value of 0.05mg/l which is

equal to the DW1 value which indicates that these values are of the correct

magnitude (this issue will be discussed in a later section).


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31

Furthermore, the DMRB assessment requires knowledge of the River Class

alone and not of the drinking water category. In fact, (E)QSs can be found from

a number of different sources each with a different aim, application of the

relevant (E)QS should be the key issue.

The extended RE classification EQSs can be found in Table 7 below.

4.2.1. Upstream Concentrations

With regards to the background values (upstream concentrations) of the

determinands the CIRIA report has suggested that 0.5 times the relevant EQS

should be used (where information from the regulator is unavailable). This will

therefore be the method implemented in this report. It may also be of interest to

vary this factor to observe variations in downstream water quality.


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32

Hardnessmg/l

CaCo3

LeadAnnual

Average(g/l)

ChromiumAnnualAverage

(g/l)

NickelAnnual

Average(g/l)

Cadmium

(g/l)

Iron(mg/l)

Manganese(mg/l)

Cobalt(mg/l)

TotalSuspended

Solids(mg/l)

COD (mg/l)90 percentile

HydrocarbonsAnnual

Average(mg/l)

Class

250 20 50 200


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33

4.3. Task 3: Accounting for Dissolved Oxygen

Accounting for dissolved oxygen (DO) presents a unique problem because DO

is not a pollutant, does not build up on roads and is a time dependent

determinand depending on local environment (e.g. presence of BOD).

Furthermore, any additional DO from highway runoff (rainwater) has a positive

impact on river quality, unlike the other determinands being considered in this

report.

DO is a function of temperature, salinity and atmospheric pressure (altitude). A

strong inverse relationship exists between DO and temperature, a weaker

inverse relation between DO and salinity exists; and a positive relationship

between DO and atmospheric pressure exists. A number of empirical

relationships exist which relate DO saturation with temperature, (salinity and

altitude). DO in rivers occurs due to photosynthesis of plants and oxygen

transfer from the atmosphere (affected by turbulence). DO in rivers therefore

has a diurnal component. During daylight hours of summer months the rate of

photosynthesis can be such that the DO can become supersaturated.

DO in rainwater is more complicated; size of droplets, gas transfer coefficient,

initial DO concentration and time of exposure all have an impact on the final DO

concentration in rainwater as described by Daltons/Henrys Law.

Performing a mass balance of DO will result in the instantaneous DO

concentration immediately downstream of the outfall (assuming instantaneous

complete mixing). Such a mass balance requires the following information (andassumptions) to determine the initial DO conditions:

Background DO levels in upstream river.

Temperature (salinity and altitude) of river.

DO concentration in rainwater.

Background DO levels can be obtained from the GQA scheme data which is

available from the EA in terms of %DO saturation. This value must be convertedto mg/l by calculating the DO saturation (mg/l) using an empirical relationship,


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34

The main independent variable for DO in water is temperature; this will be

assumed to be 10 C for all subsequent initial calculations for simplicity.

To simplify the problem further, rainwater will be considered to be saturated atthe ambient temperature. This can be easily proven by implementing

Henrys/Daltons Laws. Qualitatively, rainwater droplets have a high surface

area to volume ratio (SA:V) and are exposed to the atmosphere for a significant

length of time, and are therefore saturated with DO.

Given these initial conditions, it becomes clear that performing a mass balance

for DO will result in an increase of DO downstream of the outfall regardless of

the DO concentration upstream (unless supersaturated conditions are prevalent

upstream). The outfall discharge therefore has a beneficial effect on the river

quality downstream in terms of DO.

In this brief assessment, the effect of BOD (loading on roads) on the highway

runoff before it is discharged at the outfall has not been considered; it is unlikely

that this will have a significant impact on the DO as the time of concentration for

the impervious surfaces being drained is usually very short (~minutes,

Sansalone et al., 1996). The only exception would be if a highly a fast chemical

oxidation demand is exerted, due to presence of a highly chemically oxidative

substance.


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4.3.1. Streeter-Phelps Equation

The previous evaluation does not consider the effect of BOD on DO

downstream of the discharge although BOD concentrations in highway runoff

are significant (Pitt et al., 2001) and would have a significant impact on DO.

Such an evaluation would require a process to model the effect of BOD on DO

with time. The Streeter-Phelps (1925) equation can be used as a first order tool

to model the impact of BOD on DO as a spatially variant problem for a steady

input (Jubb et al., 1998). This method, although useful in understanding the

spatial variant nature of DO in the river, may be considered beyond the remit of

the stage 2 DMRB assessment, and would not correspond to the level of

complexity applied to the other determinands.

Equation 1: Streeter Phelps-Equation

La = Ultimate BOD at t =0 k1= BOD reaction constant

Da = DO deficit at t =0. k2= reaeration constant

Dt = DO deficit at time t.

Implementing Streeter-Phelps poses a number of obstacles:

Constants required to perform the analysis are unknown.

The solution of Streeter-Phelps results in the variation of DO with time of

travel. The result therefore cannot be compared easily with RE standards

or other conventional standards.

The constants can be assumed or derived from current literature; the constants

could be manipulated to represent the worse case scenario. Currently the

DMRB assessment compares downstream determinand concentration with

percentile standards from the RE classification system. This would not be

possible if Streeter-Phelps was implemented as the variation of DO with time is

calculated. The issue of intermittent discharges and their resulting impacts has

been discussed extensively in the Urban Pollution Management Manual

([UPMM] FWR, 1998).

tk

a

tktkat D

kk

LkD 221 10]1010[

12

1 +

=


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36

The UPMM recognised the need for wet weather standards, particularly for

CSOs which discharge during wet weather and are characterised as being high-

load, short-duration events, which can have a disproportionate impact on river

ecology. It can be argued that highway discharges are similar in terms of their

intermittent nature, however the pollutant make up of the effluents (CSOs,

highway discharge) will no doubt be different.

The UPMM has suggested the use of Fundamental Intermittent Standards (FIS

see Table 8) to overcome the problem of measuring such complex intermittent

events. FIS are directly related to the characteristics of events which impact on

river ecosystems. These standards are expressed in terms of concentration-

duration thresholds, with permissible return-periods.

Dissolved Oxygen Concentration (mg/l)

Return Period Sustained Duration

1 hour 6 hours 24 hours

1month 5.0 5.5 6.0

3 month 4.5 5.0 5.5

1 year 4.0 4.5 5.0

Table 8: Fundamental Intermittent Standards ({UPMM}, FWR, 1998) for salmonidpopulation i.e. RE1/RE2 river.

The implementation of FIS in this evaluation would introduce a means by which

the result of the Streeter-Phelps equation could be compared. In addition,

implementation of FIS on highway discharges introduces a unique way ofintegrating the evaluation of intermittent events. Furthermore, FIS introduces a

more pertinent question about how highway discharge should be assessed; this

will be discussed in a later section.


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An example of implementation of Streeter-Phelps with FIS is given for a river

with the following properties. The values have been used for demonstration

purposes only.

River Class RE2

DO upstream 70% saturation

DO saturation 10 mg/l

k1 0.1 (day-1)

k1 0.4 (day-1)

La 36 mg/l (Pitt et al., 2001)

Worse case scenario

Da 3 mg/l

Occurrence Once a month

Table 9: Values required to calculate DO sag curve (Streeter Phleps)

The characteristic DO sag caused by the oxidation of BOD can be seen in the

graph below.

Variation of Dissolved Oxygen with Time

0

1

2

3

4

5

6

7

8

0 1 2 3 4

Time (days)

D

issolvedOxygen(mg/l)

Figure 3: characteristic sag curve defined by the Streeter-Phelps Equation

Once calculated, the DO sag curve can be used to compare against the FIS

standards, this is shown in the table below (the red shading indicates failure).


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In this example the DO was found to be lower than the 6 of the 9 limits set for

an RE 2 river.

Dissolved Oxygen Concentration (mg/l)

Return Period Sustained Duration

1 hour 6 hours 24 hours

1month 5.0 5.5 6.0

3 month 4.5 5.0 5.5

1 year 4.0 4.5 5.0

Table 10: DO failure of a river compared with FIS, red shading indicates DO failure.

It is appreciated that Streeter Phelps is an over simplification of the interaction

between DO and BOD, and ignores a number of other important factors which

impact on DO e.g. photosynthesis. However, the principal remains that a

method (of suitable complexity to the CIRIA methodology) should be

implemented in order to understand the variation of DO with time which can be

compared to standards that are designed for intermittent events.


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4.4. Task 4: Applying the Methodology

4.4.1. DMRB Examples

The methodology described in the previous sections for the additional

determinands (except DO) has been implemented for examples 1-3 given in the

DMRB (Annex III, 11.3.10). The initial conditions (key values) for the three

examples are given below.

Example

1 2 3

River Class RE2 RE2 RE1

Q95 low flow (m3 /s) 0.016 0.011 0.063

Average hardness (mg/l) 610 97 65

Antecedent dry period 5 5 5

Rainfall (mm) 11 13 11

Run off coefficient 0.5 0.5 0.8

AADT on motorway 18,000 120,000 40,000

AADT on exit slip road 2000 0 0AADT on entry slip road 2500 0 0

Impervious area ofMotorway (ha) 6.45 11.25 30.00

Impervious area of sliproad (ha) 0.16 0 0

Table 11: Initial conditions for DMRB examples.

With the above information, Table 5 was used to calculate the downstream

concentrations of the additional determinands, Table 7 was then used to

compare the downstream concentration with the relevant derived EQSs. The

results of implementing the CIRIA 142 methodology on the additional

determinands for the DMRB examples are given in the table below (Table 12).

The downstream pollutant concentration, relevant EQS and the ratio of these

two values are given for all determinands in order to provide an indication of the

magnitude by which a specific EQS has been breached.


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DMRB Examples

1 2 3

Determinand

DownstreamConcentration

(D mg/l)EQS(mg/l)

Ratio(D:EQS)


(D mg/l)EQS(mg/l)

Ratio(D:EQS)

DownstreamConcentration(D mg/l)

EQS(mg/l)

Ratio(D:EQS)

Pb 0.023 0.02 1.15 0.044 0.01 4.41 0.026 0.01 2.62

Cr 0.024 0.05 0.48 0.017 0.01 1.66 0.011 0.01 1.10

Ni 0.083 0.2 0.42 0.036 0.1 0.36 0.038 0.1 0.38

Cd 0.002 0.005 0.50 0.003 0.005 0.56 0.002 0.005 0.49

Fe 0.750 1 0.75 1.657 1 1.66 0.931 0.5 1.86


52.913 25 2.12 121.617 25 4.86 71.969 25 2.88

COD 37.731 25 1.51 71.214 25 4.31 44.006 25 2.57

BOD 4.367 4 1.09 8.920 4 2.23 5.668 4 1.42

NH4-N 0.440 0.6 1.15 0.536 0.6 3.34 0.405 0.6 2.03

hydrocarbons 0.838 0.2 4.19 2.348 0.2 11.74 1.338 0.2 6.69

Table 12: Results of implementing the CIRIA 142 methodology for the additional determinands, the red shading indicates failure.


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It can be seen from the above table (Table 12) that the EQSs have been

exceeded for the majority of the determinands.

The following determinands exceed the EQSs consistently for all examples:

Lead

Total Suspended Solids

COD

BOD

NH4-N

Hydrocarbons

The most alarming result is that the lead EQS (which is a list 2b dangerous

substance) has been exceeded.

The chromium (list 2b hardness related dangerous substance) EQS has been

exceeded for example 2 and 3, both of which have low hardness values (97, 65

respectively). Whereas for example 1 the corresponding EQS has not been

exceeded as the hardness value (610) is much higher, therefore the

corresponding EQS is higher and harder to exceed.

The Iron EQS has been exceeded for example 2 and 3 both of which have

traffic flows (AADT) >30,000, it has not been exceeded for example 1 for which

the AADT< 30,000.

The hydrocarbon EQS has consistently been exceeded by a significant amount

(average of ~7.5 times EQS) for all examples.

The Nickel EQS has not been exceeded for a single example, the ratio deviates

negatively away from the background value i.e less than 0.5. This indicates that

the highway runoff is a source of dilution for this determinand as the

concentration in the highway runoff must be significantly less than the EQS.


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Example 2 performs the worst from the three examples, the same number of

EQSs have been exceeded for example 3, however the magnitude by which

they have been exceeded for example 2 is greater for all determinands. This is

due to the combination of the following factors:

low river flow (Q95).

high traffic loading (AADT).

low hardness values.

This effect could have been made worse by implementing a reduced rainfall

(and runoff coefficient), which would have result in reduced dilution of the

pollutants.


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4.4.2. Area 10 Examples

This section includes the location, description, key values, and results for 3 sites

taken from the Area 10 region for which stage 2 assessments have already

been completed (Atkins, 2004). In addition a single example from the Area 2(West Midlands) region has been included.

Langwood Brook

Langwood Brook is a tributary of the River Irwell. This outfall drains a section of

the A56 trunk road near Haslingden into the Langwood Brook. The land

surrounding the outfall is rural and agricultural. Langwood Brook is of Good

quality and its water quality objective is RE 2. Previous study has found (Atkins,

2004) that no mitigation measures are required for routine discharge, however

measures should be in place to protect against accidental spillage. The location

catchment plan and photographs of the outfall are given below.

Figure 4: Location and catchment plan of Langwood Brook outfall

The outfall discharging into Langwood Brook, a milky water colour was noted.

Langwood Brook


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River Mersey

The River Mersey is of fair quality and its water quality objective is RE 4. Two

outfalls have been considered in a previous report (Atkins, 2004) which drain

sections of the M60 and A5103 (south Manchester, junction 5), areas with

extremely high traffic flows into the River Mersey. These outfalls are considered

High Risk (URS, 2000). Previous study (Atkins, 2004) has found that no

mitigation measures are required for routine discharge; however measures

should be in place to protect against accidental spillage. The location and

catchment plan are given below. The location map is given below.

Figure 5: Location map of catchment which discharges to River Mersey. The blackshading indicates the catchment being drained to the River Mersey.


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Worsley Brook

Worsley Brook flows to the Manchester Ship Canal, it is of fair quality and its

water quality objective is RE 4.The outfall being considered drains sections of

the M60 (west Manchester, junction 12) into Worsley Brook. The land

surrounding the outfall is residential. This outfall is considered High Risk (URS,

2000). Previous study has found (Atkins, 2004) that no mitigation measures are

required for routine discharge, however measures should be in place to protect

against accidental spillage. The location map is given below, along with

photographs of the outfall and the open channel leading to Worsley Brook.

Figure 6: Location map of catchment which discharges to Worsley Brook.

Figure 7: Outfall with screen, open channel carrying discharge to Worley Brook; asignificant film was observed on the water surface.


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River Boyd

This example has been taken from an Area 2 report (Atkins, 2002) which

involved a DMRB stage 3 assessment of outfalls considered High Risk in a

previous report (URS, 2000). This outfall drains a section of the M4 (Junction

18) into the River Boyd. The River Boyd is of good quality and its water quality

objective is RE 2. The report concluded that mitigation measures for the

discharge of pollution are required in the form of petrol interceptors and

constructed wetland.


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The key values required for implementation of the CIRIA methodology are given

in the following table:

Example

LangwoodBrookA56 Near

Haslingden

RiverMersey

M60 Jn 5

WorsleyBrook

M60 Jn 12

RiverBoyd

M4 Jn 18

River Class RE 2 RE 4 RE 4 RE 2

Q95 low flow (m3 /s) 0.735 3.204 0.03 0.006

Average hardness (mg/l) 150 85 198 150

Antecedent dry period 5 5 5 5

Rainfall (mm) 12 12 9 5.75

Run off coefficient 0.7 0.7 0.7 0.7

AADT on motorway 1 41,700 117,200 108,600 71,000

AADT on motorway 2 0 107,200 0 0

AADT on entry slip road 2085 5360 22,300 0

Impervious area ofMotorway (ha)

10.6 14.7 18.1 8.44

Area of embankment andcuttings 39.9 0 2.6 16.05

Table 13: Key values required for water quality calculation downstream of outfall.

Calculations were performed as described in earlier sections (4.4.1DMRB

Examples).

Notice that in these examples the runoff coefficient was set at 0.7 rather than

the suggested value of 0.5 (CIRIA 142). In addition, area of embankment and

cuttings are included in these examples, this part of the catchment contributes

additional diluting potential to the runoff and the runoff coefficient of these areas

was set at 0.4.

The value of rainfall used for the River Boyd catchment outfall is 5.75 mm which

is significantly less than other values used. This is because this was taken from

a stage 3 assessment and the Wallingford procedure was not implemented as

suggested by CIRIA 142. This value was taken directly from rain gauge data

and was presented as mean rainfall on the day following a dry period.


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Results

Area 10 (2) examples

Langwood Brook(A 56 Near Haslingden)

River Mersey(M60 Junction 5)

Worsley Brook(M60 Junction 12)

River Boyd(M60 Junction 18)

DeterminandDownstream

Concentration

(D mg/l)

EQS

(mg/l)

Ratio

(D:EQS)


(D mg/l)

EQS

(mg/l)

Ratio

(D:EQS)


(D mg/l)

EQS

(mg/l)

Ratio

(D:EQS)


(D mg/l)

EQS

(mg/l)

Ratio

(D:EQS)

Pb 0.005 0.01 0.54 0.063 0.125 0.50 0.059 0.125 0.47 0.033 0.010 3.32

Cr 0.010 0.02 0.50 0.100 0.2 0.50 0.072 0.200 0.36 0.014 0.020 0.68

Ni 0.074 0.15 0.50 0.075 0.15 0.50 0.053 0.150 0.35 0.029 0.150 0.20

Cd 0.002 0.005 0.50 0.002 0.005 0.50 0.002 0.005 0.41 0.001 0.005 0.30

Fe 0.509 1 0.51 0.756 1.5 0.50 1.064 1.500 0.71 1.212 1 1.21

TotalSuspended

Solids13.530 25 0.54 13.169 25 0.53 55.385 25 2.22 91.766 25.000 3.67

COD 13.022 25 0.52 40.269 80 0.50 52.976 80 0.66 53.122 25 3.25

BOD 2.059 4 0.51 4.035 8 0.50 5.862 8 0.73 6.603 4 1.65

NH4-N 0.301 0.6 0.50 1.248 2.5 0.50 0.982 2.5 0.39 0.376 0.60 2.5

Hydrocarbons 0.122 0.2 0.61 0.513 1 0.51 1.274 1 1.27 1.788 0.20 8.94

Table 14: Results of implementing the CIRIA 142 methodology for the additional determinands on outfalls in the Area 10 (2) region, the red shadingindicates failure for the corresponding EQS.


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It can be seen from the above table that in contrast to Table 12, (DMRB

examples) the EQSs have not been exceeded for the majority of the

determinands, for three out of the four examples.

Langwood Brook does not exceed a single EQS, however small deviationsfrom the background levels are observed for the following determinands: Lead,

Total Suspended Solids, COD and Hydrocarbons. This result is due to the fact

that the Q95 value is significant (in comparison to the DMRB examples) and

therefore represents a bigger proportion of the downstream flow once runoff is

taken into account. In addition, the hardness value is quite large, which

increases the value of the hardness related EQSs.

The River Mersey does not exceed a single EQS. Small deviations are

observed for Total Suspended Solids and Hydrocarbons. This result is due the

low river quality (RE 4) which results in high targets (which can be passed

easily) and the large Q95 value as observed for Langwood Brook.

Worsley Brook is observed to fail the Total Suspended Solids and

Hydrocarbon EQSs. Deviations toward failure are observed for Iron, COD and

BOD. The concentration of the remaining determinands (Pb, Cr, Ni, Cd and

NH4-N) downstream are observed to fall below the background value, indicating

that the runoff provides a source of dilution. This example appears to be a

candidate for EQS failure for the majority of determinands because of the low

Q95 value, however the low river quality (RE 4) and the high hardness value

results in high EQS values.

The River Boyd is observed to fail the majority of EQSs. This example is

similar to the DMRB examples as it exhibits good river quality, moderate

hardness and a very low Q95 value. In addition, the dilution potential is reduced

further because of the low rainfall value taken from the stage 3 assessment.

This combination results in multiple failures of corresponding EQSs. The

chromium and NH4-N concentration downstream is observed to deviate toward

the EQS. The chromium EQS is hardness related; if the hardness is reduced

(


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4.4.3. Sensitivity Analysis

In summary, watercourses which are at risk of exceeding these preliminary

EQSs are characterised by the following:

Good quality rivers (RE 2 and above). Low hardness values (


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In addition, when the low flow (Q95) is increased, (Figure 15) the downstream

pollutant concentration decreases. Again, this is expected as the increase in

volume represents an increase in absolute mass (M = C x V) of the

determinand, in comparison to the absolute mass available in runoff. In this

example, when the flow is increased to ~0.45 m3/s, this value is just enough to

bring all the determinands below the corresponding EQS (i.e. EQS ratio 30,000).

The concentrations are catchment area independent as both mass loading and

volume of runoff are proportional to area; the absolute concentrations are given

in the table below (Table 16).


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Relationship between Highway runoff pollutant concentration

(expressed as a ratio to corresponding EQS) and river class.

0

10

20

30

40

RE 1 RE 2 RE 3 RE 4

River Class

Ratio{Runoff:EQS}concentration

Fe

TSS

COD

BOD

NH4-N

Hydrocarbons

Cd

137

Figure 8: Concentrations of determinands in highway runoff (expressed as a ratio ofcorresponding EQS) for a 10mm rainfall event (AADT>30,000)

RE 1 / RE 2 RE 3 / RE 4

Hardness 250 250

Determinand

Pb 30.8 12.33 12.3 6.16 6.16 6.16 6.16 0.99 0.99 0.49 0.49 0.49

Cr 8.22 4.11 2.05 2.05 0.82 0.82 0.27 0.23 0.21 0.21 0.16 0.16

Ni 0.44 0.22 0.15 0.15 0.11 0.11 0.44 0.22 0.15 0.15 0.11 0.11

Table 15: Concentrations of hardness related determinands in highway runoff (expressedas a ratio of corresponding EQS) for a 10mm rainfall event (AADT>30,000)

Determinand Pb Cr Ni Cd Fe TSS COD BOD NH4-NHydro-carbons

Concentrationin highway

Runoff (mg/l)0.123 0.041 0.022 0.004 4.11 343 301 23.3 5.48 6.85

Table 16: Absolute concentrations of determinands in highway runoff for a 10mm rainfallevent (AADT>30,000).


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It is observed from Figure 8 that the order of priority pollutants in terms of the

magnitude by which they exceed corresponding EQSs in descending order are

as follows:

1. Hydrocarbons

2. Total Suspended Solids

3. COD

4. NH4-N

5. BOD

6. Iron

The cadmium concentration in highway runoff does not exceed the relevant

EQS for all RE classes (0.82 EQS throughout RE classes) and is therefore not

a priority pollutant, as it is not possible for cadmium concentration to exceed the

relevant EQS downstream of an outfall.

It is appreciated that hydrocarbon EQS is actually a DWS.

Likewise, for hardness related determinand the order is as follows (Table 15):

1. Lead

2. Chromium

The nickel concentration in highway runoff does not exceed the relevant EQS

for all RE classes (


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In terms of the mass balance calculation:

A range of situations exist, the extremes are given here:

When catchment area is small, i.e. VRO


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Relationship between the ratio of runoff volume and river flow todownstream pollutant concentration (expressed as ratio of EQS)

0

2

4

6

8

10

12

14

0 0.2 0.4 0.6 0.8 1

Ratio {runoff:river} Volume

Ratio{Downstream:EQS}

concentration

Pb

Cr

Cd

Ni

Fe

TSS

COD

BOD

NH4-N

Hydrocarbons

Figure 9: Use of VRO : Vb index for DMRB example 2.

The VRO : Vb index has been implemented for all the examples in this study and

are shown in the table below (Table 17). It is clear from these results that this

index gives a good, quick indication of whether a site is likely to fail. In addition,

this index can be used with limited knowledge of the site only, Q95 and

catchment area are required all other factors can be assumed for a simple

assessment to identify high risk sites.

DMRB1

DMRB2

DMRB3

RiverMersey

M60 Jn 5

Langwoodbrook

A56 NearHaslingden

WorsleyBrook

M60 Jn 12

River BoydM4 Jn 18

REclass

RE 2 RE 2 RE 1 RE 4 RE 2 RE 4 RE 2

VRO:Vb 0.26 0.77 0.49 0.003 0.001 0.50 0.75

No. offailures

6 8 8 0 0 2 7

Table 17: Use of index VRO : Vb to indicating as VRO : Vb increases so does the number offailures.


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Industrial Project Critical Review of DMRB Assessment

56

5. Critical Review of DMRB Assessment

5.1. Current Assessment

The DMRB assessment introduces a simple solution to a complex time variant

problem in order to provide an understanding of the worse case scenario of the

impact of highway on water quality when designing highways and associated

drainage systems. This has been achieved by implementing a number of

simplifying assumptions:

Fixed antecedent dry period.

Regional precipitation characteristics.

Summer 1-year 24 hour storm.

Linear pollutant build up.

Total pollutant wash off.

Fixed runoff coefficient.

95%ile flow represents 95%ile concentration.

Relationship between AADT and pollutant loading is linear.

It is recommended that these assumptions require updating to reflect changes

in opinion suggested in the literature.


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5.2. Proposed Assessment

It is proposed that the current DMRB assessment should be updated so that

level of complexity remains the same, however the simplifying assumptions

should be changed. In fact the principal document (CIRIA 142, 1994)

recognised that the assumptions made were simplistic and another method of

water quality assessments is suggested (Appendix A, CIRIA 142, 1994).

5.2.1. Rainfall Characteristics and Antecedent Dry Period

CIRIA report 142 recognised the characteristics of a storm which represented

the worse case scenario (pp107), this can be generalised as a short duration

rainfall and therefore small dilution volume. In addition, if this rainfall event

occurred in summer there would be low flows in the receiving waters for dilution.

Furthermore, summer storms are usually preceded by significant dry periods

which allow pollutant build up. However, CIRIA 142 suggested the use of the

Wallingford procedure to characterise the rainfall, this procedure considers the

24-hr, 1 year storm for all UK regions. This procedure is simple to implement

however it yields quite large values of rainfall ~10mm which may not representthe most concentrate scenario for highway runoff.

In addition the Wallingford procedure has been updated by the Flood Estimation

Handbook (Institute of Hydrology, 1999) which recommends guidance in order

to construct design storms considering rainfall: profile, duration storm and return

period. However this is beyond the complexity required for the DMRB stage 2

water quality assessment. The FEH also offers guidance on calculating rainfallfollowing an antecedent dry period, which is simply a weighted average of

previous rainfall events. Again, this would require additional (rainfall gauged)

information which may not be ideal for a simple assessment.

For a simple assessment therefore a single value of rainfall is required which

represents the least volume which produces highway runoff i.e. exceeds

infiltration capacity (depression storage). Sansalone et al(1996) have found this

value to be 0.25mm in the USA; whilst Atkins have suggested that the value is


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1.2mm (in the UK) before highway runoff is achieved. Clearly infiltration

capacity is dependant on highway type (concrete, asphalt etc.) and these

figures require further confirmation. However, these values do suggest that a

much smaller value than that suggested by the Wallingford procedure could be

implemented to represent the worse case scenario.

Rather than setting arbitrary values based on exceedance of infiltration capacity

alone these could be combined with return periods. Such data is available from

the FEH (CD-ROM) for individual areas (T-year D-hour; MT-Dh) the FSR

relationships are given below for illustrative purposes only. Once the rainfall

depth is decided upon it could be checked to see whether this corresponds to a

realistic return period i.e. 6 month and 1 year return periods need only be

considered.

Relationship between Rainfall and duration of rainfall for various

return periods (FSR)

0

5

10

15

20

25

30

35

40

45

50

0 5 10 15 20 25 30 35

Duration (mins)

Rainfall(mm) 6 months

1 year

2 years

10 years

100 years

1000 years

Figure 10: Relationship between Rainfall and duration for Silsoe; adapted from NERCFlood studies Report (1975)


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5.2.2. Pollutant Build up and Wash Off

The CIRIA guidance indicates the use of linear build up of pollutants on

highways with time, by implementing pollutant loading rates. This offers a

Documents

Final Report Area Ten