Boron: More than just a marker for sewage effluent

Martyn Tattersall

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Abstract

18 sites across 11 rivers in the Northumbria River Basin were sampled and analysed for

soluble reactive phosphorus (SRP) and boron (B) so that the variables could be used to see

the interaction between SRP and B and the relationship between a soluble reactive

phosphate and boron ratio (SRP:B) and a seasonal change of SRP (SC_SRP) method of

determining sources of P. The data suggests that there is a statistically significant positive

relationship between the variables B and SRP; SRP and SC_SRP and a statistically

significant negative relationship between the variables B and distance from nearest city

(DNC); SRP and DNC. The relationship between SRP and SC_SRP shows that sites with

SC_SRP values closest to the even contribution figure (ECF) show the smallest SRP

values. An increase in the magnitude of SC_SRP showed an increase in SRP particularly

when SC_SRP is positive. Regression analysis suggests that there is a moderate correlation

between SRP:B and SC_SRP that is significant at P = 0.05. The model produces

predictions of dominant P source that agrees with both tests and outlines any sites that vary

away from the norm. The most promising method explored is by multiple regression

analysis of SRP;B and B in predicting SC_SRP values, there is a strong positive

correlation. Estimated SC_SRP (eSC_SRP) values produced from the regression equation

were correlated with actual SC_SRP values using spearman’s rho and found the

relationship to be statistically significant at P = 0.001. Alternative methods using export

coefficients are too complex for reliable predictions or are too basic and produce unreliable

predictions. This test is significant and meets Water Framework Directive (WFD)

requirements of being simple, quick and cost effective.

Key words: Soluble reactive phosphorus, Boron, Water Framework Directive, SC_SRP,

Eutrophication, Management strategies.

Word Count: 9737

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CONTENTS Page Number

Title page

Declaration

Abstract……………………………………………………………………………1

Contents…………………………………………………………………………...2 - 4

Abbreviations……………………………………………………………………..5

Figures…………………………………………………………………………….6

Tables……………………………………………………………………………..7-8

Acknowledgments……………………………………………………………......9

1. INTRODUCTION………………………………………………………….....10-12

1.1 General……………………………………………………………………….10-11

1.2 Aims and Objectives………………………………………………………....11-12

1.3 Hypotheses…………………………………………………………………...12

2. LITERATURE REVIEW…………………………………………………......13-22

2.1 Phosphorus in England’s Surface Waters………………………………...….13

2.2 The European Water Framework Directive……………………………...…..14-15

2.3 Phosphorus and Eutrophication………………………………………...…....15-17

2.4 Sources of Phosphorus……………………………………………………….18-19

2.5 Methods of Phosphorus Source Determination………………………...……19-22

2.5.1 Export Coefficient Model…………………………………….……20-21

2.5.2 Boron as a Marker for Sewage Effluent……………………….…..21-22

2.5.3 Seasonal Variability of Phosphorus…………………………….….22

3. METHODOLOGY…………………………………………………………....23-39

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3.1 Site Description……………………………………………………………..23-24

3.2 Collection of Data…………………………………………………………..25

3.3 Sampling…………………………………………………………………….26-37

3.4 Chemical Analysis – Boron…………………………………………………38

3.5 Nutrient Analysis – Soluble Reactive Phosphorus………………………….38

3.6 GQA Standards……………………………………………………………...39

3.7 Result Analysis……………………………………………………………...39

4. RESULTS…………………………………………………………………….40-59

4.1 General Results……………………………………………………………...40

4.2 Soluble Reactive Phosphate Results………………………………...………41-42

4.3 Boron Results………………………………………………………………..42-43

4.4 Variables Statistics…………………………………………………………..44-50

4.4.1 B and SRP…………………………………………………………44-45

4.4.2 SRP and SC_SRP………………………………………………….45-47

4.4.3 B and Urban Land Use (DNC)……………………………...….....47-48

4.4.4 SRP and Urban Land Use (DNC)…………………………...…….48-49

4.4.5 Multiple Regression of SRP with B and DNC……………...…….50

4.5 Method Statistics……………………………………………………...….....51-59

4.5.1 SRP:B and SC_SRP……………………………..........................51-52.

4.5.2 B and SC_SRP………………………………..............................53-55

4.5.3 Multiple Regression of SC_SRP with SRP:B and B………….....55-56

4.5.4 eSC_SRP and SC_SRP……………………………………..........57-59

5. DISSCUSSION……………………………………………………………....60-67

5.1 Variable Statistics…………………………………………………………...60-65

5.1.1 B and SRP………………………………………………………....60-62

5.1.2 SRP and SC_SRP………………………………………………....62-63

5.1.3 B and SRP Response to Urban Land Use (DNC)……………..….64-65

5.2 Method Analysis………………………………………………………...…..65-67

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5.2.1 SRP:B and SC_SRP………………………………………………..65-66

5.2.2 B and SC_SRP……………………………………………………..66

5.2.3 Multiple Regression of SC_SRP with SRP:B and B……………....67

6. CONCLUSION………………………………………………………………..67-68

7. LIMITATIONS AND IMPROVEMENTS……………………………………68

8. APPENDICES………………………………………………………………...69-87

8.1 Primary Data…………………………………………………………………69

8.2 Secondary Data………………………………………………………………70-85

8.3 Other…………………………………………………………………………86-87

Fieldwork Risk Assessment Form…………………………………………........88-92

Laboratory use form

9. BIBLIOGRAPHY …………………………………….…………………..…93-99

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Abbreviations

AES Atomic Emission Spectrometer

B Boron

CIEEM Chartered Institute of Ecology and Environmental Management

DNC Distance from Nearest City

EA Environment Agency

ECF Even Contribution Figure

eSC_SRP Estimated Seasonal Change of Soluble Reactive Phosphorus

EU European Union

ICP – MS Inductively Coupled Plasma Mass Spectrometer

ICP – OES Inductively Coupled Plasma Optical Emission Spectrometry

LOIS Land – Ocean Interaction Study

NRBD Northumbria River Basin District

P Phosphorus

SC_SRP Seasonal Change of Soluble Reactive Phosphorus

SRP Soluble Reactive Phosphorus

SRP:B Soluble Reactive Phosphorus to Boron Ratio

STWs Sewage Treatment Works

UK United Kingdom

u/s Upstream

WFD Water Framework Directive

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Figures

Figure Description Pg.1 The proportion of waters in the NRBD in good condition. 102 Target phosphorus concentrations for river in England and Wales with

suggested applications for the type of river16

3 Export coefficient figures for different land uses to be used in P source determination methods

20

4 A map of Northumbria outlining the four regions within the district, the change from rural in the west to urban in the east and the major rivers in the NRBD

24

5 A site map with corresponding site numbers. Shows the general relief of the catchment area.

27

6 A site map with corresponding site numbers. Illustrates the rural and urban land use areas

28

7 Site 1. Pauperhaugh, River Coquet 298 Site 2. Clap Shaw, River Derwent 299 Site 3. Middleton Wood, River Leven 3010 Site 4a. Jesmond Dene, River Ouseburn 3011 Site 4b. Three Mile Bridge, River Ouseburn 3112 Site 5. South Park Darlington, River Skerne 3113 Site 6a. u/s Birtley STW, River Team 3214 Site 6b. Lamesley, River Team 3215 Site 7a. Dinsdale, River Tees 3316 Site 7b. Dent Bank, River Tees 3317 Site 8. Wark, River North Tyne 3418 Site 9. Alston, River South Tyne 3419 Site 10a. How Burn, River Wansbeck 3520 Site 10b. Mitford, River Wansbeck 3521 Site 11a. Bishop Auckland, River Wear 3622 Site 11b. Cocken Bridge, River Wear 3623 Site 11c. Stanhope, River Wear 3724 Site 11d. Shincliffe Bridge, River Wear 3725 The graph of the linear regression model between SRP (mg/l) and B (mg/l) 4526 The graph of the linear regression model between SC_SRP (mg/l) and SRP

(mg/l)47

27 The graphs from exponential curve estimation between B (mg/l) and DNC (km) and between SRP (mg/l) and DNC (km)

49

28 The graphs from exponential curve estimation between B (mg/l) and DNC (km) and between SRP (mg/l) and DNC (km)

49

29 The graph from linear regression between SC_SRP (mg/l) and SRP:B 5230 The graph from linear and cubic regression between B (mg/l) and SC_SRP

(mg/l)55

31 The graph from linear regression between eSC_SRP (mg/l) and SC_SRP (mg/l) 5932 A stacked histogram showing the relationship between SRP and B as the

volume of sewage effluent increases60

33 A map of past coal mining areas in the NRBD. Represented by the semi-transparent area within the black margins

62

34 Diagram and equations to illustrate how changes in concentration vary in magnitude depending on the initial concentration

63

35 4 graphs to show the concentrations of TP when point source contributes (a) 0 – 25% (b) 25- 50 % (c) 50 – 75% and (d) 75-100% of the phosphorus load

87

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Tables

Table Description Pg.1 A table of sampled rivers and the sites along them 262 GQA classification table for phosphates 393 Table of sampling sites and their DNC figures 404 Table of sampling sites and their SRP concentrations 415 Table of sampling sites and their SC_SRP values 426 Table of sampling sites and their B concentrations 437 Model summary of SRP and B 448 ANOVA output of SRP and B 449 Model summary of SC_SRP and SRP 4610 ANOVA output of SC_SRP and SRP 4611 Model summary of B and DNC 4712 ANOVA output of B and DNC 4813 Model summary of SRP and DNC 4814 ANOVA output of SRP and DNC 4815 Model summary of SRP and the variables B and DNC 5016 ANOVA output of SRP and the variables B and DNC 5017 Coefficients output of SRP and the variables B and DNC 5018 Model summary of SRP:B and SC_SRP 5119 ANOVA output of SRP:B and SC_SRP 5120 Coefficients output of SRP:B and SC_SRP 5121 Model summary of B and SC_SRP 5322 ANOVA output of B and SC_SRP 5323 Model summary of B and SC_SRP 5424 ANOVA output of B and SC_SRP 5425 Model summary of SC_SRP and the variables SRP:B and B 5626 ANOVA output of SC_SRP and the variables SRP:B and B 5627 Coefficients output of SC_SRP and the variables SRP:B and B 5628 Sample sites and their recorded SC_SRP values and their eSC_SRP values 5729 Model summary of eSC_SRP and SC_SRP 5830 ANOVA output of eSC_SRP and SC_SRP 5831 Correlations output from Spearman’s rho correlation analysis between

eSC_SRP and SC_SRP59

32 Sample sites and all their data for the variables: B, SRP, P, SC_SRP and DNC 6933 Shincliffe Bridge, River Wear and the secondary data obtained from the EA 7034 Cocken Bridge, River Wear and the secondary data obtained from the EA 7135 Bishop Auckland, River Wear and the secondary data obtained from the EA 236 Stanhope, River Wear and the secondary data obtained from the EA 7337 Alston, River S Tyne and sample site Wark, River N Tyne and the secondary

data obtained from the EA74

38 Mitford, River Wansbeck and sample site u/s How Burn confluence, River Wansbeck and the secondary data obtained from the EA

75

39 Pauperhaugh, River Coquet and the secondary data obtained from the EA 7640 Clap Shaw, River Derwent and the secondary data obtained from the EA 76-7741 u/s Birtley STWs, River Team and the secondary data obtained from the EA 77

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42 Lamesley, River Team and the secondary data obtained from the EA 78-7943 Dent Bank, River Tees and the secondary data obtained from the EA 7944 Dinsdale, River Tees and the secondary data obtained from the EA 8045 Jesmond Dene, River Ouseburn and the secondary data obtained from the EA 8146 Three Mile Bridge, River Ouseburn and the secondary data obtained from the

EA82

47 South Park Darlington, River Skerne and the secondary data obtained from the EA

83

48 Middleton Wood, River Leven and the secondary data obtained from the EA 84-8549 Data on water composition of B and SRP immediately after STWs 8550 Key pressures being applied on phosphorus control in rivers 8651 Summary of the NRBD sectors identified that are preventing good status to be

reached87

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Acknowledgments

I would like to thank many people for making this dissertation possible.

I wish to thank Emma Pearson and Simon Drew for allowing me to use the laboratory and

its analysis equipment. I wish to thank Andy Large for giving me guidance and keeping me

calm at particular times of worry.

Thanks goes to Doug Meynell of Lanes PLC for making the connection with Northumbria

Water and to Lanes Group plc for funding the boron analysis.

Thanks go to the Northumbria Water laboratories for analysing the boron.

Final thanks go to my family for continuous support.

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1. Introduction

1.1 General

The Water Frame Directive (WFD) was officially published in 2000 by the EU with an aim

to achieve good water status in all European waters by 2015 (Hering et al., 2010; Mostert,

2003). In the directive phosphorus is targeted in particular because of its relationship with

eutrophication as the key limiting nutrient (EA, 2012; Hilton et al., 2006; Jarvie et al.,

2006). Eutrophication of waters requires a lot of attention as it causes adverse effects on

water use and its social benefits (EA, 1012) as well as the detrimental effect it can have on

river ecology health (Hilton et al., 2006). In Northumbria the location of this study, rivers

suffer from poor ecology more than any other surface water body (figure 1) outlining the

importance of river management strategies with respect to this study.

The WFD requires a technique that is simple, reliable and cost effective so that mitigation

strategies can be put in place to improve the rivers in time for the 2015 deadline (EA,

2000; Hilton et al., 2002; May et al., 2001; Neal et al., 2008). Methods to improve to

phosphorus levels in rivers include an increase in tertiary treatment in STWs for rivers

10

Figure 1 The proportion of waters in the NRBD in good condition. From EA (2013)

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affected by point source inputs, or riparian buffer strips and improved farming practices

(Bowes et al., 2008). Management strategies can only be successfully administered when

the relative contributions of point and diffuse sources of phosphorus is calculated (Bowes

et al., 2008).

Research into finding a method that meets the WFD requirements has seen the increase in

studies using boron as a marker of sewage effluent to be used in conjunction with

phosphorus source determination methods (Jarvie et al., 2002; Jarvie et al., 2006; Neal et

al., 2010). It was Neal et al. (1998) that proposed the development of techniques using

boron as an indicator is a big step towards the development of management strategies

before the WFD was even installed. However this project aims to move past the

restrictions of boron as a marker for sewage effluent. Instead it intends to offer an

alternative approach to determining the sources of phosphorus with boron at the heart of

the investigation.

1.2 Aims and objectives

Aims - To produce a simple but effective method of determining the dominant source

of phosphorus for rivers, using boron based methods in relation to the

seasonal variation of phosphorus method.

To confirm findings in previous studies of the relationship between soluble

reactive phosphorus and boron, and that B is a useful marker of sewage

effluent.

Objectives – Develop a suitable methodology for collection and detection of appropriate

water characteristics at sites that will support the study, through literature and

Environment Agency (EA) water quality sites.

Choose suitable techniques to analyse the water samples in the laboratory that

will best support the aims of the study.

Use suitable statistical techniques to assess the relationship between boron

and soluble reactive phosphorus to accept or reject the null hypothesis.

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Use suitable statistical techniques to test the effectiveness of the study

techniques against an agreed upon selected technique for determining

dominant phosphorus source from literature, with an aim to accept or reject

the null hypothesis.

1.3 Hypotheses

1. H0 = There is no statistically significant relationship between soluble reactive

phosphorus and boron.

2. H0 = There is no statistically significant relationship between the ratio of soluble

reactive phosphorus with boron and the seasonal variability of soluble reactive

phosphorus.

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2. Literature Review

2.1 Phosphorus in England’s surface waters

The EA recognises that phosphorus is the most common failing WFD element in England.

There have been significant reductions in phosphorus post 1990 with the major reductions

in STW loading (EA, 2013). The percentage of rivers with high phosphorus levels has

fallen from 69% in 1990 to a current 45% (EA, 2013). However, of these 45%, half are

more than 2.5 times over the ‘good status’ level and a further quarter of rivers are more

than 5 times over the level (EA, 2013). The poor phosphorus levels have the biggest

impact on England plant and animal communities, and the natural processes, structure and

function of ecosystems in the UK.

In England the main source of river phosphorus is from sewage effluent. The EA (2013)

estimates that it contributes 60-80% of the total phosphorus and that the agricultural sector

adds 25% of the total phosphorus found in England’s waters. The relative proportion of the

two depends on the catchment land use. Heavily urban river basins like the Thames district

produces enough domestic waste to fill 900 Olympic sized swimming pools every day

(EA, 2013), whereas, an intense agricultural basin like the Anglian River Basin with a

population of only 7.1 million will have less impact on river phosphorus from sewage

effluent and more from agricultural practice (EA, 2013). On average, detergents account

for 16% of the total phosphorus added by sewage, with food and drink only making up 6-

10% of sewage (EA, 2013). Phosphorus stripping of the sewage is unfortunately not

enough to keep the river phosphorus levels below the ‘good status’ standard as nationally

the EA (2013) estimates that there are 100,000 misconnections in the English sewer works.

The misconnections take foul waters containing high phosphorus loads and export them

into freshwater systems instead of exporting them to be treated. During times of heavy

precipitation foul water sewers can also fail and overflow into safe water sewers and again

be exported to freshwater systems increasing the phosphorus load. England also has 1500

km2 of road surfaces that produce urban run off at times of high precipitation, dumping

contaminants and phosphorus directly into the rivers (EA, 2013). Phosphorus is the main

issue for freshwater river systems in England and this is reflected in the WFD.

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2.2 The European Water Framework Directive (WFD)

The WFD was adopted in 2000 by the EU in an attempt to unite the water policies and

regulations of the European nations, outlining the general rule that humans can take

advantage of water resources as long as the ecology of the system is not significantly

harmed (Dworak et al., 2005). The establishment of the WFD has provided the most

significant development towards the improvement of surface waters in Europe (Hilton et

al., 2006). Mostert (p.523, 2003) outlines that the specific aims of the directive are:

1. To reduce pollution of surface and groundwaters by reducing inputs of selected and

hazardous priority substances.

2. To prevent further deterioration of water bodies.

3. To promote sustainable water use.

4. To reduces the effects of extreme water conditions; flooding and droughts.

The overall objective was to achieve a ‘good water status’ by 2015 (Mostert, 2003). To

achieve the aims a management strategy was put in place. The EU enforced a change in the

way that water quality was viewed, from an individual chemical assessment of the river to

a wider concept of the river basin ecology (Bateman et al., 2006). The individual basins

could be assigned an authority and produce an individual management plan to take the

region from identifying the health status to identifying the success or failure of the

management scheme in 2015 (Allan et al., 2006; Mostert, 2003).

To support the aims of management schemes it required the establishment of monitoring

programmes divided into three categories (Dworak et al., 2005):

Surveillance monitoring- to assess the long term changes in river health

Operational monitoring- to be used as an extra measure for those rivers at risk of

not meeting the ‘good status’ by 2015.

Investigative monitoring- to be used when the standards are not met for an

unexplained reason.

For each monitoring type an assessment of biological qualities, chemical qualities and

hydromorphological qualities are produced (Allan et al., 2006). Operational monitoring has

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been the main focus by the EU nations with 17 of the 25 states favouring operational

monitoring over surveillance monitoring (Hering et al., 2010) indicating that the main

efforts are focused primarily on the restoration side of the WFD. With the increase of

monitoring there is a need to improve the efficiency of monitoring. Monitoring tools must

advance to provide the large amount of data required, at a low cost and within a suitable

time frame (Allan et al., 2006). The technical advancement could involve developing tools

that record river data on site (Allan et al., 2006) however such tools may be able to record

levels of phosphorus but will be unable to determine the source without further

information. The aim of this work could provide a suitable alternative for this situation

with particular beneficial qualities for investigative monitoring. Current methods that have

been developed are criticised for being too complex in their aim for perfection (Hering et

al., 2010) instead of providing a quick simple method to show the appropriate direction

that measures should be taken like this paper aims to do.

Although the methods for implementing the WFD are still being decided upon, the WFD

has started the process of standardised European water enforcements including the way

that river systems are approached, monitored and managed (Hering et al., 2010). The

deadline of 2015 is ambitious but it has made EU nations put time and effort into the

process that otherwise wouldn’t have happened (Jones & Schmitz, 2009). Without the

increase in river monitoring the secondary data for this paper would not be available, or

available for other studies.

2.3 Phosphorus and eutrophication

Phosphorus is a high priority substance addressed in the WFD because of its association

with eutrophication and the harmful effects like nuisance phytoplankton it brings (Jarvie et

al., 2006). Phosphorus is an unsustainable rock that is mined for fertilisers, detergents and

other products (EA, 2002). Phosphorus can take different forms within the water column

varying between organic or inorganic and particulate or dissolved (Jarvie et al., 2005).

However the most abundant form in rivers is SRP averaging 67% of the total phosphorus

(Jarvie et al., 2006). The most eutrophic plant species take up SRP from the water column

suggesting it is the main form to focus on in studies regarding eutrophication and nutrients

(Hilton et al., 2006)

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Eutrophication has been recognised as an international concern since the 1990s (EA, 2012)

and has been extensively linked with phosphorus as the key limiting nutrient in studies

(EA, 2012; Hilton et al., 2006; Jarvie et al., 2005; Jarvie et al., 2006; Mainstone and Parr,

2002). SRP was even used by the EA (2000) to set the guidelines for good health for

different river types (figure 2). Studies taken by the EA (2012 and 2002) showed that river

integrity and phosphorus were negatively correlated as well as a strong positive correlation

between planktonic algae and phosphorus enrichment in large rivers.

Eutrophication is rarely a natural phenomenon but with anthropogenic influences it can

cause the shift from macrophytes to algae dominance, stimulate the excessive growth of

the algae, lower the dissolved oxygen content of the water column, promote blue green

cyanobacteria growth and increase the turbidity of the water (Hilton et al., 2006). 50% of

failing lakes and 60% of failing rivers in the US are due to eutrophication; however on

average the amount of suspended algae in lakes is significantly higher than in rivers

(Smith, 2003). Smith (2003) suggests that this is because of the velocity of the flow but in

Young et al. (1999) study they found that the relationship between flow and suspended

algae was not significantly connected and went further to find that phosphorus wasn’t the

limiting factor as it was readily available. The limiting factor of eutrophication may be due

to environmental factors of light intensity, turbidity, temperature or the availability of other

important nutrients (Mainstone and Parr, 2002).

Throughout the extensive studies on river eutrophication it is the new paradigm suggested

by Hilton et al. (2006) that appears the most likely: it is not the velocity of the flow that is

important but the duration. Reynolds (1984) suggests that it takes two days for algae cells

to replicate so in the context of a lake, algae blooms will be a possibility when retention

16

Figure 2 Target phosphorus concentrations for river in England and Wales with suggested applications for the type of river. From EA (2000)

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time is longer than 4 days. However as inoculum of suspended algae is minimal at the

source of the river the duration time must be greater than 4 days, promoting benthic algae

growth in smaller rivers as opposed to phytoplankton (Hilton et al., 2006). Conversely,

with rivers that have a long duration time due to their large lengths and depths, there is

time for sufficient replications of suspended algae to promote growth and make it the

dominant plant species. In general, phytoplanktonic species will increase with distance

downstream (Hilton et al., 2006). With the similarities between retention time and duration

time the eutrophic processes of lakes and some rivers could be looked at in a similar way

(Smith, 2003) proven by Reynolds et al. (1998) when a minor adaptation of the PROTECH

lake model was used to predict potamoplankton on the River Thames.

The undesirable effects of eutrophication are most prominent during the low summer flows

(Jarvie et al., 2006). These outcomes can be separated into environmental effects and social

effects. With increases in turbidity and phytoplankton the water column can potentially

become anoxic and cause mass fish deaths (Withers and Jarvie, 2008). If eutrophic blue-

green cyanobacteria are formed it can release deadly toxins again killing fish and reducing

biodiversity (Hilton et al., 2006).

Socially eutrophication disturbs angling, conservation interests, navigation and, because of

its unattractive aesthetics, it affects tourism and water front property prices (EA, 2012).

Further economic consequences include algae growth within reservoirs increasing the cost

of water cleansing to achieve drinking water standards and increasing the risk of flooding

by the stimulated growth of excessive rooted plants (Hilton et al., 2006).

Hilton et al. (2006) estimate that it costs £100 million per year to address the effects of

eutrophication on society. With the WFD in place it is vitally important that it is followed

through to reduce these costs. Eutrophication is clearly an expensive issue highlighting the

importance of this paper.

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2.4 Sources of phosphorus

Sources of phosphorus can be natural or anthropogenic. Natural sources can be from soil

weathering, riparian inputs, fish migration and bank erosion (Walling et al., 2008; Withers

and Jarvie, 2008). Furthermore, atmospheric sources of phosphorus in precipitation are

small only reaching 10 mg/l (Wood et al., 2005). Natural sources provide small amounts of

phosphorus and in the non-bioavailable form of particulates so it can be eliminated as a

threat to stream health (Withers and Jarvie, 2008). Wood et al. (2005) proved this by

finding no evidence to support bank erosion inputs of phosphorus on the River Taw.

Anthropogenic sources can be divided into three categories: point, intermediate and diffuse

sources (Neal et al., 2005). Sewage treatment works (STWs) are the main point sources.

STWs discharge effluent rich in detergents, food and phosphorus from lead dosing directly

into water courses (EA, 2012; Neal et al., 2005). SRP is the dominant form of phosphorus

emitted into the rivers from STWs, providing immediate availability for plant use

(Mainstone and Parr, 2002). A combination of continuous SRP inputs throughout the year

and minimum dilution at low flows in summer make a high risk of eutrophication (Bowes

et al., 2005). The concentration of phosphorus in sewage effluent depends on the scale of

treatment the STWs apply, the size of the population it provides for and the industrial

activity within the sewered area (Withers and Jarvie, 2008). After primary, secondary and

tertiary treatment the average phosphorus concentration lies between 1 and 20 mg/l

(Withers and Jarvie, 2008).

Future population growth will exacerbate the risk of eutrophication with the increase in

sewage load, particularly in areas already exceeding phosphorus WFD standards (EA,

2002). The WFD estimates that there will be 650 STWs with tertiary treatment serving 24

million people by 2015 (EA, 2002).

Intermediate sources include run-off from urban land uses like roads and cities, and

phosphorus from septic tanks (Jarvie et al. 2006). The majority of UK rural areas rely on

septic tanks as their sewage removal mechanism (Wood et al., 2005). Septic tanks

discharge onto areas of low soil saturation, however in heavy rainfall events this can be

washed into river systems as a source of phosphorus (Neal et al., 2008). Furthermore areas

relying on older septic tanks may release their waste directly into rivers, or have an

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irregular and large release of effluent leading to high soil and river phosphorus

concentrations (Withers and Jarvie, 2008).

Urban run-off mobilises sources of phosphorus such as dead vegetation, litter, industrial

matter and disturbed soils during high precipitation events. Although the process is

intermittent it contributes a rapid supply of phosphorus directly into the river course (Neal

et al., 2005).

The WFD has caused an increase in tertiary treatment of sewage. Jarvie et al. (2006)

estimated that agriculture contributed to 50% of the annual river phosphorus in the UK. It

is the application and removal of fertilizers from agricultural lands that defines it as a

diffuse source (Neal et al., 2005). The addition of phosphorus from diffuse sources is very

seasonal (Mainstone and Parr, 2002). Cooper et al. (2002) suggested that for the Thames

catchment 66-84% of the annual diffuse phosphorus load was transported during the winter

months. The majority of the load is delivered as non-bioavailable particulates (Mainstone

and Parr, 2002) so may not be the main contributor to eutrophic conditions unlike STWs.

The quantification of phosphorus loads from the highly variable catchment sources is

difficult and impossible to be 100% accurate (Bowes et al., 2005). However it is possible

to identify the key contributing source and reduce risks arising from phosphorus

enrichment.

2.5 Methods of phosphorus source determination

Producing methods to assess the relative contributions of phosphorus to rivers has become

increasingly important since the introduction of the WFD (Bowes et al., 2008; EA, 2000;

Hilton et al., 2002; Neal et al., 2008). The required method needs to be simple, low cost

and accurate enough to assess which source needs to be addressed (Hilton et al., 2002). It

is the development of these methods that will ensure a sustainable, affordable success of

the WFD goals (Jarvie et al., 2002).

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2.5.1 Export coefficient model

The most common method being developed is the export coefficient model, pioneered by

Johnes (1996) before the instalment of the WFD. Since Johnes (1996), the method has

been studied and improved to attempt to reach WFD standards. In 2001 studies (May et al.;

Wang) used aerial imagery to measure the extent of different land uses in the catchment

and assigned particular coefficients (figure 3) for their contribution of phosphorus to the

river. The export coefficients were based on an annual study of run offs or from scaling up

results from small tests on each land use (Hilton et al., 2002). Hilton et al. (2002)

attempted to reduce the complexity by assigning predesigned uncalibrated coefficients

based on generic land uses. The relative contribution of diffuse sources was calculated

based on the area of land uses upstream of STWs and urban influence and point sources

downstream (Hilton et al., 2002). Bennion et al. (2005) progressed the method further by

applying export coefficients to point loading by STWs. The volume of phosphorus loaded

was estimated by a population in the catchment coefficient (Wood et al., 2005).

There are a large number of water quality models but they do not meet the requirements of

the WFD because they are too complex, require too much data, are time consuming or are

unreliable (EA, 2000). For the UK the main priority is estimating the influence of STWs.

20

Figure 3 Export coefficient figures for different land uses to be used in P source determination methods. From May et al. (2001)

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Methods that require data on direct sewage effluent are rare because of the inaccurate or

sparse data collected on effluent composition (Boorman, 2003; Wood et al., 2005).

Producing export coefficients for STWs like in the Bennion et al. study (2005) does not

distinguish between houses that are served by STWs and those that rely on septic tanks

(Wood et al., 2005), it does not account for varying levels of effluent treatment from STWs

or for the transfer of sewage from one catchment into another (Wood et al., 2005). Without

these complications there is also no universal figure for phosphorus levels in sewage

effluent. In the original Johnes (1996) study a coefficient of secondary treated effluent was

0.38 kgP/capita/y whereas in the Carvalho et al. (2003) study the value ranged from 0.14-

1.55 kgP/capita/y.

To produce accurate models to predict diffuse inputs it requires even larger amounts of

data (Bowes et al., 2008; Hilton et al., 2002; Wang, 2001): fertiliser use, livestock

numbers, stock headage, type of agriculture, meteorology and several years of water

monitoring data to establish a calibrated set of coefficients. Data that is rare and requires

years of research. In the Hilton et al. (2002) study the uncalibrated export coefficients

could not be reliable as they may not have been appropriate for the studied catchment

(Bowes et al., 2008) indicating that the method is even more complicated to try

simplifying. Most models are not acceptable for regular monitoring on a lot of catchment

sites (EA, 2000).

2.5.2 Boron as a marker of sewage effluent

The use of boron in aquatic investigations was pioneered by Neal et al. (1998) in the Land-

Ocean Interaction Study (LOIS) (Jarvie et al., 2002). Boron is an element that is present in

aquatic ecosystems from both natural and anthropogenic sources (Fox et al., 2000).

Sewage effluent is rich in boron as it is made up of boron-containing substances (Jarvie et

al., 2002; Jarvie et al., 2006; Neal et al., 1998; Neal et al., 2010): detergents, washing

powders, soaps and cleaning products. In water bodies boron is found in the stable

unreactive form borate because of its high affinity for oxygen (Jarvie et al., 2002; Neal et

al., 1998; Wyness et al., 2003). The chemically unreactive borate was identified by the

LOIS studies as a useful marker for sewage because of its stable form in water and its

strong correlation with sewage phosphorus (Jarvie et al., 2006; Neal et al., 1998; Neal et

21

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al., 2005). These characteristics could prove useful in methods to determine sources and

impacts of phosphorus (Neal et al., 2010).

Natural sources of boron from weathered igneous rocks and leaching of salt deposits can

produce a background source that need to be taken into account when using boron as an

effluent marker (Jarvie et al., 2006; Neal et al., 1998; Neal et al., 2005). With this study the

background reading is minimal (<10 ug/l) because of the areas predominant sedimentary

geology and minimal saline deposits (Neal et al., 1998).

Neal et al. (1998) believed that the use of boron in studies of this kind is a key step in

improving management strategies for water quality. Boron has been used as an indicator or

facilitator in studies on hydrodynamic behaviour of STWs (Fox et al., 2000), sewage and

other river inputs (Jarvie et al., 2002) and the impact of tertiary treatment on sewage

effluent (Neal et al., 2000). In studies that have limited access to sewage effluent records or

require a more reliable source of data than export coefficients, boron as a tracer is a

sensible option (Neal et al., 1998).

2.5.3 Seasonal variability of phosphorus

With every model associated with phosphorus inputs there has been one general conclusion

relating the seasonal variability of phosphorus with its appropriate source. Rivers with

predominantly point source inputs of phosphorus experience the highest concentrations

during the summer months when dilution is at its lowest whereas rivers that are

predominantly diffuse source influenced have the highest concentrations in the winter

months when rainfall and flow are highest (Bowes et al., 2005; Bowes et al., 2008; Cooper

et al., 2002; Jarvie et al., 2002; Jarvie et al., 2006; May et al., 2001; Neal et al., 1998;

Nishikoori, 2011; Wood et al., 2005).

There is no unified approach of monitoring source inputs of phosphorus in to rivers

(Wyness et al., 2003) but the development of methods is essential in the aim to control

eutrophication (May et al., 2001). However we know that using estimates from catchment

uses will not be as reliable as actual river monitoring (Bowes et al., 2008). Boron could

play a key role in future methods, and this study aims to use it in conjunction with the only

agreed upon method of seasonal variability.

22

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3. Methodology

3.1 Site Description

The rivers being used for testing whether B can be used to infer STW inputs are located in

the Northumbria River Basin District (NRBD). The NRBD covers 9029 km2 and is home

to 2.5 million people (EA, 2013). The area is comprised of Northumberland, County

Durham, parts of North Yorkshire and Cumbria. Over the large area of land there is a great

variation in land uses and land types: industrial, urban regions, hills and valleys in the

Northumberland National Park and Pennine regions and coastal features along the east

side. 67% of the land is used for farming or forestry and only 693km2 of the land is urban

(EA, 2007). Towards the west, away from the coast and urban cities the NRBD has a

predominantly rural setting with heather moorland coverage. In the north and west areas

with higher reliefs there is extensive sheep grazing. As you move further east and south to

the lower flatter lands the land use changes to arable or mixed farming practices. Mining

and quarrying were once wide spread in the district however industry and manufacturing

still remains important in the industrial cities to the east. The main industries are chemical,

petrochemical, metal sectors and transport sectors (EA, 2013).

The human influence over the land produces a variety of different methods that can

influence or harm freshwater ecosystems. Out of the 362 rivers, 42% are deemed to be in

moderate condition (EA, 2007). 17% of the NRBD freshwater failures are due to sewage

inputs from industry, 16% from rural pollution and 6% from urban sewage system failure

(EA, 2013). In 2015 the government are aiming to improve the sewer networks to reduce

failing during high rainfall, if B can be used to infer P inputs selection of areas to improve

can be identified better and quicker. Furthermore, with a predominantly Carboniferous and

Cretaceous sedimentary bedrock the NRBD has low background B concentration making it

the perfect site to test for relationship between B and water quality (Neal et al., 1998).

23

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24

Figure 4 A map of Northumbria outlining the four regions within the district, the change from rural in the west to urban in the east and the major rivers in the NRBD. From EA (2013)

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3.2 Collection of data

To assess whether a B:P ratio could be used as a method of river nutrient analysis it

requires both primary and secondary data. Secondary data was supplied by previous

samples collected by the Environment Agency at the sites specific to the investigation

(tables 33-48). The samples were tested for orthophosphates. The data provided was

reduced to leave only data that met the required categories: data post 01/01/1995, data

taken from the summer months of June, July and August, data taken from the early winter

months of December and January. The data restrictions were put in place to avoid using

out dated information and to provide the seasonal change in orthophosphates used an

analogue for point source determination method comparisons.

Rivers and sites for primary data were selected by following principles needed to assess the

effectiveness of the proposed method. The rivers required:

1. A broad range of phosphate input methods.

2. A large influence on the overall freshwater health of the NRBD.

3. A frequent monitoring programme.

A general rule that as distance downstream increases, urban land use increases and there is

a larger point source input of phosphates was used to help select sites along the rivers to

meet the criteria of the first principle. Using the secondary data provided by the

Environment Agency in conjunction with google maps appropriate sites were selected

based on the 3 principles. Time restraints and vehicle accessibility also played a part in

finalising the sites.

The primary data collection period took 3 days from 27/11/2013-29/11/2013. This was a

period of constant dry weather which had followed a week of rainfall, allowing the

assumption that the samples were taken under the same conditions. When applying the

‘dilution and drainage’ theory, the data collected would show relatively low

orthophosphate levels in areas affected by point source inputs such as STWs and high

orthophosphate levels in diffuse source affected areas.

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3.3 Sampling

Nineteen sites were chosen for sampling, spanning across eleven rivers in the North East

region of England (table 1). An on-site judgemental approach was taken to decide the

specific sample site. The specific site was selected by: taking time restraints into account,

safety precautions with the relatively high flows, river accessibility and avoiding static or

slow moving sites at the river’s edge as this allows more time for nutrient recycling and

use (Withers and Jarvie, 2008). At each site two 250ml plastic bottle grab samples were

collected, removing all air bubbles from the sample. The samples were placed into dark

storage to avoid adsorption and were put into below 4oC refrigeration at the first

opportunity. Analysis of the water samples was done within a week to keep holding times

to a minimum.

Site number River Location1 Coquet Pauperhaugh2 Derwent Clap Shaw3 Leven Middleton Wood4a Ouseburn Jesmond Dene4b Ouseburn Three Mile Bridge5 Skerne South Park Darlington6a Team u/s Birtley STW6b Team Lamesley7a Tees Dinsdale7b Tees Dent Bank8 North Tyne Wark9 South Tyne Alston

10a Wansbeck u/s How Burn confluence10b Wansbeck Mitford11a Wear Bishop Auckland11b Wear Cocken Bridge11c Wear Stanhope11d Wear Shincliffe Bridge

26

Table 1 A table of sampled rivers and the sites along them.

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27

1

2

3

4a4b

5

6a 6b

7a

7b

8

9

11a

11b

11c 10a 11d

10a10b

Figure 5 A site map with corresponding site numbers. Shows the general relief of the catchment area.

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28

1

10b10a

8

4b4a

6a 6b

2

9 11c

11d

11a

11b

7b

57a

3

Figure 6 A site map with corresponding site numbers. Illustrates the rural and urban land use areas.

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Figure 7 Site 1. Pauperhaugh, River Coquet

Figure 8 Site 2. Clap Shaw, River Derwent

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Figure 9 Site 3. Middleton Wood, River Leven

Figure 10 Site 4a. Jesmond Dene, River Ouseburn

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Figure 11 Site 4b. Three Mile Bridge, River Ouseburn

Figure 12 Site 5. South Park Darlington, River Skerne

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Figure 13 Site 6a. u/s Birtley STW, River Team

Figure 14 Site 6b. Lamesley, River Team

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Figure 15 Site 7a. Dinsdale, River Tees

Figure 16 Site 7b. Dent Bank, River Tees

33

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Figure 17 Site 8. Wark, River North Tyne

Figure 18 Site 9. Alston, River South Tyne

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Figure 19 Site 10a. How Burn, River Wansbeck

Figure 20 Site 10b. Mitford, River Wansbeck

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Figure 21 Site 11a. Bishop Auckland, River Wear

Figure 22 Site 11b. Cocken Bridge, River Wear

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Figure 23 Site 11c. Stanhope, River Wear

Figure 24 Site 11d. Shincliffe Bridge, River Wear

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3.4 Chemical analysis - Boron

There are a few methods that can be used for boron determination; the main two being

spectrophotometric and plasma-source spectrometric approaches. The samples were taken

to Northumbrian Water Scientific Services and an ICP-MS method was used. A plasma-

source method was favoured over AES as it has a higher sensitivity and can detect lower

concentrations of B and favoured over time consuming nuclear methods (Sah and Brown,

1997). The ICP-MS method was preferred to ICP-OES for the same reasons.

ICP-MS used argon induced plasma for sample ionization. The different ions were

detected in the mass spectrometer and a mass number for B was produced. The data was

then calibrated using an internal standard of beryllium as it has the closest mass number to

B and it is simple and efficient (Sah and Brown, 1997). A B concentration was produced in

the form mgl-1.

3.5 Nutrient analysis - soluble reactive phosphates

A HACH Portable Spectrophotometer (DR/2400) was used to measure orthophosphates

using a PhosVer3 ascorbic acid method: determination limits 0.02-2.5 mgl-1 PO43-. The

orthophosphate reacts with molybdate to form a phosphate-molybdate complex. The

ascorbic acid then reduced the complex to emit a moybdemnum blue colour. The intensity

of the blue was measured using method number 490p at a wavelength of 880nm

A 10ml sample cell was filled with the water sample and a PhosVer3 powder pillow was

added to the solution and was capped immediately. The solution was inverted to mix the

contents. The sample was given a two minute reaction time, during which another sample

cell was filled with deionized water and placed into the spectrophotometer to serve as a

standard for comparison. After the reaction time was up the sample was placed in the

spectrophotometer and read giving values in mgl-1 PO43-.

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3.6 GQA standards

Classification for phosphate

Grade boundaries (mg/l) Description

1 <0.02 Very Low2 0.02<P<0.06 Low3 0.06<P<0.1 Moderate4 0.1<P<0.2 High5 0.2<P<1.0 Very High6 >1.0 Excessively High

3.7 Result analysis

The data was subjected to linear regression and curve estimation analysis on SPSS.

Multiple regression was applied to the variables that shared common relationships. The

analysis was split into two sections: statistical tests for the variables used in phosphorus

source determination methods, and statistical tests to examine the relationship between the

investigative methods of phosphorus source determination and the established method of

seasonal variability.

The secondary data was split into summer averages and winter averages. The winter

average was then subtracted from the summer average to produce the seasonal change in

SRP.

Distance data was produced using a map and ruler. Measurements were taken from the

geographical centre of the nearest city to the site location.

39

Table 2 GQA classification table for phosphates

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4. Results

4.1 General results

Sites selected ranged from 3.3 km to 61.8 km distance from the nearest city (DNC). DNC

is used as an estimate of urban influences within the catchment, the larger the distance the

less urban the catchment. With 18 sites within this range there is a variety of scales of

urban influence.

Site Distance from Nearest City km

Coquet at Pauperhaugh 52.5

Derwent at Clap Shaw 38.9

Leven at Middleton Wood 13

Ouseburn at Jesmond Dene 3.3

Ouseburn at Three Mile Bridge 6.5

Skerne at South Park Darlington 23.7

Team u/s Birtley STW 7.9

Team at Lamesley 4.3

Tees at Dinsdale 18.2

Tees at Dent Bank 61.8

N Tyne at Wark 46.2

S Tyne at Alston 59.5

Wansbeck u/s How Burn 25.4

Wansbeck at Mitford 24.7

Wear at B Auckland 39.8

Wear at Cocken Bridge 19.5

Wear at Stanhope 49.9

Wear at Shincliffe Bridge 23

40

Table 3 Table of sampling sites and their DNC figures

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4.2 Soluble Reactive Phosphate results

From 18 sites from 11 rivers in the NRB there are only 7 which fall into phosphate

classification 3 or lower according to GQA classification (table 2). 11 sites have SRP

measurements in the high to very high categories with the River Team at Lamesley

pushing the excessively high boundary with an SRP measurement of 0.95 mg/l (table 4).

From the data for the River Wear there is a clear increase in SRP with reducing DNC. This

relationship applies to all the other rivers with multiple sites.

Site SRP mg/l

Coquet at Pauperhaugh 0.11Derwent at Clap Shaw 0.04Leven at Middleton Wood 0.50Ouseburn at Jesmond Dene 0.40Ouseburn at Three Mile Bridge 0.18Skerne at South Park Darlington 0.43Team u/s Birtley STW 0.49Team at Lamesley 0.95Tees at Dinsdale 0.50Tees at Dent Bank 0.04N Tyne at Wark 0.07S Tyne at Alston 0.04Wansbeck u/s How Burn 0.18Wansbeck at Mitford 0.05Wear at B Auckland 0.06Wear at Cocken Bridge 0.25Wear at Stanhope 0.05Wear at Shincliffe Bridge 0.22

There are 6 sites with a negative value for seasonal change of SRP (SC_SRP). The River

Team at Lamesley has the largest SC_SRP value showing an increase of 0.211 mgSRP/l

from winter to summer. The River Wear shows a negative to positive progression as DNC

decreases SC_SRP increasing from -0.04 at Stanhope to 0.07 at Cocken Bridge.

41

Table 4 Table of sampling sites and their SRP concentrations

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Site Seasonal Changeof SRP mg/l

Coquet at Pauperhaugh -0.049Derwent at Clap Shaw -0.048Leven at Middleton Wood 0.147Ouseburn at Jesmond Dene 0.022Ouseburn at Three Mile Bridge 0.049Skerne at South Park Darlington 0.065Team u/s Birtley STW 0.036Team at Lamesley 0.211Tees at Dinsdale 0.041Tees at Dent Bank -0.016N Tyne at Wark -0.062S Tyne at Alston 0.003Wansbeck u/s How Burn 0.047Wansbeck at Mitford 0.024Wear at B Auckland -0.013Wear at Cocken Bridge 0.070Wear at Stanhope -0.040Wear at Shincliffe Bridge 0.016

4.3 Boron results

The data for 17 of the 18 sites lies within 0.01 – 0.1 mgB/l with the exception to the River

Team at Lamesley that has a significantly bigger value of 0.230 mgB/l. The relationship

between B and distance from nearest city doesn’t quite follow the same pattern as SRP

however over large distances it does have a relative increase with the reducing DNC.

42

Table 5 Table of sampling sites and their SC_SRP values

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Site Boron mg/l

Coquet at Pauperhaugh 0.021Derwent at Clap Shaw 0.021Leven at Middleton Wood 0.039Ouseburn at Jesmond Dene 0.081Ouseburn at Three Mile Bridge 0.086Skerne at South Park Darlington 0.095Team u/s Birtley STW 0.055Team at Lamesley 0.230Tees at Dinsdale 0.052Tees at Dent Bank 0.035N Tyne at Wark 0.074S Tyne at Alston 0.024Wansbeck u/s How Burn 0.037Wansbeck at Mitford 0.010Wear at B Auckland 0.024Wear at Cocken Bridge 0.047Wear at Stanhope 0.031Wear at Shincliffe Bridge 0.050

43

Table 6 Table of sampling sites and their B concentrations

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4.4 Variables statistics

4.4.1 B and SRP

Tables 7 and 8 show the statistical significance between the variables B and SRP. The

SPSS linear regression model gives an R2 output of 0.637 with an estimated error of 0.153,

indicating a strong positive correlation. P = 0.000072 so the predicted values from the

model are statistically significant at the 0.001 level. Furthermore with F(1,16) = 28.08 it

suggests a good fit for the model with the data. From the graph in figure 25 the relationship

is clearly displayed with only 5 sites as partial outliers (Leven at Middleton Wood, Tees at

Dinsdale, Team u/s of Birtley, N Tyne at Wark and Ouseburn at Three Mile Bridge)

leading to the highest values of 0.95 mgSRP/l and 0.23 mgB/l at Lamesley on the River

Team.

Linear regression equation

y = 0.03 + 3.96x

y = SRP

x = Boron

Model Summary

R R

Square

Adjusted R

Square

Std. Error of

the Estimate

.798 .637 .614 .153

The independent variable is B.

ANOVA

Sum of Squares df Mean Square F Sig.

Regression .661 1 .661 28.080 .000

Residual .377 16 .024

Total 1.038 17

The independent variable is B.

44

Tables 7 & 8 The SPSS model summary and ANOVA outputs from linear regression between SRP and B

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4.4.2 SRP and SC_SRP

The statistical analysis results for the relationship between DRP and SC_SRP is shown in

tables 9 and 10. From the model summary (table 9) there is a very strong positive

relationship between the variables with 72% of the variation accounted for by the model (R

= 0.850 and R2 = 0.722). With a standard error result of 0.37 the accuracy of the model is

high. The model is significant at the 0.001 level as p = 0.000008 (table 10).

Linear regression equation

y = - 0.03 + 0.24x

y = SC_SRP

x = SRP

45

Figure 25 The graph of the linear regression model between SRP (mg/l) and B (mg/l)

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As the concentration of SRP increases the SC_SRP increases in magnitude. Furthermore,

the lowest SRP concentrations are when SRP concentrations are highest in winter. When

SRP = 0.125 mg/l the SC_SRP shows no change in concentrations from summer to winter.

Model Summary

R R Square Adjusted R

Square

Std. Error of the

Estimate

.850 .722 .705 .037

The independent variable is SRP.

ANOVA

Sum of Squares df Mean Square F Sig.

Regression .058 1 .058 41.570 .000

Residual .022 16 .001

Total .081 17

The independent variable is SRP.

46

Tables 9 & 10 The SPSS model summary and ANOVA outputs from linear regression between SC_SRP and SRP

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4.4.3 B and urban land use (DNC)

Tables 11 and 12 show the output from exponential curve estimation for B and DNC. The

regression analysis shows how B concentration is affected by the size of urban influences.

From the model summary (table 11) the relationship is a moderate positive exponential

correlation (R = 0.556), the rate of B accumulation increases with DNC decreasing. The

relationship has a p value of 0.17 which is only significant at the 0.05 level, however the

model predictions are still statistically significant.

Model Summary

R R Square Adjusted R

Square

Std. Error of the

Estimate

.556 .309 .265 .615

The independent variable is D_N_City.

47

Figure 26 The graph of the linear regression model between SC_SRP (mg/l) and SRP (mg/l)

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ANOVA

Sum of Squares df Mean Square F Sig.

Regression 2.703 1 2.703 7.143 .017

Residual 6.055 16 .378

Total 8.759 17

The independent variable is D_N_City.

Coefficients

4.4.4 SRP and urban land use (DNC)

The exponential regression model summary (table 13) show a very strong positive

exponential relationship between SRP and DNC with 69% of the variance accounted for in

the model (R = 0.832, R2 = 0.693). From the ANOVA output (table 14) the model has a p

value of 0.000018, indicating significance at the 0.001 significance boundary. The

probability that chance influenced the results is less than 0.1%. A high F(1,16) value

further indicates a strong significant correlation. As DNC decreases SRP increases

exponentially.

Model Summary

R R Square Adjusted R

Square

Std. Error of the

Estimate

.832 .693 .674 .610

The independent variable is D_N_City.

ANOVA

Sum of Squares df Mean Square F Sig.

Regression 13.444 1 13.444 36.084 .000

Residual 5.961 16 .373

Total 19.406 17

The independent variable is D_N_City.

48

Tables 11 & 12 The SPSS model summary and ANOVA outputs from exponential curve estimation between B and DNC

Tables 13 & 14 The SPSS model summary and ANOVA outputs from linear regression between SRP and DNC

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Figures 27 and 28 show the visual correlation of both exponential regressions. The circled

plot on both graphs is the Mitford site on the River Wansbeck. The B and SRP values are

anonymously low for a DNC of 24.7 km. When the site is removed for the analysis the R 2

figure for B rises from 0.285 to 0.309 and the R2 figure for SRP rises even more from

0.693 to 0.772, suggesting that the point is an anomaly.

Comparing the two graphs (figures 17 and 28) it is clear that the rate of exponential growth

is larger in the SRP regression model than in the B model. This suggests that the

accumulation rate of SRP is greater than that for B.

49

Figures 27 & 28 The graphs from exponential curve estimation between B (mg/l) and DNC (km) and between SRP (mg/l) and DNC (km)

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4.4.5 Multiple regression of SRP B and DNC

Multiple regression was applied to B, SRP and DNC to further explore the interactions

between the variables. The model summary (table 15) suggests that the interaction between

the three variables is very strong (R2 = 0.772) with a small standard error for the model

(0.126). The coefficients table (table 17) shows that both B and DNC added to the

statistical significance of the predicted SRP model, as all have P < 0.05. SRP increases

when B increases and when DNC decreases.

Model Summary

Model R R Square Adjusted R

Square

Std. Error of the

Estimate

1 .879a .772 .742 .125614

a. Predictors: (Constant), D_N_City, B

ANOVAa

Model Sum of Squares df Mean Square F Sig.

1

Regression .802 2 .401 25.405 .000b

Residual .237 15 .016

Total 1.038 17

a. Dependent Variable: SRP

b. Predictors: (Constant), D_N_City, B

Coefficientsa

Model Unstandardized Coefficients Standardized

Coefficients

t Sig.

B Std. Error Beta

1

(Constant) .253 .087 2.895 .011

B 2.847 .718 .573 3.968 .001

D_N_City -.006 .002 -.431 -2.981 .009

a. Dependent Variable: SRP

50

Tables 15, 16 & 17 The SPSS model summary, ANOVA and coefficients outputs from multiple regression analysis between SRP and the variables B and DNC

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4.5 Method statistics

4.5.1 SRP:B regression with SC_SRP

The model summary from linear regression (table 18) shows a moderate positive

correlation between the two P source predictive methods (R = 0.525 and R2 = 0.276). The

variance around the model is low as standard error is only 0.06, in combination with a p

value of 0.025 the model is significant at the 0.05 significance boundary. The probability

that chance didn’t influence the results is above 95%..

Model Summary

Model R R Square Adjusted R

Square

Std. Error of the

Estimate

1 .525a .276 .230 .060437

a. Predictors: (Constant), SRP_B

ANOVAa

Model Sum of Squares df Mean Square F Sig.

1

Regression .022 1 .022 6.089 .025b

Residual .058 16 .004

Total .081 17

a. Dependent Variable: SC_SRP

b. Predictors: (Constant), SRP_B

Coefficientsa

Model Unstandardized Coefficients Standardized

Coefficients

t Sig.

B Std. Error Beta

1(Constant) -.023 .025 -.907 .378

SRP_B .011 .005 .525 2.468 .025

a. Dependent Variable: SC_SRP

51

Tables 18, 19 & 20 The SPSS model summary, ANOVA and coefficients outputs from linear regression analysis between SRP:B and SC_SRP

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The graph in figure 29 displays the relationship between the two method models. Lines at y

= 0 and x = 3 have been added. The line y = 0 signifies the point when seasonal difference

changes from a negative to a positive. The line x = 2was selected to show the values of

SRP: B when SC_SRP changes from negative to positive (when y = 0). 83% of the sites

fall within the unshaded areas selected with only 1 of the 3 outlier sites being extreme. The

extreme site is at Pauperhaugh, River Coquet with a SRP:B ratio of 5.238 (SRP = 0.110, B

= 0.021) and a SC_SRP of -0.049 mgSRP/l. The graph shows the largest SC_SRP when

the SRP:B ratio is increasing and when SC_SRP is positive.

Linear regression equation

y = - 0.02 + 0.01x

y = SC_SRP

x = SRP:B

52

Figure 29 The graph from linear regression between SC_SRP (mg/l) and SRP:B. With additional y = 0 and x = 2 lines based on the intersection of the trend line with the ECF of SC_SRP. Shaded red areas illustrate the areas that hold anomalous data.

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4.5.2 B and SC_SRP

Linear

The model summary (table 28) for linear regression between B and SC_SRP suggests a

moderate-strong positive correlation with an R2 value of 0.463. The p value is 0.002 (table

22) suggesting the model is significant at the 0.01 significance boundary. The output

suggests that as B increases there is a statistically significant increase in SC_SRP in the

positive direction.

Model Summary

R R Square Adjusted R

Square

Std. Error of the

Estimate

.681 .463 .430 .038

The independent variable is SC_SRP.

ANOVA

Sum of Squares df Mean Square F Sig.

Regression .020 1 .020 13.806 .002

Residual .023 16 .001

Total .042 17

The independent variable is SC_SRP.

Cubic

The curve estimation model summary (table 23) shows a very strong positive relationship

between B and SC_SRP when a cubic model is applied (R = 0.828 and R2 = 0.619). The

cubic model shows a small standard error value of 0.031 so variance about the model is

small. From the ANOVA table (table 24) the p value is 0.001, so the model is significant at

the 0.001 significance boundary when there is a 99.9% chance that the data was not

influenced by chance.

53

Tables 21 & 22 The SPSS model summary and ANOVA outputs from linear regression between B and SC_SRP

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54

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Model Summary

R R Square Adjusted R

Square

Std. Error of the

Estimate

.828 .686 .619 .031

The independent variable is SC_SRP.

ANOVA

Sum of Squares df Mean Square F Sig.

Regression .029 3 .010 10.206 .001

Residual .013 14 .001

Total .042 17

The independent variable is SC_SRP.

Both linear and cubic regression models were plotted on a graph because although the R2

value for the cubic model is 0.156 higher the F (3, 14) value for the linear model is 13.806

as oppose to the cubic F (1, 16) value 10.206. However because the F values both suggest a

good fit for the data and because of the extremely low p value for the cubic model it is

likely that it is the more accurate model and so represents the relationship between B and

SC_SRP.

Cubic model equation

y = 0.05 + 0.05x – 2.93x2 + 30.44x3

y = Boron

x = SC_SRP

The graph (figure 30) shows the general trend of B increasing as SC_SRP shifts more

positive. However according to the cubic model the level of B remains relatively constant

at 0.5 mg/l between – 0.3 mgSRP/l and 0.7 mgSRP/l of SC_SRP. There are no extreme

outliers but the site at Wark, River N Tyne does fall slightly out. If it was removed from

the regression analysis then the R2 value would rise to 0.779 and P would decrease to

0.000151 whilst keeping the same trend.

55

Tables 23 & 24 The SPSS model summary and ANOVA outputs from cubic curve estimation between B and SC_SRP

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4.5.3 Multiple regression of SC_SRP with SRP:B and B

The multiple regression model summary shows that there is a very strong positive

relationship between the three variables as R2 = 0.742 (table 25) the strongest correlation

out of all the statistical models for method analysis. Both SRP:B and B increase the

statistical significance of the predicted SC_SRP model as all 3 are significant at the 0.001

significance boundary (table 26). There is only 0.01% probability that the relationship of B

and SRP:B with SC_SRP is due to chance. With a variance of only 0.037 (table 25) the

model has a high accuracy. The model suggests a linear relationship that when SRP:B and

B increases the SC_SRP becomes more positive.

56

Figure 30 The graph from linear and cubic regression between B (mg/l) and SC_SRP (mg/l)

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Multiple regression equation

y = - 0.076 + 0.011x1 + 0.946x2

x1 : SRP:B

x2 : Boron

Model Summary

Model R R Square Adjusted R

Square

Std. Error of the

Estimate

1 .862a .742 .708 .037227

a. Predictors: (Constant), B, SRP_B

ANOVAa

Model Sum of Squares df Mean Square F Sig.

1

Regression .060 2 .030 21.611 .000b

Residual .021 15 .001

Total .081 17

a. Dependent Variable: SC_SRP

b. Predictors: (Constant), B, SRP_B

Coefficientsa

Model Unstandardized Coefficients Standardized

Coefficients

t Sig.

B Std. Error Beta

1

(Constant) -.076 .018 -4.118 .001

SRP_B .011 .003 .528 4.032 .001

B .946 .181 .683 5.213 .000

a. Dependent Variable: SC_SRP

57

Tables 25, 26 & 27 The SPSS model summary, ANOVA and coefficients outputs from multiple regression analysis between SC_SRP and the variables SRP:B and B

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4.5.4 eSC_SRP and SC_SRP

Using the multiple regression equation from SC_SRP, SRP and B a set of estimated

seasonal change in SRP (eSC_SRP) data was produced (table 28). Comparing the

eSC_SRP and the actual SC_SRP there is a 78% success rate in predicting the correct sign

(positive or negative) for the SC_SRP. Three out of the four that changed between positive

and negative was within 0.005 mgSRP/l of zero, and all four initially and after prediction

remained close to the zero value of no change in SC_SRP.

Site Seasonal Change

of SRP

(SC_SRP) mg/l

Estimated Seasonal Change of

SRP

(eSC_SRP) results

Coquet at Pauperhaugh -0.049 0.001

Derwent at Clap Shaw -0.048 -0.035

Leven at Middleton Wood 0.147 0.102

Ouseburn at Jesmond Dene 0.022 0.055

Ouseburn at Three Mile Bridge 0.049 0.028

Skerne at South Park

Darlington

0.065 0.064

Team u/s Birtley STW 0.036 0.074

Team at Lamesley 0.211 0.187

Tees at Dinsdale 0.041 0.079

Tees at Dent Bank -0.016 -0.030

N Tyne at Wark -0.062 0.004

S Tyne at Alston 0.003 -0.035

Wansbeck u/s How Burn 0.047 0.013

Wansbeck at Mitford 0.024 -0.012

Wear at B Auckland -0.013 -0.026

Wear at Cocken Bridge 0.070 0.027

Wear at Stanhope -0.040 -0.029

Wear at Shincliffe Bridge 0.016 0.020

58

Tables 28 Table of sample sites and their recorded SC_SRP values and their eSC_SRP values produced from the multiple regression equation between SC_SRP and the variables SRP:B and B

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From linear regression analysis between the estimate and the actual figures there is a very

strong positive relationship (R2 = 0.734 from table 29). The regression model is statistically

significant at the 0.001 significance boundary as P = 0.000006 (table 30). There is a 0.1%

probability that the relationship is due to chance. F (1, 16) = 44.262 suggesting that the

trend line is a very good fit for the data.

Linear regression equation

Y = 0.00673 + 0.73x

y = SC_SRP

x = eSC_SRP

Model Summary

R R Square Adjusted R

Square

Std. Error of the

Estimate

.857 .734 .718 .031

The independent variable is SC_SRP.

ANOVA

Sum of Squares df Mean Square F Sig.

Regression .043 1 .043 44.262 .000

Residual .015 16 .001

Total .058 17

The independent variable is SC_SRP.

Spearman’s rho was used to show the correlation between the estimated and the actual

SC_SRP. From table 31 it shows that there is a very strong correlation because of the high

correlation coefficient of 0.749. The p value is 0.000352 (table 31) indicating that the

correlation is statistically significant at the 0.001 significance boundary.

59

Tables 29 & 30 The SPSS model summary and ANOVA outputs from linear regression analysis between eSC_SRP and SC_SRP

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60

Figure 31 The graph from linear regression between eSC_SRP (mg/l) and SC_SRP (mg/l)

Table 31 The SPSS correlations output from Spearman’s rho correlation analysis between eSC_SRP and SC_SRP

Correlations

eSC_SRP SC_SRP

Spearman's rho

eSC_SRP

Correlation Coefficient 1.000 .749**

Sig. (2-tailed) . .000

N 18 18

SC_SRP

Correlation Coefficient .749** 1.000

Sig. (2-tailed) .000 .

N 18 18

**. Correlation is significant at the 0.01 level (2-tailed).

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5. Discussion

5.1 Variable statistics

5.5.1 B and SRP

B and SRP both contribute to sewage effluent (House and Denison, 1997; Jarvie et al.,

2006; Wyness et al., 2003). As sewage effluent is the largest contributor of SRP for the

majority of the rivers in England (Jarvie et al., 2006) it is not surprising to see SRP

increases as B increases. From the linear regression equation the gradient of the

relationship between the two variables is 3.96, so for every single increase in B, SRP

increases by 3.96. From the table (table 49) constructed using Neal et al. (2005) data the

average concentration of B in waters immediately after STWs is significantly less than the

average concentration of SRP. Due to the lack of data available on sewage effluents (Neal

et al., 1998; Wood et al., 2005) the composition of water after input had to suffice. Figure

32 describes why there is such a steep linear relationship in the variables. When the

volume of sewage effluent increases the relative increase in SRP is much greater than the

relative increase in B so with every small increase of sewage marker B there is a large

increase in SRP inputs.

1 2 30

5

10

15

20

25

30

35

40

45

SRPB

Relative increase in sewage effluent x2 each step

Rel

ativ

e ch

ange

s in

SR

P:B

61

Figure 32 A stacked histogram showing the relationship between SRP and B as the volume of sewage effluent increases.

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From the 5 outliers indicated it is the two that lie below the trend line that require

investigation because of the unusually high B compared to SRP that does not fit the steep

graded relationship between the variables. Neal et al. (1998) suggest that natural inputs of

B can come from weathering of igneous rock and leaching of salt deposits however the

catchment areas for both rivers is in the sedimentary Northumberland basin (Johnson,

1995) and the large distance from the coast suggests that the soil and groundwaters have

little salt content.

As the sites do not suggest high natural inputs of B we can presume that anthropogenic

activity must be influencing B. The River Ouseburn at Three Mile Bridge is only 6.5 km

away from the Newcastle city centre and is situated in the highly residential area of

Gosforth. The river receives direct ‘clean water’ from the residential areas. However

because B is in high concentrations in soaps and detergents (Neal et al., 2010) it is

definitely possible that these soaps and detergents are in the clean water sewers being

discharged into the river. This would cause the elevated levels of B without the elevated

levels of SRP.

The site at Wark on the N Tyne is 46.2 km away from the nearest city so we can assume

that high urban activity is not the cause of the anomaly. The catchment around the site is

highly agricultural so there is a possibility that B containing fertilisers were spread to

improve deficient soils (Jarvie et al., 2006). However it is assumed that SRP from diffuse

sources would also increase to fit the regression model. The final and most likely

possibility is B from disused coalmine drainage (Neal et al., 2010; Wyness et al., 2003).

From figure 33 from the Coal Authority website there is a distinct area of past coal mining

in the catchment of the Wark area. The old mines are drained during heavy precipitation

and deposited in the River N Tyne.

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The statistical tests allow us to reject the null hypothesis as p is significant at the 0.001

significance boundary and to confirm the previous findings in other studies (Jarvie et al.,

2006; Neal et al., 2005)

5.1.2 SRP and SC_SRP

From the results and statistical analysis SRP concentrations increase as SC_SRP moves

away from zero. The SC_SRP method of P source determination predicts that when

seasonal change is less than zero it is a diffuse source dominated river and when seasonal

change is greater than zero it is a point source dominated river. Zero is the even

contribution figure (ECF) for phosphorus source dominance. The magnitude of seasonal

change is greatest when point source inputs are dominant and when SRP concentrations are

highest. This is because the greatest inputs of SRP are from urban activity and STWs

(Jarvie et al., 2006).

63

Figure 33 A map of past coal mining areas in the NRBD. Represented by the semi-transparent area within the black margins. From The Coal Authority online map

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The magnitude of seasonal change and its relationship to the concentration of SRP can be

explained with simple volume maths. From figure 34 the river (a) has a large input of the

solvent compared to the small input in river (b) (100 p/a, 16 p/a). When the volume of the

river decreases the concentration of the solvent increases. However the relative change in

concentration is 0.37 p/a more in river (a) compared to river (b). The same principle

applies to this model, rivers with larger inputs of SRP will have a large seasonal variation.

It is important to acknowledge that the sites with a SC_SRP value close to the ECF have a

SRP concentration that falls below 0.1 mg/l and are therefore in classification 3 or less for

phosphates (table 2).

(a.i) conc. = 102/202 = 0.25 p/a (a.ii) conc. = 102/122 = 0.69 p/a

(b.i) conc. = 42/202 = 0.04 p/a (b.ii) conc. = 42/122 = 0.11 p/a

Δ (a) = 0.69 – 0.25 = 0.44 p/a

Δ (b) = 0.11 – 0.04 = 0.07 p/a

Δ = difference

p/a = parts per area

conc. = concentration

64

Figure 34 Diagram and equations to illustrate how changes in concentration vary in magnitude depending on the initial concentration.

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5.1.3 B and SRP response to urban land use (DNC)

As B and SRP are significantly related it is to be expected that they follow the same pattern

in respect to DNC. Both exponentially increase as DNC decreases because of the relative

change in land uses as you move towards the city centres. The urban structure promotes the

exponential growth of the two variables as it progresses to the city centre. From periphery

sparse housing, to the dense residential areas, to the heavy industrial sector and then the

city centre (Heiden et al., 2012) the inputs of B and SRP a particularly high in industrial

sectors (Withers and Jarvie, 2008) and decrease with the reduction in housing density

moving away from the centre. The increase in population density as DNC decreases can

also contribute to the effect (EA, 2012)

From the graphs in figures 27 and 28 there are two sites that vary from the main trend line.

The Mitford site on the River Wansbeck requires the most attention as it goes against the

exponential model. The River Coquet does not flow towards a major city, it flows from

west to east just north of the Newcastle area. The site at Mitford is up stream of both of the

urban areas Morpeth and Ashington on the river. These are the only urban influences on

the river. The reason for the small B and SRP concentrations may be because of small

inputs from diffuse sources and the low urban activity upstream. Small inputs of diffuse

phosphorus in a predominantly agricultural catchment could be because of the buffering

effect of vegetation that lines the riparian zone along the whole river (Winter and Dillon,

2005).

The site with particularly high values for SRP and B is at Lamesley on the River Team.

The sampling site is 500m directly downstream of the Birtley STWs. STWs discharge the

highest concentrations of SRP and B than any other input (Withers and Jarvie, 2008).

Furthermore the scale of the STWs is grand with 10 secondary treatment clarifiers that

serve 35,000 people in the Birtley area (CIEEM).

The rate of accumulation is greater with SRP than B because of the reason outlined in

figure 32. If the volume of sewage effluent increases when DNC decreases then the

relative increase in SRP will be greater than that of B because of its larger composition of

sewage effluent

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From multiple regression analysis of the three variables, SRP has a more of a statistically

significant relationship with B than with DNC. This is to be expected as DNC does not

directly connect to the level of urban activity it provides an estimate, whereas the

relationship between B and SRP is statistically significant as proven by the results and

other studies (Jarvie et al., 2006; Neal et al., 2005).

5.2 Method analysis

5.2.1 SRP:B and SC_SRP

The statistical analysis shows a moderate positive relationship between SC_SRP and

SRP:B. From the earlier variable analysis discussion the relationship between SRP and B

has been verified and explained in terms of a steep graded trend line. The relationship

between SRP and SC_SRP has also been discussed so it is to be expected that the

regression analysis of SRP:B and SC_SRP produce a similar model.

By using the point at which the trend line crosses the ECF for SC_SRP we can produce an

estimate for the point when SRP:B ratio predicts equal contribution from point and diffuse

sources (x = 2 figure 29). Any ratio of SRP:B higher than 2suggests that the river is point

source dominant. Any ratio that falls under an SRP:B of 2 suggests a river that is diffuse

source dominant. Using SC_SRP = 0 and SRP:B = 2 areas that both methods agree on are

the unshaded areas displayed in figure 29. However three points show a disagreement on

what the main phosphorus source is, adding doubt to the reliability of the tested method.

Two of the three points that fail to agree are the two points closest to the ECF intersection.

As suggested before, values of SC_SRP close to the ECF tend to display very low

concentrations of SRP (figure 26). In the regression model between the two methods

(figure 29) the two points discussed have SRP concentrations of 0.04 mg/l and 0.06 mg/l

and fall in the classification group 2 for phosphates (table 2) confirming the SC_SRP –

SRP relationship and suggesting that they are not in need of any recovery management

scheme anyway (Mostert, 2003).

The true outlier is at the Pauperhaugh site on the River Coquet. The SRP:B ratio is

uncharacteristically high for a supposedly diffuse source dominated river. The SRP:B ratio

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is high because of a high SRP value for the site. In figure 7 and google maps the

surrounding catchment for the site is an operational golf course. From a Winter and Dillon

(2005) study they concluded that management and up keep of an operational golf course

caused streams that drain the land to have a higher phosphorus level. The same can be

applied to this site as SRP levels had raised whilst B remained low.

The regression model is statistically significant at the 0.05 significance boundary so the

null hypothesis can be rejected. In reality the SRP:B method cannot be used on its own

because it is not reliable enough as it would have assumed that the Pauperhaugh site was

point source dominated and because the R2 value for the model is too low. However, when

used in conjunction with the SC_SRP method it can be a handy tool for determining P

source as it fits the WFD criteria for operational monitoring (Alan et al., 2006; Dworak,

2005) and it identifies sites in the red shaded areas (figure 29) that require investigative

monitoring (Dworak, 2005).

5.2.2 B and SC_SRP

The method of just using B as a way of determining the dominant phosphorus source has a

stronger more significant relationship with the SC_SRP method than the SRP:B method

does. However because of the cubic nature of the regression line B values that are equal to

or close to 0.5mgB/l are impossible to distinguish whether they lay on the positive or the

negative side of the ECF for SC_SRP. The ECF of the SC_SRP method is the essential

part of the model as it distinguishes what the WFD management plans should address,

because the B method is intersected at its constant period between - 0.3 mgSRP/l and 0.7

mgSRP/l it is unsuitable to achieve the aim of the study. Even with the removal of the high

B figure for the Wark site because of coal mining drainage (Neal et al., 2010; Wyness et

al., 2003) the significance would improve but the main issue persists. The benefits of the

SRP:B ratio over this method is that it is essentially two variables in coordination to

predict an outcome. If B is unusually high in the B method then it will lay far out of the

regression model, whereas in the SRP:B method the variance of the result will be reduced

due to the SRP figure.

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5.2.3 Multiple regression of SC_SRP with SRP:B and B

The logical progression from a model with a high significance but low predictive value and

a model with a clear predictive value but lower significance is to combine the two

methods. The multiple regression model has the highest statistical significance of the

method models against SC_SRP. In the model SC_SRP increases when SRP:B and B

increases this is because of the basic relationships between SRP and B with SC_SRP

discussed earlier and in the relevant studies (Jarvie et al., 2006; Neal et al., 2005).

The estimated SC_SRP values that are predicted show a strong significant correlation. The

reliability of the model is put into question because of the four sites that crossed the ECF

however these sites lie so close to the ECF that the SRP will be within classification 3 or

lower according to the variable relationship between SRP and SC_SRP (figure 26)). The

significant relationship between eSC_SRP and SC_SRP suggests that the method can be

used on its own unlike the other two methods that required verification by checking against

SC_SRP. The method meets the needs of the WFD as it provides relatively fast data that

can reliably predict the P source of rivers that have a SRP above classification 3 in the

GQA standards (table 2), the rivers most in need of a management strategy (Mostert,

2003).

6. Conclusion

My results replicate the findings of other studies (Fox et al., 2000; Jarvie et al., 2006; Neal

et al., 1998; Neal et al., 2005; Neal et al., 2010) that B can be used as a marker for sewage

effluent marker because of its relationship with SRP especially at high levels that are

typical of point source affected rivers (Jarvie et al., 2006; Neal et al., 2005) that have the

largest positive SC_SRP values.

The most accurate, reliable model at predicting SC_SRP is the SRP:B, B multiple

regression model. From the estimated SC_SRP figure the dominant P source can be

determined and a management scheme can be produced. However the benefits of the

SRP:B and SC_SRP model cannot be overlooked as it provides an easy to read analysis of

the relative P source contributions and highlights the sites that need further enquiries by

WFD investigative monitoring (Hering et al., 2010).

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The SRP:B, B multiple regression equation could be the basis for a one off spot sampling

technique that provides information on the relevant P source that needs attention,

particularly those with the highest SRP values linked to eutrophication (EA, 2012; Hilton

et al., 2006; Jarvie et al., 2006). The spot sampling could be done every 6 months or

annually to track progress, with the aim to produce mitigation measures that bring the

eSC_SRP value closer to zero which is likely to be an SRP value in GQA classification 3

or lower according to SRP and SC_SRP relations.

It is a simple and cost effective technique compared to operational continuous monitoring

(Dworak et al., 2005; Hering et al., 2010) and more reliable than export coefficient

methods as it is data taken from the river itself (Bowes et al., 2008). The method achieves

the aim of the project.

7. Limitations and improvements

As using SRP:B in relation to SC_SRP to show its capabilities of predicting the dominant

sources of P has never been used before in other studies there is no data to compare

against. It would have been beneficial to use the multiple regression equation produced on

another set of data from a river from another site and because of the time restraints of the

project I could not collect the second set myself.

The concept shows good grounding and there definitely is a possible progression with the

SRP:B and B method. If there were no time restraints more data could be collected from

more sites along an individual river and across more rivers in general to improve the

strength of the regression model. Primary data specific for the SC_SRP method could be

collected every week within the summer and winter months for two to three years. Finally

from other studies (Neal et al., 1998; Neal et al., 2005) flow is often linked to B, flow

could be recorded and incorporated as a function so that the model is more likely to

address changes in flow upstream and downstream and between rivers of different sizes.

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8. Appendices

8.1 Primary data

Site Boron mg/l

SRP mg/l

P ug/l Seasonal Changeof SRP mg/l

SRP/B mg/l

Distance from Nearest City km

Coquet at Pauperhaugh

0.021 0.11 35.106 -0.049 5.238 52.5

Derwent at Clap Shaw 0.021 0.04 12.766 -0.048 1.905 38.9

Leven at Middleton Wood

0.039 0.50 159.574 0.147 12.821

13

Ouseburn at Jesmond Dene

0.081 0.40 127.660 0.022 4.938 3.3

Ouseburn at Three Mile Bridge

0.086 0.18 57.447 0.049 2.093 6.5

Skerne at South Park Darlington

0.095 0.43 137.234 0.065 4.526 23.7

Team u/s Birtley STW 0.055 0.49 156.383 0.036 8.909 4.3

Team at Lamesley 0.230 0.95 303.191 0.211 4.130 7.9

Tees at Dinsdale 0.052 0.50 159.574 0.041 9.615 18.2

Tees at Dent Bank 0.035 0.04 12.766 -0.016 1.143 61.8

N Tyne at Wark 0.074 0.07 22.340 -0.062 0.946 46.2

S Tyne at Alston 0.024 0.04 12.766 0.003 1.667 59.5

Wansbeck u/s How Burn

0.037 0.18 57.447 0.047 4.865 25.4

Wansbeck at Mitford 0.010 0.05 15.957 0.024 5.000 24.7

Wear at B Auckland 0.024 0.06 19.149 -0.013 2.500 39.8

Wear at Cocken Bridge 0.047 0.25 79.787 0.070 5.319 19.5

Wear at Stanhope 0.031 0.05 15.957 -0.040 1.613 49.9

Wear at Shincliffe Bridge

0.050 0.22 70.213 0.016 4.400 23

70

Table 32 Sample sites and all their data for the variables: B, SRP, P, SC_SRP and DNC

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8.2 Secondary data

SITE NAME DATE OF SAMPLE

TIME OF SAMPLE

SAMPLE TEST VALUE (mg/l)

WEAR AT SHINCLIFFE BRIDGE

20-Jan-2011 1220 Orthophosphate, reactive as P

0.056

WEAR AT SHINCLIFFE BRIDGE

15-Feb-2011 1045 Orthophosphate, reactive as P

0.037

WEAR AT SHINCLIFFE BRIDGE

14-Jun-2011 1040 Orthophosphate, reactive as P

0.134

WEAR AT SHINCLIFFE BRIDGE

12-Jul-2011 1047 Orthophosphate, reactive as P

0.107

WEAR AT SHINCLIFFE BRIDGE

05-Aug-2011 1109 Orthophosphate, reactive as P

0.131

WEAR AT SHINCLIFFE BRIDGE

08-Dec-2011 1052 Orthophosphate, reactive as P

0.067

WEAR AT SHINCLIFFE BRIDGE

11-Jan-2012 1145 Orthophosphate, reactive as P

0.061

WEAR AT SHINCLIFFE BRIDGE

07-Feb-2012 1142 Orthophosphate, reactive as P

0.124

WEAR AT SHINCLIFFE BRIDGE

20-Jun-2012 0901 Orthophosphate, reactive as P

0.056

WEAR AT SHINCLIFFE BRIDGE

09-Jul-2012 1008 Orthophosphate, reactive as P

0.042

WEAR AT SHINCLIFFE BRIDGE

06-Aug-2012 1137 Orthophosphate, reactive as P

0.043

WEAR AT SHINCLIFFE BRIDGE

18-Dec-2012 1204 Orthophosphate, reactive as P

0.054

WEAR AT SHINCLIFFE BRIDGE

12-Feb-2013 1143 Orthophosphate, reactive as P

0.069

WEAR AT SHINCLIFFE BRIDGE

02-Aug-2013 1123 Orthophosphate, reactive as P

0.065

AVERAGE SUMMER MONTHS 0.083

WINTER MONTHS 0.069

SEASONAL DIFFERENCE

0.014

71

Table 33 sample site Shincliffe Bridge, River Wear and the secondary data obtained from the EA and the calculated seasonal change from the average of the summer and winter months

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SITE NAME DATE OF SAMPLE

TIME OF SAMPLE

SAMPLE TEST VALUE (mg/l)

WEAR AT COCKEN BRIDGE

21-Jan-2010 0935 Orthophosphate, reactive as P

0.055

WEAR AT COCKEN BRIDGE

17-Feb-2010 0915 Orthophosphate, reactive as P

0.075

WEAR AT COCKEN BRIDGE

22-Jul-2010 0900 Orthophosphate, reactive as P

0.078

WEAR AT COCKEN BRIDGE

09-Aug-2010 0820 Orthophosphate, reactive as P

0.179

WEAR AT COCKEN BRIDGE

24-Aug-2010 0830 Orthophosphate, reactive as P

0.248

WEAR AT COCKEN BRIDGE

11-Jan-2011 0850 Orthophosphate, reactive as P

0.056

WEAR AT COCKEN BRIDGE

17-Feb-2011 0825 Orthophosphate, reactive as P

0.057

WEAR AT COCKEN BRIDGE

09-Jun-2011 0855 Orthophosphate, reactive as P

0.404

WEAR AT COCKEN BRIDGE

20-Jul-2011 0915 Orthophosphate, reactive as P

0.196

WEAR AT COCKEN BRIDGE

30-Aug-2011 0930 Orthophosphate, reactive as P

0.195

WEAR AT COCKEN BRIDGE

07-Dec-2011 0855 Orthophosphate, reactive as P

0.139

WEAR AT COCKEN BRIDGE

18-Jan-2012 0920 Orthophosphate, reactive as P

0.177

WEAR AT COCKEN BRIDGE

08-Feb-2012 0915 Orthophosphate, reactive as P

0.206

WEAR AT COCKEN BRIDGE

21-Feb-2012 0915 Orthophosphate, reactive as P

0.179

WEAR AT COCKEN BRIDGE

20-Jun-2012 1124 Orthophosphate, reactive as P

0.088

WEAR AT COCKEN BRIDGE

23-Aug-2012 1135 Orthophosphate, reactive as P

0.099

WEAR AT COCKEN BRIDGE

04-Dec-2012 1202 Orthophosphate, reactive as P

0.076

WEAR AT COCKEN BRIDGE

12-Dec-2012 1155 Orthophosphate, reactive as P

0.078

WEAR AT COCKEN BRIDGE

13-Feb-2013 1332 Orthophosphate, reactive as P

0.093

WEAR AT COCKEN BRIDGE

19-Aug-2013 1104 Orthophosphate, reactive as P

0.114

AVERAGE SUMMER MONTHS 0.178

72

110138619

WINTER MONTHS 0.108

SEASONAL DIFFERENCE

0.070

SITE NAME DATE OF SAMPLE

TIME OF SAMPLE

SAMPLE TEST VALUE (mg/l)

WEAR AT BISHOP AUCKLAND

31-Aug-2010 0830 Orthophosphate, reactive as P

0.021

WEAR AT BISHOP AUCKLAND

19-Jan-2011 1120 Orthophosphate, reactive as P

0.028

WEAR AT BISHOP AUCKLAND

14-Feb-2011 0830 Orthophosphate, reactive as P

0.135

WEAR AT BISHOP AUCKLAND

28-Feb-2011 1150 Orthophosphate, reactive as P

0.043

WEAR AT BISHOP AUCKLAND

02-Jun-2011 0925 Orthophosphate, reactive as P

0.021

WEAR AT BISHOP AUCKLAND

30-Jun-2011 1215 Orthophosphate, reactive as P

0.026

WEAR AT BISHOP AUCKLAND

18-Jul-2011 0905 Orthophosphate, reactive as P

0.040

WEAR AT BISHOP AUCKLAND

10-Aug-2011 1225 Orthophosphate, reactive as P

0.035

WEAR AT BISHOP AUCKLAND

22-Aug-2011 0850 Orthophosphate, reactive as P

0.038

WEAR AT BISHOP AUCKLAND

06-Dec-2011 0900 Orthophosphate, reactive as P

0.023

WEAR AT BISHOP AUCKLAND

11-Jan-2012 1315 Orthophosphate, reactive as P

0.023

WEAR AT BISHOP AUCKLAND

01-Feb-2012 1310 Orthophosphate, reactive as P

0.032

WEAR AT BISHOP AUCKLAND

01-Feb-2012 1340 Orthophosphate, reactive as P

0.047

WEAR AT BISHOP AUCKLAND

16-Feb-2012 1235 Orthophosphate, reactive as P

0.022

WEAR AT BISHOP AUCKLAND

16-Feb-2012 1330 Orthophosphate, reactive as P

0.034

WEAR AT BISHOP AUCKLAND

10-Aug-2012 1210 Orthophosphate, reactive as P

0.021

WEAR AT BISHOP AUCKLAND

18-Dec-2012 1041 Orthophosphate, reactive as P

0.022

WEAR AT BISHOP AUCKLAND

05-Jun-2013 1001 Orthophosphate, reactive as P

0.021

73

Table 34 sample site Cocken Bridge, River Wear and the secondary data obtained from the EA and the calculated seasonal change from the average of the summer and winter months

110138619

AVERAGE SUMMER MONTHS 0.029

WINTER MONTHS 0.041

SEASONAL DIFFERENCE

-0.012

SITE NAME DATE OF SAMPLE

TIME OF SAMPLE

SAMPLE TEST VALUE (mg/l)

WEAR AT STANHOPE 22-Aug-2000 1050 Orthophosphate, reactive as P

0.053

WEAR AT STANHOPE 29-Aug-2000 1230 Orthophosphate, reactive as P

0.032

WEAR AT STANHOPE 12-Dec-2000 1200 Orthophosphate, reactive as P

0.059

WEAR AT STANHOPE 25-Jan-2001 1345 Orthophosphate, reactive as P

0.023

WEAR AT STANHOPE 22-Jun-2001 0950 Orthophosphate, reactive as P

0.027

WEAR AT STANHOPE 16-Jul-2001 0950 Orthophosphate, reactive as P

0.022

WEAR AT STANHOPE 16-Aug-2001 1010 Orthophosphate, reactive as P

0.038

WEAR AT STANHOPE 09-Jan-2002 1450 Orthophosphate, reactive as P

0.034

WEAR AT STANHOPE 27-Feb-2002 1315 Orthophosphate, reactive as P

0.034

WEAR AT STANHOPE 11-Jun-2002 1445 Orthophosphate, reactive as P

0.048

WEAR AT STANHOPE 21-Aug-2002 1400 Orthophosphate, reactive as P

0.023

WEAR AT STANHOPE 04-Jun-2003 1015 Orthophosphate, reactive as P

0.046

WEAR AT STANHOPE 14-Jan-2004 1139 Orthophosphate, reactive as P

0.206

WEAR AT STANHOPE 05-Aug-2004 0754 Orthophosphate, reactive as P

0.020

WEAR AT STANHOPE 07-Feb-2005 0957 Orthophosphate, reactive as P

0.075

WEAR AT STANHOPE 06-Jun-2005 1135 Orthophosphate, reactive as P

0.010

AVERAGE SUMMER MONTHS 0.030

WINTER MONTHS 0.072

SEASONAL -0.042

74

Table 35 sample site Bishop Auckland, River Wear and the secondary data obtained from the EA and the calculated seasonal change from the average of the summer and winter months

110138619

DIFFERENCE

SITE NAME DATE OF SAMPLE

TIME OF SAMPLE

SAMPLE TEST VALUE (mg/l)

NORTH TYNE AT WARK 07-Jun-2000 0930 Orthophosphate, reactive as P

0.027

NORTH TYNE AT WARK 12-Dec-2000 0915 Orthophosphate, reactive as P

0.022

NORTH TYNE AT WARK 14-Dec-2000 0820 Orthophosphate, reactive as P

0.027

NORTH TYNE AT WARK 22-Jan-2001 0840 Orthophosphate, reactive as P

0.022

NORTH TYNE AT WARK 26-Feb-2002 0930 Orthophosphate, reactive as P

0.386

NORTH TYNE AT WARK 24-Jun-2002 1040 Orthophosphate, reactive as P

0.020

NORTH TYNE AT WARK 24-Jul-2002 0910 Orthophosphate, reactive as P

0.020

NORTH TYNE AT WARK 12-Aug-2002 1120 Orthophosphate, reactive as P

0.025

NORTH TYNE AT WARK 06-Dec-2002 1010 Orthophosphate, reactive as P

0.026

NORTH TYNE AT WARK 20-Jan-2003 1130 Orthophosphate, reactive as P

0.024

AVERAGE SUMMER MONTHS 0.023

WINTER MONTHS 0.085

SEASONAL DIFFERENCE

-0.062

SOUTH TYNE AT ALSTON

09-Jan-2006 1340 Orthophosphate, reactive as P

0.023

SOUTH TYNE AT ALSTON

30-Jan-2006 1150 Orthophosphate, reactive as P

0.028

SOUTH TYNE AT ALSTON

08-Jun-2006 1135 Orthophosphate, reactive as P

0.028

AVERAGE SUMMER MONTHS 0.028

WINTER MONTHS 0.025

SEASONAL DIFFERENCE

0.003

75

Table 36 sample site Stanhope, River Wear and the secondary data obtained from the EA and the calculated seasonal change from the average of the summer and winter months

Table 37 sample site Alston, River S Tyne and sample site Wark, River N Tyne and the secondary data obtained from the EA and the calculated seasonal change from the average of the summer and winter months

110138619

SITE NAME DATE OF SAMPLE

TIME OF SAMPLE

SAMPLE TEST VALUE (mg/l)

WANSBECK AT MITFORD

02-Jun-2011 0945 Orthophosphate, reactive as P

0.070

WANSBECK AT MITFORD

10-Aug-2011 1035 Orthophosphate, reactive as P

0.110

WANSBECK AT MITFORD

24-Jan-2012 1000 Orthophosphate, reactive as P

0.060

WANSBECK AT MITFORD

11-Jul-2012 1245 Orthophosphate, reactive as P

0.063

WANSBECK AT MITFORD

07-Dec-2012 1110 Orthophosphate, reactive as P

0.054

AVERAGE SUMMER MONTHS 0.081

WINTER MONTHS 0.057

SEASONAL DIFFERENCE

0.024

WANSBECK U/S HOW BURN CONFLUENCE

25-Jun-2010 0730 Orthophosphate, reactive as P

0.049

WANSBECK U/S HOW BURN CONFLUENCE

30-Jun-2010 0929 Orthophosphate, reactive as P

0.055

WANSBECK U/S HOW BURN CONFLUENCE

20-Jul-2010 0929 Orthophosphate, reactive as P

0.148

WANSBECK U/S HOW BURN CONFLUENCE

25-Aug-2010 0845 Orthophosphate, reactive as P

0.024

WANSBECK U/S HOW BURN CONFLUENCE

02-Jun-2011 1100 Orthophosphate, reactive as P

0.213

WANSBECK U/S HOW BURN CONFLUENCE

11-Jul-2011 1515 Orthophosphate, reactive as P

0.045

WANSBECK U/S HOW BURN CONFLUENCE

10-Aug-2011 1200 Orthophosphate, reactive as P

0.025

WANSBECK U/S HOW BURN CONFLUENCE

25-Jan-2012 0930 Orthophosphate, reactive as P

0.020

WANSBECK U/S HOW BURN CONFLUENCE

20-Feb-2012 1025 Orthophosphate, reactive as P

0.026

WANSBECK U/S HOW BURN CONFLUENCE

15-Jun-2012 1110 Orthophosphate, reactive as P

0.029

WANSBECK U/S HOW BURN CONFLUENCE

11-Jul-2012 1217 Orthophosphate, reactive as P

0.075

WANSBECK U/S HOW BURN CONFLUENCE

03-Aug-2012 1120 Orthophosphate, reactive as P

0.035

76

110138619

AVERAGE SUMMER MONTHS 0.070

WINTER MONTHS 0.023

SEASONAL DIFFERENCE

0.047

SITE NAME DATE OF SAMPLE

TIME OF SAMPLE

SAMPLE TEST VALUE (mg/l)

COQUET AT PAUPERHAUGH

21-Jun-2010 1112 Orthophosphate, reactive as P

0.026

COQUET AT PAUPERHAUGH

11-Jan-2011 1205 Orthophosphate, reactive as P

0.026

COQUET AT PAUPERHAUGH

02-Feb-2011 1230 Orthophosphate, reactive as P

0.210

COQUET AT PAUPERHAUGH

09-Jan-2012 1205 Orthophosphate, reactive as P

0.026

COQUET AT PAUPERHAUGH

01-Feb-2012 1220 Orthophosphate, reactive as P

0.036

AVERAGE SUMMER MONTHS 0.026

WINTER MONTHS 0.075

SEASONAL DIFFERENCE

-0.049

SITE NAME DATE OF SAMPLE

TIME OF SAMPLE

SAMPLE TEST VALUE (mg/l)

DERWENT AT CLAP SHAW

06-Jul-1995 0745 Orthophosphate, reactive as P

0.020

DERWENT AT CLAP SHAW

23-Aug-1995 1123 Orthophosphate, reactive as P

0.020

DERWENT AT CLAP SHAW

19-Feb-1997 1200 Orthophosphate, reactive as P

0.020

DERWENT AT CLAP SHAW

23-Jun-1997 1350 Orthophosphate, reactive as P

0.020

DERWENT AT CLAP SHAW

05-Dec-1997 0940 Orthophosphate, reactive as P

0.020

77

Table 38 sample site Mitford, River Wansbeck and sample site u/s How Burn confluence, River Wansbeck and the secondary data obtained from the EA and the calculated seasonal change from the average of the summer and winter months

Table 39 sample site Pauperhaugh, River Coquet and the secondary data obtained from the EA and the calculated seasonal change from the average of the summer and winter months

110138619

DERWENT AT CLAP SHAW

22-Jan-1998 1005 Orthophosphate, reactive as P

0.030

DERWENT AT CLAP SHAW

26-Aug-1998 1000 Orthophosphate, reactive as P

0.030

DERWENT AT CLAP SHAW

01-Dec-1998 1000 Orthophosphate, reactive as P

0.340

DERWENT AT CLAP SHAW

11-Dec-1998 1020 Orthophosphate, reactive as P

0.030

DERWENT AT CLAP SHAW

28-Jun-1999 1025 Orthophosphate, reactive as P

0.030

DERWENT AT CLAP SHAW

11-Dec-2000 1330 Orthophosphate, reactive as P

0.022

DERWENT AT CLAP SHAW

24-Jan-2001 0915 Orthophosphate, reactive as P

0.040

AVERAGE SUMMER MONTHS 0.024WINTER MONTHS 0.072SEASONAL DIFFERENCE

-0.048

SITE NAME DATE OF SAMPLE

TIME OF SAMPLE

SAMPLE TEST VALUE (mg/l)

TEAM U/S BIRTLEY STW OUTFALL

08-Jan-2013 1139 Orthophosphate, reactive as P

0.183

TEAM U/S BIRTLEY STW OUTFALL

04-Feb-2013 1127 Orthophosphate, reactive as P

0.184

TEAM U/S BIRTLEY STW OUTFALL

18-Feb-2013 1302 Orthophosphate, reactive as P

0.279

TEAM U/S BIRTLEY STW OUTFALL

04-Jun-2013 1217 Orthophosphate, reactive as P

0.396

TEAM U/S BIRTLEY STW OUTFALL

16-Jul-2013 1342 Orthophosphate, reactive as P

0.291

TEAM U/S BIRTLEY STW OUTFALL

21-Aug-2013 1016 Orthophosphate, reactive as P

0.275

TEAM U/S BIRTLEY STW OUTFALL

03-Dec-2013 0930 Orthophosphate, reactive as P

0.493

AVERAGE SUMMER MONTHS 0.321

WINTER MONTHS 0.285

SEASONAL DIFFERENCE

0.036

78

Table 40 sample site Clap Shaw, River Derwent and the secondary data obtained from the EA and the calculated seasonal change from the average of the summer and winter months

Table 41 sample site u/s Birtley STWs, River Team and the secondary data obtained from the EA and the calculated seasonal change from the average of the summer and winter months

110138619

SITE NAME DATE OF SAMPLE

TIME OF SAMPLE

SAMPLE TEST VALUE (mg/l)

TEAM AT LAMESLEY 11-Dec-1997 0930 Orthophosphate, reactive as P

0.640

TEAM AT LAMESLEY 27-Jan-1998 1345 Orthophosphate, reactive as P

1.120

TEAM AT LAMESLEY 12-Feb-1998 1045 Orthophosphate, reactive as P

0.640

TEAM AT LAMESLEY 18-Jun-1998 0910 Orthophosphate, reactive as P

0.380

TEAM AT LAMESLEY 14-Jul-1998 0820 Orthophosphate, reactive as P

0.620

TEAM AT LAMESLEY 26-Aug-1998 0955 Orthophosphate, reactive as P

1.150

TEAM AT LAMESLEY 03-Dec-1998 1445 Orthophosphate, reactive as P

1.490

TEAM AT LAMESLEY 09-Jun-1999 1445 Orthophosphate, reactive as P

1.850

TEAM AT LAMESLEY 13-Jul-1999 1455 Orthophosphate, reactive as P

2.070

TEAM AT LAMESLEY 19-Aug-1999 1535 Orthophosphate, reactive as P

0.710

TEAM AT LAMESLEY 17-Jul-2000 1425 Orthophosphate, reactive as P

1.590

TEAM AT LAMESLEY 14-Dec-2000 1354 Orthophosphate, reactive as P

0.532

TEAM AT LAMESLEY 26-Jan-2001 1400 Orthophosphate, reactive as P

0.829

TEAM AT LAMESLEY 09-Feb-2001 0920 Orthophosphate, reactive as P

0.287

TEAM AT LAMESLEY 26-Jul-2001 1100 Orthophosphate, reactive as P

0.380

TEAM AT LAMESLEY 09-Aug-2001 1050 Orthophosphate, reactive as P

0.446

TEAM AT LAMESLEY 17-Dec-2001 1150 Orthophosphate, reactive as P

0.483

79

110138619

TEAM AT LAMESLEY 29-Jan-2002 1245 Orthophosphate, reactive as P

0.625

TEAM AT LAMESLEY 27-Feb-2002 1430 Orthophosphate, reactive as P

0.384

TEAM AT LAMESLEY 11-Jun-2002 1430 Orthophosphate, reactive as P

0.617

TEAM AT LAMESLEY 16-Aug-2002 1250 Orthophosphate, reactive as P

1.440

TEAM AT LAMESLEY 04-Dec-2002 1025 Orthophosphate, reactive as P

1.120

TEAM AT LAMESLEY 12-Dec-2002 1250 Orthophosphate, reactive as P

1.260

TEAM AT LAMESLEY 13-Dec-2002 1005 Orthophosphate, reactive as P

1.150

AVERAGE SUMMER MONTHS 1.023

WINTER MONTHS 0.812

SEASONAL DIFFERENCE

0.211

SITE NAME DATE OF SAMPLE

TIME OF SAMPLE

SAMPLE TEST VALUE (mg/l)

TEES AT DENT BANK 10-Jan-2006 0949 Orthophosphate, reactive as P

0.023

TEES AT DENT BANK 02-Feb-2006 1032 Orthophosphate, reactive as P

0.011

TEES AT DENT BANK 07-Jun-2006 1045 Orthophosphate, reactive as P

0.012

TEES AT DENT BANK 15-Jan-2007 1230 Orthophosphate, reactive as P

0.026

TEES AT DENT BANK 22-Feb-2007 1210 Orthophosphate, reactive as P

0.055

TEES AT DENT BANK 19-Jun-2007 1205 Orthophosphate, reactive as P

0.013

AVERAGE SUMMER MONTHS 0.013

WINTER MONTHS 0.029

SEASONAL DIFFERENCE

-0.016

80

Table 42 sample site Lamesley, River Team and the secondary data obtained from the EA and the calculated seasonal change from the average of the summer and winter months

Table 43 sample site Dent Bank, River Tees and the secondary data obtained from the EA and the calculated seasonal change from the average of the summer and winter months

110138619

SITE NAME DATE OF SAMPLE

TIME OF SAMPLE

SAMPLE TEST VALUE (mg/l)

TEES AT DINSDALE 02-Feb-2010 1235 Orthophosphate, reactive as P

0.130

TEES AT DINSDALE 24-Feb-2010 1325 Orthophosphate, reactive as P

0.193

TEES AT DINSDALE 06-Jul-2010 1125 Orthophosphate, reactive as P

0.374

TEES AT DINSDALE 20-Jul-2010 1330 Orthophosphate, reactive as P

0.037

TEES AT DINSDALE 30-Jul-2010 1217 Orthophosphate, reactive as P

0.339

TEES AT DINSDALE 24-Aug-2010 1032 Orthophosphate, reactive as P

0.151

TEES AT DINSDALE 12-Jan-2011 1316 Orthophosphate, reactive as P

0.065

TEES AT DINSDALE 02-Feb-2011 1344 Orthophosphate, reactive as P

0.124

TEES AT DINSDALE 14-Jun-2011 1013 Orthophosphate, reactive as P

0.120

TEES AT DINSDALE 12-Jul-2011 1123 Orthophosphate, reactive as P

0.157

TEES AT DINSDALE 27-Jul-2011 1030 Orthophosphate, reactive as P

0.197

TEES AT DINSDALE 10-Jan-2012 1011 Orthophosphate, reactive as P

0.107

TEES AT DINSDALE 06-Feb-2012 1018 Orthophosphate, reactive as P

0.162

TEES AT DINSDALE 29-Feb-2012 1051 Orthophosphate, reactive as P

0.192

TEES AT DINSDALE 12-Jun-2012 1030 Orthophosphate, reactive as P

0.099

TEES AT DINSDALE 19-Jun-2012 0958 Orthophosphate, reactive as P

0.102

TEES AT DINSDALE 05-Jul-2012 1306 Orthophosphate, reactive as P

0.052

TEES AT DINSDALE 14-Aug-2012 1130 Orthophosphate, 0.227

81

110138619

reactive as PTEES AT DINSDALE 04-Feb-2013 1018 Orthophosphate,

reactive as P0.087

TEES AT DINSDALE 05-Aug-2013 1155 Orthophosphate, reactive as P

0.221

AVERAGE SUMMER MONTHS 0.173

WINTER MONTHS 0.133

SEASONAL DIFFERENCE

0.041

SITE NAME DATE OF SAMPLE

TIME OF SAMPLE

SAMPLE TEST VALUE (mg/l)

OUSE BURN AT JESMOND DENE

07-Jun-2010 1046 Orthophosphate, reactive as P

0.140

OUSE BURN AT JESMOND DENE

22-Jun-2010 1210 Orthophosphate, reactive as P

0.201

OUSE BURN AT JESMOND DENE

08-Jul-2010 1246 Orthophosphate, reactive as P

0.204

OUSE BURN AT JESMOND DENE

04-Aug-2010 1257 Orthophosphate, reactive as P

0.145

OUSE BURN AT JESMOND DENE

11-Jan-2011 1340 Orthophosphate, reactive as P

0.045

OUSE BURN AT JESMOND DENE

28-Jan-2011 1400 Orthophosphate, reactive as P

0.062

OUSE BURN AT JESMOND DENE

09-Jun-2011 1505 Orthophosphate, reactive as P

0.217

OUSE BURN AT JESMOND DENE

12-Jul-2011 1520 Orthophosphate, reactive as P

0.140

OUSE BURN AT JESMOND DENE

11-Aug-2011 1040 Orthophosphate, reactive as P

0.090

OUSE BURN AT JESMOND DENE

22-Aug-2011 1055 Orthophosphate, reactive as P

0.150

OUSE BURN AT JESMOND DENE

10-Jan-2012 0930 Orthophosphate, reactive as P

0.114

OUSE BURN AT JESMOND DENE

06-Feb-2012 0925 Orthophosphate, reactive as P

0.161

OUSE BURN AT JESMOND DENE

20-Jun-2012 0847 Orthophosphate, reactive as P

0.111

OUSE BURN AT JESMOND DENE

29-Jun-2012 0632 Orthophosphate, reactive as P

0.115

OUSE BURN AT JESMOND DENE

16-Aug-2012 1134 Orthophosphate, reactive as P

0.098

OUSE BURN AT JESMOND DENE

07-Jun-2013 1006 Orthophosphate, reactive as P

0.153

82

Table 44 sample site Dinsdale, River Tees and the secondary data obtained from the EA and the calculated seasonal change from the average of the summer and winter months

110138619

OUSE BURN AT JESMOND DENE

03-Dec-2013 1406 Orthophosphate, reactive as P

0.318

OUSE BURN AT JESMOND DENE

06-Jan-2014 1422 Orthophosphate, reactive as P

0.050

AVERAGE SUMMER MONTHS 0.147

WINTER MONTHS 0.125

SEASONAL DIFFERENCE

0.022

SITE NAME DATE OF SAMPLE

TIME OF SAMPLE

SAMPLE TEST VALUE (mg/l)

OUSE BURN AT THREE MILE BRIDGE

07-Jun-2010 0854 Orthophosphate, reactive as P

0.110

OUSE BURN AT THREE MILE BRIDGE

22-Jun-2010 1051 Orthophosphate, reactive as P

0.180

OUSE BURN AT THREE MILE BRIDGE

08-Jul-2010 1133 Orthophosphate, reactive as P

0.140

OUSE BURN AT THREE MILE BRIDGE

04-Aug-2010 1158 Orthophosphate, reactive as P

0.166

OUSE BURN AT THREE MILE BRIDGE

11-Jan-2011 1110 Orthophosphate, reactive as P

0.053

OUSE BURN AT THREE MILE BRIDGE

28-Jan-2011 1050 Orthophosphate, reactive as P

0.056

OUSE BURN AT THREE MILE BRIDGE

09-Jun-2011 1400 Orthophosphate, reactive as P

0.196

OUSE BURN AT THREE MILE BRIDGE

12-Jul-2011 1445 Orthophosphate, reactive as P

0.133

OUSE BURN AT THREE MILE BRIDGE

11-Aug-2011 1120 Orthophosphate, reactive as P

0.086

OUSE BURN AT THREE MILE BRIDGE

22-Aug-2011 1020 Orthophosphate, reactive as P

0.102

OUSE BURN AT THREE MILE BRIDGE

10-Jan-2012 1000 Orthophosphate, reactive as P

0.097

OUSE BURN AT THREE MILE BRIDGE

06-Feb-2012 0955 Orthophosphate, reactive as P

0.108

OUSE BURN AT THREE MILE BRIDGE

14-Jun-2012 1342 Orthophosphate, reactive as P

0.075

OUSE BURN AT THREE MILE BRIDGE

29-Jun-2012 0609 Orthophosphate, reactive as P

0.098

OUSE BURN AT THREE MILE BRIDGE

16-Aug-2012 1308 Orthophosphate, reactive as P

0.086

OUSE BURN AT THREE MILE BRIDGE

10-Jun-2013 1420 Orthophosphate, reactive as P

0.058

83

Table 45 sample site Jesmond Dene, River Ouseburn and the secondary data obtained from the EA and the calculated seasonal change from the average of the summer and winter months

110138619

OUSE BURN AT THREE MILE BRIDGE

03-Dec-2013 1245 Orthophosphate, reactive as P

0.076

OUSE BURN AT THREE MILE BRIDGE

02-Jan-2014 1419 Orthophosphate, reactive as P

0.034

AVERAGE SUMMER MONTHS 0.119

WINTER MONTHS 0.071

SEASONAL DIFFERENCE

0.049

SITE NAME DATE OF SAMPLE

TIME OF SAMPLE

SAMPLE TEST VALUE (mg/l)

SKERNE AT SOUTH PARK DARLINGTON

18-Jan-2008 1200 Orthophosphate, reactive as P

0.235

SKERNE AT SOUTH PARK DARLINGTON

27-Feb-2008 1457 Orthophosphate, reactive as P

0.645

SKERNE AT SOUTH PARK DARLINGTON

19-Jun-2008 1330 Orthophosphate, reactive as P

0.355

SKERNE AT SOUTH PARK DARLINGTON

30-Jul-2008 1125 Orthophosphate, reactive as P

0.445

SKERNE AT SOUTH PARK DARLINGTON

14-Aug-2008 1249 Orthophosphate, reactive as P

0.290

SKERNE AT SOUTH PARK DARLINGTON

03-Dec-2008 1457 Orthophosphate, reactive as P

0.280

SKERNE AT SOUTH PARK DARLINGTON

12-Jan-2009 1316 Orthophosphate, reactive as P

0.261

SKERNE AT SOUTH PARK DARLINGTON

06-Feb-2009 1154 Orthophosphate, reactive as P

0.160

SKERNE AT SOUTH PARK DARLINGTON

04-Jun-2009 1258 Orthophosphate, reactive as P

0.329

SKERNE AT SOUTH PARK DARLINGTON

10-Jul-2009 1339 Orthophosphate, reactive as P

0.348

SKERNE AT SOUTH PARK DARLINGTON

31-Jul-2009 1305 Orthophosphate, reactive as P

0.215

SKERNE AT SOUTH PARK DARLINGTON

12-Aug-2009 1328 Orthophosphate, reactive as P

0.265

SKERNE AT SOUTH PARK DARLINGTON

10-Dec-2009 0811 Orthophosphate, reactive as P

0.149

SKERNE AT SOUTH PARK DARLINGTON

18-Jan-2010 1439 Orthophosphate, reactive as P

0.143

SKERNE AT SOUTH PARK DARLINGTON

08-Feb-2010 1511 Orthophosphate, reactive as P

0.190

SKERNE AT SOUTH PARK DARLINGTON

12-Dec-2013 1151 Orthophosphate, reactive as P

0.241

84

Table 46 sample site Three Mile Bridge, River Ouseburn and the secondary data obtained from the EA and the calculated seasonal change from the average of the summer and winter months

110138619

AVERAGE SUMMER MONTHS 0.321

WINTER MONTHS 0.256

SEASONAL DIFFERENCE

0.065

SITE NAME DATE OF SAMPLE

TIME OF SAMPLE

SAMPLE TEST VALUE (mg/l)

LEVEN AT MIDDLETON WOOD

17-Jan-2010 1318 Orthophosphate, reactive as P

0.074

LEVEN AT MIDDLETON WOOD

18-Feb-2010 1015 Orthophosphate, reactive as P

0.103

LEVEN AT MIDDLETON WOOD

15-Jun-2010 1415 Orthophosphate, reactive as P

0.331

LEVEN AT MIDDLETON WOOD

09-Jul-2010 1035 Orthophosphate, reactive as P

0.375

LEVEN AT MIDDLETON WOOD

09-Aug-2010 1140 Orthophosphate, reactive as P

0.399

LEVEN AT MIDDLETON WOOD

14-Dec-2010 1207 Orthophosphate, reactive as P

0.104

LEVEN AT MIDDLETON WOOD

17-Jan-2011 1218 Orthophosphate, reactive as P

0.126

LEVEN AT MIDDLETON WOOD

14-Feb-2011 1230 Orthophosphate, reactive as P

0.117

LEVEN AT MIDDLETON WOOD

22-Jun-2011 1110 Orthophosphate, reactive as P

0.409

LEVEN AT MIDDLETON WOOD

05-Jul-2011 1000 Orthophosphate, reactive as P

0.423

LEVEN AT MIDDLETON WOOD

19-Jul-2011 1151 Orthophosphate, reactive as P

0.343

LEVEN AT MIDDLETON WOOD

19-Aug-2011 1107 Orthophosphate, reactive as P

0.288

LEVEN AT MIDDLETON WOOD

04-Jan-2012 1131 Orthophosphate, reactive as P

0.128

LEVEN AT MIDDLETON WOOD

30-Jan-2012 1140 Orthophosphate, reactive as P

0.150

LEVEN AT MIDDLETON WOOD

23-Feb-2012 1217 Orthophosphate, reactive as P

0.264

LEVEN AT MIDDLETON WOOD

12-Jun-2012 0848 Orthophosphate, reactive as P

0.131

85

Table 47 sample site South Park Darlington, River Skerne and the secondary data obtained from the EA and the calculated seasonal change from the average of the summer and winter months

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LEVEN AT MIDDLETON WOOD

19-Jun-2012 0841 Orthophosphate, reactive as P

0.174

LEVEN AT MIDDLETON WOOD

28-Jun-2012 1030 Orthophosphate, reactive as P

0.179

LEVEN AT MIDDLETON WOOD

14-Aug-2012 0922 Orthophosphate, reactive as P

0.190

LEVEN AT MIDDLETON WOOD

08-Jan-2013 1124 Orthophosphate, reactive as P

0.147

LEVEN AT MIDDLETON WOOD

06-Feb-2013 1156 Orthophosphate, reactive as P

0.088

LEVEN AT MIDDLETON WOOD

04-Jun-2013 1315 Orthophosphate, reactive as P

0.125

LEVEN AT MIDDLETON WOOD

25-Jun-2013 0912 Orthophosphate, reactive as P

0.165

LEVEN AT MIDDLETON WOOD

08-Aug-2013 1205 Orthophosphate, reactive as P

0.342

LEVEN AT MIDDLETON WOOD

02-Dec-2013 1132 Orthophosphate, reactive as P

0.146

LEVEN AT MIDDLETON WOOD

03-Jan-2014 1140 Orthophosphate, reactive as P

0.112

AVERAGE SUMMER MONTHS 0.277

WINTER MONTHS 0.130

SEASONAL DIFFERENCE

0.147

86

Table 48 sample site Middleton Wood, River Leven and the secondary data obtained from the EA and the calculated seasonal change from the average of the summer and winter months

Table 49 Table created from Neal et al. (2005) data on water composition of B and SRP immediately after STWs

B ug/l of water directly after STW

SRP ug/l of water directly after STW

588 50131054 9154500 5207409 4384384 4287641 6064

Average - 596 Average - 5685

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8.3 Other

87

Table 50 A table of the key pressures being applied on phosphorus control in rivers. From Mainstone and Parr (2002)

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88

Figure 35 4 graphs to show the concentrations of TP when point source contributes (a) 0 – 25% (b) 25- 50 % (c) 50 – 75% and (d) 75-100% of the phosphorus load. From Bowes et al. (2005)

Table 51 Summary of the NRBD sectors identified that are preventing good status to be reached. From EA (2013)

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School of Geography, Politics and Sociology

Fieldwork Risk Assessment Form

This risk assessment form should be completed electronically and approved and signed by the principal investigator/module leader, and in case students are involved the School Safety Officer. Guidance on completing this form is provided in the HSE guidance Five Steps to Risk Assessment which can be downloaded from the HSE website or Safety Office website. It is the responsibility of the person in charge of the fieldwork that this risk assessment is made available to all participants of the fieldwork.

Title of project/module: DISSERTATION:

Can a ratio of boron to phosphorus be used to infer the influence of point source effluents on the phosphorus levels in rivers?

PI/Module Leader

Dr Steve Juggins

Dr Andy Large

Dr Martyn Kelly

Other people involved in this Fieldwork

(If needed attach separate Sheet)

Chris Speight

Date(s) 26/11/13

27/11/13

28/11/13

29/11/13

Location(s) River Team

River Ouseburn: Jesmond Dene

Woolsington

River Coquet: Rothbury

River Wear: Wolsingham

Bishop Auckland

Shincliffe

Finchale

River Tyne S: Alston

River Tyne N: Wark

River Wansbeck: Morpeth

River Derwent

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90

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