Investigation of Road Runoff Inputs from the A42 Into the River Mease, UK: Winter 2013/14

Investigation of road runoff inputs from the A42 into the River Mease, UK: winter 2013/14

Final: April 2014

A Taylor, WH Blake*, S Comber, R Goddard, A Fisher, HG Smith, L Gaspar, J Darmovzalova Catchment and River Science Research Group (CaRiS), School of Geography, Earth and Environmental Sciences, Plymouth University, PL4 8AA (*contact: [email protected] )

With Victoria Levett (project manager) and David Fraser (project director) APEM Ltd, Centre for Innovation & Enterprise, Oxford University Begbroke Science Park, Begbroke Hill, Woodstock Road, Begbroke, Oxfordshire, OX5 1PF (APEM Project 412766)

This project is part of the IPENS programme (LIFE11NAT/UK/000384IPENS) which is financially supported by LIFE, a financial instrument of the European Community

Contents pg

1. Introduction 3 1.1 Project Brief 1.2 Road runoff problems: review of literature 1.3 Aims and objectives

2. Methods and approach 12

2.1. Site selection 2.2. Field monitoring 2.3 Water quality sampling and analysis for metals 2.4 Sediment quality sampling and fingerprinting

3. Results and discussion 15 3.1. Hydrographs from Mease channel above and below and at the A42 culvert input 3.2. Total and dissolved metals during sampled storm periods 3.3. Road dust and impacts on river sediment quality

4. Conclusions and recommendations 29 4.1 Key messages 4.2 Recommendations 4.3 Limitations and further work

This project is part of the IPENS programme (LIFE11NAT/UK/000384IPENS) which is financially supported by LIFE, a financial instrument of the European Community’

“The Improvement Programme for England’s Natura 2000 Sites (IPENS), supported by EU LIFE+, is a new strategic approach to managing England’s Natura 2000 sites. It will enable Natural England, the Environment Agency, and other key partners to plan what, how, where and when they will target their efforts on Natura 2000 sites and on land surrounding them. This project is part of the IPENS programme (LIFE11NAT/UK/000384IPENS) which is financially supported by LIFE, a financial instrument of the European Community.” http://www.naturalengland.org.uk/ourwork/conservation/designations/sac/ipens2000.as px

1. Introduction

1.1 Project brief

Road networks can impact on aquatic and marine systems by providing both a contaminant source and a pathway of entry to watercourses. In the Mease catchment, there is concern that runoff from the A42 might be having adverse effects on water and sediment quality, and hence aquatic life. The River Mease and the lower part of Gilwiskaw Brook are special lowland rivers that are designated as a Special Area of Conservation (SAC) under the EU Habitats Directive, and as a Site of Special Scientific Interest (SSSI) under the Wildlife and Countryside Act (as amended). They were designated because the River Mease represents one of the best examples of an unspoilt meandering lowland river, which supports characteristic habitats and species. The River Mease SSSI/SAC supports populations of spined loach (Cobitis taenia) and bullhead (Cottus gobio); two notable species of native freshwater fish that have a restricted distribution in England. The rivers also support populations of white-clawed crayfish (Austropotamobius pallipes), otter (Lutra lutra), and a range of river plants such as water crow-foot (Ranunculus sp.). In this context, the aim of this work programme was to establish if contamination from A42 road runoff reaches the River Mease SSSI/SAC (Site of Special Scientific Interest and Special Area of Conservation) in dissolved or particulate from at levels that exceed environmental quality guidelines.

1.2 Literature Review: Road derived contaminants and impacts on aquatic systems

Contaminants directly associated with roads, which present potential threats to aquatic systems, can be classed as heavy metals, salts and organic molecules (particularly Polycyclic Aromatic Hydrocarbons (PAHs)) (Beasley and Kneale 2002; Boxall and Maltby 1995). Of these, 4 metals (Cd, Hg, Ni and Pb) and 8 PAHs are included in the Water Framework Directive (WFD) list of priority substances (2008/105/EC). Copper and zinc are identified as Specific Pollutants by the UK under the WFD and site- specific standards are set based on ambient water quality (Table 1).These contaminants can be transported into watercourses via runoff and drainage systems or become stored in neighbouring buffer zones (vegetated verges and banks), to be later mobilised under suitable conditions. Once in a watercourse, changing environmental parameters can influence the remobilisation and bioavailability of contaminants with subsequent bioaccumulation affecting ecological status (Trombulak and Frissell 2000). With vehicle-related contamination predicted to increase with rising traffic volumes, roads present a significant threat to aquatic systems (Napier et al. 2008). Literature surrounding the impact of road networks on watercourses is reviewed here focussing on contaminant sources, pathways of entry to watercourses and ecotoxicity.

Sources of road-derived contaminants

Beasley and Kneale (2002) described urban runoff as a complex concoction of potential pollutants (Table 2), with roads acting as a principal source, which is evident when comparing heavily trafficked to lightly trafficked areas (Apeagyei et al. 2011; Wijaya et al. 2012). Of those contaminants derived directly from vehicle use, Napier et al. (2008) estimated Cu, Pb, Zn and PAH contributions from tyre and brake wear, oil leakage and exhaust emission from UK passenger cars (Table 3). Tyre and break wear are key sources of Cu and Zn, which are released as particulates (generally < 125 µm) and become a component of road dust (comprised of emitted particulates and natural sediment) (Robertson and Taylor 2007; Zafra et al. 2011). Concentrations of contaminants in road dust display enrichment in the fine fractions (< 63 µm) (Zafra et al. 2011) (Table 4) and can exhibit spatial and temporal variability related to traffic volumes and road class (Apeagyei et al. 2011) and weather conditions and maintenance practice (Helmreich et al. 2010; Robertson and Taylor 2007). Another potential toxicant often elevated in road dust is Ni, which is commonly associated with engine wear and anthropogenic inputs of Ni to freshwaters have almost doubled each decade since 1930 (Beasley and Kneale, 2002). Napier et al (2008) suggested that vehicle derived concentrations of other potentially toxic metals such as Cd and Hg are likely to reduce owing to manufacturing restrictions imposed under the EU End of Life Vehicles Directive (2000/53/EC). Cu and Zn emissions are, however, likely to continue to increase with traffic volume given their association with tyre and brake wear. It should also be noted that reduction in vehicle emission of certain metals may not be reflected in reductions in potentially labile concentrations in the short-term, owing to storage and remobilisation from contaminant sinks. This has been demonstrated with regard to the persistence of Pb in soils and sediments since the introduction of lead- free fuels (Hagner 2002; Izquierdo et al. 2012; MacKinnon et al. 2011).

Vehicle exhaust catalysts are a major source of Platinum Group Elements (PGE) such as Pt, Pd and Rh, with such elements commonly found to be elevated above background concentrations in road dust and roadside soil (Table 4). Jackson et al. (2007) reported a global concentration range in road dust of 0.22 – 2.25 and 0.22 – 0.56 µg g-1 for Pt and Pd respectively. A general consistency in ratios between Pd, Pt and Rh in environmental samples across urban areas suggests that vehicles are a dominant source of PGE emission (Jackson et al. 2010; Prichard et al. 2009a) with clear spatial patterns evident in sediment concentrations, which trend towards higher values close to road sources (Prichard et al. 2008).

Table 1: Examples of Environmental Quality Standards of some priority substances

Priority Substance EQS (Maximum Allowable Concentration) Inland Surface Waters (µg L-1) Anthracene 0.1

Cd (and its compounds) a≤0.45 b0.45 c0.6 d0.9 e1.5

Fluoranthene 0.12

Hg (and its compounds) 0.07

Naphthalene 130

Ni (and its compounds) 34

PAH: Benzo(a)pyrene 0.27 Benzo(b)fluoranthene 0.017 Benzo(k)fluoranthene 0.017 Benzo(g,h,i)perylene 8.2 ×10-3

Indeno(1,2,3-cd)pyrene f1.7 ×10-4

Copper 6 or 1.0 as bioavailable metalf,g

Zinc 50 or 10.9 as bioavailable metalf,g a Class 1: <40mg CaCO3/L b Class 2: 40 to <50mg CaCO3/L c Class 3: 50 to <100mg CaCO3/L d Class 4: 100 to <200mg CaCO3/L e Class 5: ≥200mg CaCO3/L fAnnual Average concentration g Determined using the Biotic Ligand Model

Table 2: Sources of contaminants in road runoff. Adapted from Beasley and Kneale (2002).

Vehicle Surface material Surface debris Brakes Tyres Body Fuel/oil Concrete Asphalt Salt Litter

Cadmium * * Chromium * Copper * * Iron * * Lead * * * * Nickel * Vanadium * Zinc * * * Chlorides * SolidsO * * SolidsI * * * * PAHs * * Phenols *

O Organic I Inorganic

Table 3: Estimates of environmental inputs from passenger cars in the UK in 2003. Data taken from Napier et al. (2008)

Tyre wear Brake wear Oil loss (t) Exhaust (t) (t) (t)

Copper 0.3 24 0.038 0.4 Lead 1.0 1.5 0.02 1.1 Zinc 990 44 2.3 1.0 PAHs 21.7 nv 320 130

Aside from metals, road networks are also sources of salts and PAHs, both of which can impact on the status of freshwater systems (Boxall and Maltby 1995; Cañedo- Argüelles et al. 2013). The use of de-icing salts during cold weather has been shown to raise the salinity of aquatic systems and there is increasing concern over the role of secondary salinisation on freshwater ecology (Cañedo-Argüelles et al. 2013; Gillis 2011). De-icing salts largely consist of soluble chlorides (NaCl) and also soluble sulphates (CaSO4) and anti-caking agent NaFe(CN)6. Moy and Crabtree (2003) suggested a typical application rate of road salt during cold weather in the UK of 0.01 kg m-2 and estimated an annual application totalling 10,389 kg for a short section (~ 700 m in length) of the M4 in England. It is unsurprising, therefore, that high

concentrations of NaCl can be recorded in receiving waters during runoff events which follow salt applications (Neal et al. 2011).

Table 4: Examples of contaminant concentrations in road dust and road runoff sediment with ambient background concentrations in soils from rural locations

Road Dust µg g-1 a Runoff sediment µg g-1 Background µg g-1 b c

Cd 0 – 13 <1.0 – 1.01 0.39 Cu 16.4 – 6688 95.7 – 151 20.6 Ni 0 – 636 19 – 21.5 21 Pb 0 – 199 128 – 170 52.5 Zn 81 – 3164 401 – 487 81.3

Pd 0.026 – 0.45d <0.15 – <3.7 <0.002e Pt 0.027 – 0.41d <0.2 – <2.7 <0.002e

∑16 PAH 20.8 – 37.9 1.93f,g* a Charlesworth et al.(2003) b Moy and Crabtree (2003) c Barraclough (2007) d Jackson et al. (2007) e Prichard et al. (2009) f Nam et al. (2008) g Creaser et al. (2007) * Summation of the 16 PAH compounds included in Moy and Crabtree (2003) derived from the background concentrations of 15 PAHs in Creaser et al. (2007) with the addition of a background concentration for Naphthalene from Nam et al. (2008)

PAHs constitute a wide range of organic compounds, with 8 featuring in the WFD list of priority substances (2008/105/EC). The primary source of PAHs in the environment is the incomplete combustion of organic materials with road transport dominating the contribution to total emissions in the UK (Creaser et al. 2007). Numerous studies document elevated PAH concentration in road dusts (e.g. Dong and Lee, 2009) and provide evidence of their transport and toxicity in freshwaters (e.g. Boxall and Maltby, 1995; Krein and Schorer, 2000).The transport and fate of PAHs in the environment is largely controlled by the physical and chemical characteristics of the molecules with PAHs of higher molecular weights generally less volatile than those of low molecular weights, such as naphthalene, and of lower solubility in water (Dong and Lee 2009). High molecular weight PAHs (such as benzo(a)pyrene) also tend to show greater association with fine sediment fractions (<75 µm) (Dong and Lee 2009; Krein and Schorer 2000) and have higher Toxic Equivalency Factors (TEF) (toxicity relative to a

reference compound of high toxicity). These compounds are likely to be a significant component of the PAH content in urban road dust (Dong and Lee 2009).

Pathways of road-derived contaminant entry to watercourses

Road surfaces not only act as a source of potential pollutants but also serve to alter hydrology, providing an effective pathway to receiving waters via runoff and drainage discharge (Fletcher et al. 2013; Moy and Crabtree 2003). Contaminated highway runoff has been shown to discharge directly to surface waters (Moy and Crabtree 2003) and transport of road-derived contaminants by streams and rivers has been documented(Carter et al. 2006; Carter et al. 2003; Prichard et al. 2008). Carter et al. (2003) suggested that up 22 % of suspended sediment load in the lower reaches of the Rivers Aire and Calder in the UK was of road surface origin. Furthermore, these contributions corresponded to elevated levels of metals such as Pb and Cu (Carter et al. 2006). Metal contaminants in road dust are typically concentrated in the fine sediment fractions (< 63 µm), which are readily mobilised. Fine sediments have thus been shown to dominate the metal loading in road runoff ( e.g. Zhao et al., 2010). However, some studies that have explored the partitioning of metal contaminants in solid and liquid phases also demonstrate that transport in the dissolved phase can be important (e.g. Sansalone et al., 1996). Low pH conditions, particularly on asphalt surfaces (Wicke et al. 2012) can lead to a greater proportion of metals in dissolved form in highway runoff. Total metal loading is shown to be elevated in the ‘first flush’ of runoff during an event (Wicke et al. 2012) owing to the removal of trapped contaminants. This would suggest that preceding dry weather may be an important factor to consider when studying contaminant runoff although studies report conflicting importance of preceding conditions owing to rapid particle accumulation thresholds and additional influences of weather patterns and road maintenance (Helmreich et al. 2010; Wicke et al. 2012). The temporal dynamics of road dust transport and interactions in runoff solutions are important considerations when taking into account contaminant toxicity. The toxicity of road dust at heavily trafficked sites has been shown to fluctuate according to the time of interaction with rainwater solutions (Watanabe et al. 2011).

Pathways of contaminant entry to watercourses can be disrupted by the presence of vegetative sinks, with roadside buffers showing elevated concentrations of road- derived contaminants (Zehetner et al. 2008). Drainage systems can also incorporate various filtering media to retain runoff contaminants either via direct sorption or disruption of flow pathways, which encourages sedimentation and enhanced contaminant retention. Piguet et al. (2008) demonstrated the effectiveness of roadside infiltration slopes, which enabled filtration and retention of contaminants (particularly metals) by soils. The high pH (~8) of the soil columns aided the sorption process and the authors stated that, where acidic soils existed, the efficiency of retention may be reduced. Hares and Ward (2004) studied a vegetative treatment system receiving runoff from the A34 Newbury Bypass in England. A reed bed system was shown to

effectively retain metals such as Cu and Zn by disrupting hydraulic flow and enhancing sedimentation.

Bioaccumulation & toxicity to aquatic organisms

Numerous studies document the toxicity of elevated (above background) levels of metals in aquatic systems (e.g. Christensen et al. 2006; Dorchin and Shanas 2010; Hansen et al. 2007) with some providing clear linkage of reduction in species richness to road and traffic density (Beasley and Kneale 2002). Elements such as Cu, Ni and Zn are essential at trace levels but display toxic effects at higher concentrations. Beasley and Kneale (2002) provide a review of research surrounding aquatic toxicity of road-related contaminants with controlled studies demonstrating acute and chronic toxicity of metals on macroinvertebrate species. Translation of results to the field, however, appear to be site specific and influenced by varying degrees of tolerance to elements and chemical form (and hence bioavailability). Despite this, shifts in community assemblage can be a useful indicator of metal contamination with studies demonstrating increased densities of Chironomidae (non-biting midge) at metal contaminated sites and decreased densities of metal sensitive mayfly families such as Baetidae, Heptageniidae and Ephemerellidae. It is of interest to note, however, that the use of invertebrate indices did not indicate significant impact on sites affected by highway runoff in the study of Moy and Crabtree (2003), despite evidence of contaminant loading.

The predominant species of Ni in freshwater systems is Ni2+ at a pH of 5-9, which shows a high affinity for clay materials and, as such, sediment concentrations are often several orders of magnitude higher than those in the dissolved phase. Typical dissolved concentrations in UK rivers range from 0.007-0.0037 mg L-1 rising to around 0.012-0.073 mg L-1 at contaminated sites. Ni mobility is promoted by the presence of low pH, chloride, nitrate, sulphate and dissolved organic substances. Mobility is likely 3- to be reduced through precipitation under increasing pH and the presence of PO4 , 2- - CO3 , OH and H2S and by sorption processes in the presence of materials that bond more strongly to metals e.g. organic compounds and metal hydroxides. Little is known of the absolute requirements of Ni for growth and similarly, the mechanisms of toxicity but studies do report sub-lethal responses to Ni exposure such as reduced growth rate. Data are available with regard to the lethal effects of Ni on macroinvertebrates with -1 examples including an LC50 (48hr) for Chironomus (midge larvae) of 79-169 mg L and concentrations of 0.25 mg L-1 preventing completion of the life cycle of Clistoronia magnificans (caddisfly) (Beasley and Kneale, 2002).

Below pH 6 Cu is commonly in the form Cu2+ and with increasing pH is likely to precipitate out of solution as copper carbonate or cupric hydroxide. The bioavailability of Cu appears to be site specific depending on geochemical associations and, like Ni, little is known of the mechanisms of toxicity but there is some suggestion of effects on respiratory systems. Chronic toxicity has been shown in Gammarus pseudolimnaeus with 100% mortality following 6 weeks exposure to 0.028 mg Cu L-1 (at 44 mg L-1

-1 CaCO3). LC50 (96hr) for the same species is reported as 0.02 mg L . Zn, also essential in trace levels, is susceptible to mobility in solution (Meland et al. 2010a; Robertson et al. 2003) and has been shown to affect Gammarus pulex reproduction at concentrations in excess of 0.3 mg L-1. (Beasley and Kneale 2002 and references therein).

Salmonid species can be sensitive to elevated concentration of some metals in aquatic systems, with exposure to increased levels of metals such as Al, Cd, Cu, Pb, Sb and Zn stimulating oxidative stress in brown trout (Salmo trutta) (Hansen et al. 2007; Heier et al. 2009; Meland et al. 2011). Heier et al. (2009) investigated the stress response (using biomarkers such as increased blood glucose or depleted Na and Cl in plasma) of brown trout exposed to elevated concentrations of metals in contaminated stream water for up to 23 days. Total dissolved concentrations of metals in the stream water ranged from c. 367-550 µg L-1 for Al, 11-18 µg L-1 for Cu, 15-46 µg L-1 for Pb and 2-3 µg L-1 for Sb. Metals were shown to rapidly accumulate (< 5 days) in the gills and concentrate in the liver after a longer time period (c. 11 days). Al was found to be a major stressor influencing severe stress responses such as increased blood glucose and weight loss although the authors concluded that it was the exposure to multiple metal stressors, which influenced the overall reduced condition of the fish.

Meland et al. (2010a) also suggested that elevated levels of contaminants in road wash-off contributed to reduced growth of juvenile brown trout. Samples of road wash contained significantly enriched levels of Cu, Pb and Zn likely owing to sources from tyre and brake wear and erosion of asphalt and galvanised materials. Cu and Pb were found to be largely associated with particulates and colloids, while Zn tended to occur as low molecular weight species and hence in the dissolved (and more bioavailable) phase. Dissolved concentrations of Cu and Zn were well in excess of EU Environmental Quality Standards (EQS) (0.09 µg L-1) at 43 µg L-1 and 808 µg L-1 respectively. The authors suggested that such concentrations could affect fish energy uptake owing to effects on metabolism influencing the reallocation of energy from growth to detoxification and repair, which is further supported by Meland et al. (2010b). A subsequent study identified mechanisms of toxicity to brown trout which included immunosuppression and oxidative damage in cells (Meland et al. 2011).

Fewer studies focus on the bioaccumulation and toxicity of PGEs although there is an emerging body of evidence to suggest bioavailability and sub-lethal effects at environmentally relevant concentrations (Ek et al. 2004; Sures et al. 2001; Zimmermann et al. 2004). Pd is likely to be more readily mobilised from road sources than Pt or Rh owing to greater solubility (as chloride species) and has been shown to be bioavailable for aquatic organisms (Sures et al. 2001). Although Pt is likely to be less soluble than both Pd and Rh under natural conditions (c. 10 % solubility of vehicular emissions) (Ek et al., 2004), there is evidence to suggest sub-lethal effects on freshwater invertebrates at concentrations > 0.1 µg L-1 (Osterauer et al. 2011; Osterauer et al. 2009), which may have relevance given reports of total Pt in composite road runoff samples of up to 120 µg L-1 (Moy and Crabtree 2003).

The use of de-icing salts on highways has been shown to directly contribute to secondary salinisation in freshwater systems. The toxicity of this depends on a complex interaction of factors including both the level and extent of salinisation and the natural background ionic composition of the receiving waters (Cañedo-Argüelles et al. 2013). In some cases, increased salinity can reduce aquatic toxicity by complexing with free metal species, thus, reducing their bioavailability. In general, however, little is documented of the interaction between salts and other chemical species in contaminated waters although Meland et al. (2010c) suggested increased salinity enhanced metal accumulation in the gills of brown trout. Cañedo-Argüelles et al. (2013) provide a comprehensive review of research surrounding the ecological effects of secondary salinisation with strong evidence to suggest adverse effects at the individual and community level. Numerous studies document increased stress effects, such as increased O2 consumption, in aquatic fauna and reduced species richness at the community level. Across the literature cited, adverse effects have been reported at conductivities between 1-3 mS cm-1 with reductions in species richness at levels > 1.5 mS cm-1. Fish tend to have a broader tolerance range although early life stages are shown to be more sensitive. Given the likely rapid elevation in salinity resulting from runoff following de-icing salt applications in the UK, LC50 data provide a useful indication of short term lethal effects. For example, 48 hr LC50 for Ephemoptera spp. has been reported between 5 and 20 mS cm-1. It should be noted, however, that the lower limit of 5 mS cm-1 was not evident in the receiving waters in the Moy and Crabtree (2003) study of UK highway runoff (< 2 mS cm-1 downstream of runoff inputs ).

According to Beasley and Kneale (2002) PAHs are likely to be the most toxic organic compounds present in urban runoff, which is supported by studies documenting the adverse effects of PAHs to invertebrate and fish species (e.g. Arkoosh et al. 2011; Boxall and Maltby 1995). PAHs are metabolised by fish and are known to have mutogenic and/or carcinogenic effects and can also act as endocrine supressors and immunotoxins. PAHs are lipophilic and enter cells via passive diffusion where they can exhibit binding with cellular macromolecules (e.g. proteins, DNA) causing mutagenesis and cancer (Tuvikene 1995). Biomarkers such as the induction of enzyme (e.g. CYP1A) activity can signal a response of fish to PAH uptake and, thus, can identify exposure of fish to PAH sources (Jönsson et al. 2006; Roberts et al. 2006). These biomarkers are often used alongside chemical determination for environmental risk assessment. Numerous studies report the use of Toxic Equivalency (TEQ) during field assessment of PAH contamination. TEQs are given as a toxicity-weighted mass and are derived from the concentrations of individual compounds in a sample and their Toxic Equivalency Factors (TEF) (Heimann et al. 2011; Wölz et al. 2010) with higher TEQ values indicating greater toxicity. TEQs have been shown to display positive relationships with total PAH concentration although there may be examples where high TEQs do not correspond to total concentrations. For example Dong and Lee (2009) showed road dust collected from heavily trafficked areas to have higher TEQs than those from industrial regions, despite similar total concentrations. The findings identified dominace of higher molecular weight PAHs (e.g. benzo(a)pyrene)

in the samples from areas of high traffic volumes. PAH toxicity can vary according to the type of compound, across different aquatic species and as a factor of exposure levels and time. Concentrations of benzo-a-pyrene (a WFD priority substance) required to induce sub-lethal effects in rainbow trout (Oncorhynchus mykiss) eggs have been demonstrated between 0.08-0.21 µg L-1. Naphthalene, also a priority substance, has been shown to exert lethal effects on hatching and 4 day post-hatched -1 rainbow trout with LC50 values of 110 and 120 µg L respectively (CCME 1999).

In summary, the literature suggests that major roads can be a source of a concoction of vehicular-derived contaminants, which includes metals, salts and PAHs. Many of these contaminants are associated with readily mobilised fine sediment fractions and exhibit elevated concentrations (above background) in road dusts. These particles are actively transported to aquatic systems during runoff events via drainage systems, with strong evidence of contaminant loading in runoff samples and immediate receiving waters. There is also evidence of the transport of road-derived particulates through catchment systems. Whilst sediment bound contaminants appear to dominate loadings in road runoff, transport in the dissolved phase can also be of importance in some environments. Whether road-derived contaminants exhibit toxicity in aquatic systems is dependent on a complex interaction of factors including chemical speciation, interaction between contaminants, environmental parameters in receiving waters, the sensitivity of aquatic biota and the level of exposure. Nevertheless, there is a large body of evidence to suggest that common vehicular-derived contaminants can have adverse effects on aquatic systems both at the individual level (sub-lethal and lethal effects) and the community level by altering species assemblages.

1.3 Objectives and scope

The principal aim was to identify if road runoff inputs are delivering contaminants, in water and sediment, to the River Mease SSSI/SAC (Site of Special Scientific Interest and Special Area of Conservation) main channel via culverted inputs from drains. The first objective was to set up monitoring points to explore temporal patterns in water turbidity and conductivity above and below an identified culvert input. The second objective was to collect water samples during a high flow event above and below the culvert to assess any temporal dynamics in heavy metal inputs. The third objective was to assess whether road runoff leads to input of contaminated sediment to the channel bed using a simple sediment fingerprinting approach.

Figure 1: Sample site locations.

2. Methods and approach

2.1. Site selection

Three sites for monitoring and sampling were selected in consultation with local Natural England staff. A main culvert input that captured road runoff was identified for monitoring with additional river monitoring sites above and below this point (Figure 1). Site 1 (Figure 2a) was located upstream of the culvert west of Side Hollows Farm. Site 2 was located at the culvert (Figure 2b), and Site 3 (Figure 2c) was located downstream of the culvert and AB produce, the local vegetable processing plant.

2.2. Field monitoring

At each site, a Troll 9500 multiparameter sonde was deployed in November 2013 to measure water depth, turbidity (largely related to suspended solids) and specific electrical conductance (largely related to dissolved solids) on a continuous basis at 15 minute intervals. The objective of the monitoring was to capture evidence of any major input of sediment or flushing of salt-rich waters from the road surface to evidence the link between road and channel. They also provide the storm hydrograph context for the water quality sampling (section 2.3).

(a)

(b)

(c)

Figure 2: River Mease A42 study monitoring and sampling sites (a) site 1, (b) site 2 and (c) site 3.

2.3 Water quality sampling and analysis for metals

The first phase of water quality sampling was undertaken using rising limb samplers (See Figure 2b). These represent a low technology solution to collecting the first flush of materials in a storm event, often before an automatic system is triggered by a rise in stage. These were deployed during the first period of monitoring when no data on hydrograph rising limb dynamics was available to set automatic samplers.

In January 2014, ISCO automatic water samplers (Figure 3) were deployed at sites 1 and 3 to capture water samples at 30 minute intervals during storm events above and below the culvert input of road runoff. Once one event had been successfully captured at both locations, the samplers were reset to cover a 6 day period over a weekend, sampling at 6 hourly intervals to test for any change in water quality pattern at greater than event time scale (e.g. in relation to local works on the industrial estate between the sites).

Figure 3: ISCO sampler in situ at site 1

Water samples were analysed for a range of metals (total and dissolved) following standard protocols. A subsample was taken for the well-mixed sample and digested in acid in a Mars microwave digestion system. A second sample was filtered to < 0.45 µm and acidified. Both samples were analysed by ICP-OES and ICP-MS for Ca, Mn, Fe, Ba, Cu, Zn, Al, P, B, V, Cr, As, Se, Mo, Ru, Rh, Pd, Ag, Cd, Sb, Pt , Tl and Pb based on literature reviewed above.

2.4 Sediment quality sampling and fingerprinting

Sediment was collected from the river bed using the stilling well method described in the Sediment Fingerprinting Report (report 1) at sites 1 and 3. In addition samples of road dust were collected from the A42 surface and sediment samples were collected from the culvert and AB produce settling ponds. The upstream sediment, culvert sediment and AB produce sediment samples were treated as sources and the downstream sediment as the receptor to evaluate relative contributions of sediment from the catchment road and works. All sediments were analysed by XRF for major and minor element geochemistry as per sediment samples in Report 1.

3. Results and discussion

3.1. Hydrographs from Mease channel above and below and at the A42 culvert input

Hydrographs derived from the stage (river water depth) data for each sampling site (1-3) are shown in Figure 4. Following initial installation of sampling equipment on 26th November, baseflows were maintained until mid-December, with the first major event (Event 1) occurring on 18th December. A series of storm events then followed in succession with high flows maintained throughout the remaining period, relative to the flow regimes pre-December, 2013. From figure 4 it is evident that flow patterns correspond well across all sites, which supports the comparison of contaminant dynamics between locations.

2.0

1.5

1.0 Stage(M)

0.5

0.0 8 22 5 19 2 16 Nov2013 Dec2013 Jan2014 Feb2014 SITE 1 STAGE SITE 2 STAGE SITE 3 STAGE

Figure 4: Hydrograph (stage = water depth in metres) for the study period for sites 1 to 3. Major events are labelled 1-14

3.2. Total and dissolved metals during sampled storm periods

1,600 1,400 1,200 1,000 800

uS/cm 600 400 200 0

1.8 1.6 1.4 1.2 1.0

Stage(m) 0.8 0.6 0.4 0.2 0.0

1,000

800

600

FNU 400

200

0 06:00 12:00 18:00 00:00 06:00 12:00 18:00 00:00 06:00 18Dec2013 19Dec2013 20Dec2013 SITE 1 COND SITE 1 STAGE SITE 1 TURBIDITY

Figure 5: Conductivity, stage and turbidity at site 1 (upstream) for the first major event (Event 1) of the study period

1,600 1,400 1,200 1,000 800

uS/cm 600 400 200 0

1.8 1.6 1.4 1.2 1.0

Stage(m) 0.8 0.6 0.4 0.2 0.0

1,000

800

600

FNU 400

200

0 06:00 12:00 18:00 00:00 06:00 12:00 18:00 00:00 06:00 18Dec2013 19Dec2013 20Dec2013 SITE 2 COND SITE 2 STAGE SITE 2 TURBIDITY

Figure 6: Conductivity, stage and turbidity at site 2 (road drain culvert) for the first major event (Event 1) of the study period

1,600 1,400 1,200 1,000 800

uS/cm 600 400 200 0

1.8 1.6 1.4 1.2 1.0

Stage(m) 0.8 0.6 0.4 0.2 0.0

1,000

800

600

FNU 400

200

0 06:00 12:00 18:00 00:00 06:00 12:00 18:00 00:00 06:00 18Dec2013 19Dec2013 20Dec2013 SITE 3 COND SITE 3 STAGE SITE 3 TURBIDITY

Figure 7: Conductivity, stage and turbidity at site 3 (downstream) for the first major event (Event 1) of the study period

Storm 1 (Figures 5, 6 and 7) was sampled using in-situ rising limb sampler bottles which were put in place during the first phase of monitoring (when preliminary river stage data were collected to determine ISCO deployment parameters).

Figure 7 shows that there was a rapid increase in turbidity at the downstream site during the rising limb of the event. Turbidity data for Site 1, however, tends to follow a more gradual increase with flow and it is difficult to interpret data from the culvert owing to some ‘flatlining’ of the signal likely owing to fouling from debris. It is likely that the rapid increase in turbidity at the downstream location was influenced by the A42 culvert as there were no other significant culvert inputs below the culvert aside from the AB Produce works.

Conductivity data trend towards decreasing values during peak flows as can be expected from dilution by recent rainfall, which rapidly enters a system. A slight increase in conductivity is evident during the very early stages of the rising limb for the upstream and downstream locations, which could support the notion of a first flush of dissolved material, particularly since this was the first event following a dry period. At the culvert site, notwithstanding potential for probe fouling, the observed first dilution event followed by a sharp increase in conductivity is evident during the rising flow limb, which indicates an initial flush of rainwater and then dissolved substances (including road salt) from the road.

The rising limb samplers were fixed to the bank and designed to fill as river levels rose. Six samplers were filled during storm event 1 at each site and the timing of these fills was controlled by local river stage conditions. Sediment and metals data from these samples are summarised in Table 5 and full data are available in the accompanying electronic appendix (Appendix to Report 3_A42.xlsx). The lower site, downstream of the culvert appears to have captured a very early turbidity event (suspended sediment concentration = 6 g L-1) that was not observed upstream (cf above probe observation). It is presumed that this event occurred prior to any rise in main river stage and hence the lower sampler at the culvert itself was not filled at this time. This sample skewed the mean metal concentration data of the sample set and hence, for clarity, the summary data from site 3 are presented both with (n = 5) and without (n = 4) this early sample included (Table 5).

The mean concentration of a wide selection of metals is elevated in the downstream sample set (marked in bold in Table 5) and these include key indicator metals for road runoff e.g. Cu, Pd, Cd, Pt. It is important to note that Cu and Pt, and some other metals, remain elevated in the mean concentration even when the first high turbidity sample from downstream is removed. These data indicate that there is an input of metal contaminants from the A42 culvert into the Mease main channel but that the main input occurs very early in the storm hydrograph.

Event 10 was sampled upstream and downstream of the culvert using the ISCO automatic water sampler. Event 10 consisted of multiple small peaks (Figure 4) and the autosampler captured samples for the rising limb and the first of these peaks. Turbidity at both the upstream and downstream locations follows the pattern of the rising river stage, with conductivity showing an overall decrease owing to dilution. An increase in conductivity is evident before the peak flow for both sites, suggesting an input of dissolved material again. It is important to note this is at both sites above and below the culvert indicating that there are dissolved substance inputs above the study reach, potentially from other road inputs or catchment sources.

Table 5: Summary suspended sediment and total metals data for each set of rising limb samplers above, at and below the A42 culvert.

SSC Mg Ca Mn Fe Ba Cu Zn Al P B V Cr g L-1 mg L-1 mg L-1 mg L-1 mg L-1 mg L-1 mg L-1 mg L-1 mg L-1 mg L-1 mg L-1 µg L-1 µg L-1 Upstream A42 culvert Mean 0.162 24 73 0.29 5.4 0.11 0.82 1.1 8.3 0.31 0.14 23 19 (n = 6) se 0.080 3 8 0.10 2.1 0.02 0.07 0.6 3.1 0.08 0.02 11 8 2 At A42 culvert Mean 0.143 13 52 0.18 5.4 0.20 1.52 0.8 4.4 0.47 0.08 9 10 (n=6) se 0.061 2 10 0.05 2.1 0.05 0.24 0.2 1.4 0.12 0.01 3 5 Downstream A42 culvert Mean 1.590 53 109 2.90 61.8 0.67 1.18 1.3 84.1 2.93 0.35 104.2 84 (n=5) se 1.057 20 19 2.45 53.3 0.51 0.21 0.5 71.9 2.48 0.15 86.3 69 Downstream A42 culvert Mean 0.469 31 88 0.22 3.5 0.11 1.26 0.7 5.4 0.22 0.18 10 8 (n=4) se 0.304 2 4 0.06 1.7 0.02 0.23 0.2 2.8 0.02 0.01 5 4

Co Ni As Se Mo Ru Rh Pd Ag Cd Sb Pt Tl Pb µg L-1 µg L-1 µg L-1 µg L-1 µg L-1 µg L-1 µg L-1 µg L-1 µg L-1 µg L-1 µg L-1 µg L-1 µg L-1 µg L-1 Upstream A42 culvert Mean 5.8 15 4 0.62 5.2 0.001 0.001 0.04 0.3 0.40 3.86 0.005 0.193 17 (n = 6) se 2. 1 4 1 0.14 1.1 0.000 0.00 0.0 0.0 0.11 2.19 0.001 0.067 6 At A42 culvert Mean 2.3 8 3 0.48 7.0 0.000 0.001 0.05 0.3 0.59 10.29 0.004 0.100 22 (n=6) se 0.87 3 1 0.08 1.8 0.000 0.00 0.00 0.0 0.22 5.15 0.001 0.017 13 Downstream A42 culvert Mean 29 58 14 2.03 14.0 0.003 0.004 0.19 0.7 1.02 3.42 0.027 0.859 60 (n=5) se 24 44 11 1.23 3.3 0.001 0.02 0.06 0.2 0.67 0.53 0.015 0.464 48 Downstream A42 culvert Mean 30 10 2 0.68 11.2 0.004 0.004 0.15 0.5 0.29 2.84 0.029 0.364 8 (n=4) se 0.9 1 1 0.02 2.5 0.002 0.002 0.05 0.0 0.02 0.10 0.017 0.121 3

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Figure 8: Conductivity, stage and turbidity for site 1 during event 10

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Figure 9: Conductivity, stage and turbidity for site 3 during event 10

Event 10 chemographs for selected metals are shown below. Raw data for other analysed elements are provided in the electronic resource accompanying this report.

Figures 10a and 10b show total and dissolved Cu concentrations across the sampled storm event for the upstream and downstream locations. Total Cu generally follows the pattern of turbidity highlighting that Cu concentrations are dominated by sediment-bound fractions. Estimated maximum sediment concentrations in suspended sediment (mg kg-1) (derived from: (total –dissolved)/SSC) show similar Cu concentrations for both locations (c. 68 mg kg-1), which is above background levels and comparable to values shown in road runoff (Table 4). Dissolved Cu

concentrations fluctuate throughout the event with a noticeable peak at site 3 during the onset of the rising limb. At the downstream location the dissolved fraction is consistently above the environmental standard (for good status) of 6 µg L-1.

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Figures 10a (above) and 10b (below): Total Cu (T copper) and dissolved Cu (D copper) for sites 1 (above) and 3 (below). Conductivity, stage and turbidity also shown. Specific pollutants environmental standard for dissolved Cu shown (6 µg L-1)

Figures 11a and 11b show total and dissolved Zn concentrations. Again, concentrations appear to be dominated by the sediment-bound fractions with total concentrations relating closely to turbidity. Dissolved concentrations show little discernable variation across the event and remain below the (pre Biotic Ligand Model) environmental standard of 50 µg L-1. Bioavailability of metals can be determined though application of the Biotic Ligand Model which requires additional data e.g. DOC and carbonate concentrations. Estimated maximum sediment concentrations following the method outlined above show concentrations at both sites are comparable (c. 390 and 330 mg kg-1 for upstream and downstream respectively) and are close to values expected in road runoff (Table 4). A similar trend is shown for Ni in Figures 12a and 12b and here estimated maximum concentrations in suspended material are greater at the upstream location (85 mg kg-1) in comparison to downstream (58 mg kg-1). These values are elevated above expected background concentrations and greater than runoff sediment values reported in the literature. The values are within the ranges reported for road dust (Table 4). It is possible that these metals are derived from road inputs upstream e.g. where the A42 crosses near Packington or from the Measham town road networks. Further monitoring is required to quantify these potential sources of contamination.

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Figure 11a: Total Zn (T Zinc) and dissolved Zn (D Zinc) for site 1. Conductivity, stage and turbidity also shown. Specific pollutants environmental standard for dissolved Zn shown (50 µg L-1)

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Figures 11b: Total Zn (T Zinc) and dissolved Zn (D Zinc) for site 3. Conductivity, stage and turbidity also shown. Specific pollutants environmental standard for dissolved Zn shown (50 µg L-1)

Platinum group elements offer a distinct road-related fingerprint owing to low background levels and the relationship between sediment concentrations and traffic volumes shown in the literature (section 1.1). Concentrations of total and dissolved palladium (Figures 13a and 13b) show contrasting patterns across the sites. Total palladium remained low across the initial stages of flow at site 1 but then displayed rapid increase during the flow peak whilst dissolved concentrations increased during the rising limb. In contrast, total concentrations of palladium at the downstream site remained relatively stable across the flow peak but were elevated in comparison to the early stages at site 1. Dissolved concentrations displayed a rapid increase during the peak flow. Estimated maximum sediment concentrations at both sites are well above background and within the range of road runoff values reported in the literature (c. 0.9 mg kg-1 downstream and 1.6 mg kg-1 upstream). Platinum data suggest that concentrations in the channel are less likely to be related to bulk sediment transfer given the deviation of concentrations from the general trend in turbidity. Upstream values for total and dissolved platinum tend to vary across the flow peak whilst the downstream data show a marked peak in concentrations before the onset of the rising limb, suggesting an early ‘flush’ of material through the system. These more erratic displays in concentration behaviour as shown by Pd and Pt in comparison to other elements are likely to reflect the localised nature of sources and pathways to the channel and supports the notion that these elements are road-derived.

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Figures 12a (above) and 12b (below): Total Ni (T Nickel) and dissolved Ni (D Nickel) for sites 1 and 3. Conductivity, stage and turbidity also shown. Maximum Allowable Concentration EQS shown (20 µg L-1)

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Figures 13a (above) and 13b (below): Total Pd (T palladium) and dissolved Pd (D palladium) for sites 1 and 3. Conductivity, stage and turbidity also shown.

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Figures 14a (above) and 14b (below): Total Pt (T platinum) and dissolved Pd (D platinum) for sites 1 and 3. Conductivity, stage and turbidity also shown.

3.3. Road dust impacts on river sediment quality

Geochemical data for sediment materials associated with the Mease channel at the A42 culvert input are detailed in Table 7. Road dust samples were clearly elevated in indicator heavy metals e.g. Zn, Cu and As. It is also noteworthy that road dusts were elevated in Ca and P which are commonly associated with road salt, and also Cl. The similarity in the road dust materials to the sediment collected from the culvert at

the point of discharge into the Mease demonstrates the connectivity between the road and this outlet. Also of interest, is the distinct geochemistry of the material collected from settling ponds and ditches at AB Produce (Table 7), the local vegetable processing plant, which was hypothesised as potential source of sediment material to the channel. The P loading of this material was below the detection limit of the XRF unit (but could be quantified by more sensitive acid digestion techniques if a PP level in this material is required).

Comparison of the sediment in the Mease channel upstream and downstream of the culvert showed elevated concentrations of metals downstream of the A42 culvert. These can be compared to sediment quality guidelines for the protection of aquatic life (CCME, 2002) which indicates the level at which sediment contamination is likely to have an adverse effect on aquatic life. All metals listed in the CCME guideline exceeded the Probable Effect Level at the time of sampling at the downstream site (Table 6). The high concentration of metals in road dust and the apparent direct link to the Mease channel is a cause for concern. A quantitative analysis of the wider range of metals reported for water samples in above sections can be undertaken on these materials to explore other metals of concern (not detectable by the XRF unit used), if required. This would give information on trace metals which are toxic at lower concentrations e.g. As.

Table 6: Canadian sediment quality guidelines (where PEL = probable effect level)

Freshwater (ISQG) Freshwater (PEL) Mease downstream of A42 Variables (mg kg-1) (mg kg-1) (mg kg-1) Arsenic 5.9 17 27 Cadmium 0.6 3.5 below XRF detection limit Chromium 37.3 90 209 Copper 35.7 197 157 Lead 35 91.3 113 Mercury 0.17 0.486 below XRF detection limit Zinc 123 315 949

Table 7: Major and minor element geochemistry (mg kg-1) of sediment materials associated with the Mease channel at the A42 culvert input

Ba Sb Sn Mo Nb Zr Sr Rb As Pb Zn Cu Road dust (n=8) Mean 701 57 56 16 14 394 126 20 26 167 1145 416 se 67 11 9 2 1 33 22 1 1 13 112 71 Culvert sediment (n=8) Mean 494 29 39 9 12 230 77 38 32 174 1670 530 se 285 17 23 5 7 133 44 22 19 101 964 306 AB Produce solids (n=1) Mean 555 0 0 3 16 468 69 37 0 112 256 38 se 60 0 0 0 1 129 15 1 0 1 58 0 Mease upstream of A42 (n=3) Mean 298 0 20 5 14 239 101 46 < 10 114 340 60 se 41 18 20 1 1 3 2 1 10 4 340 60 Mease downstream of A42 (n=3) Mean 295 0 17 12 14 194 138 49 27 113 949 157 se 43 24 11 1.5 1 3 138 1 9 6 18 12

Fe Mn Cr V Ti Ca K Al P Si Cl S Mg 18209 Road dust (n=8) Mean 61661 1212 319 160 4429 75119 15194 21571 3298 2 455 5113 6002 se 5294 135 33 11 194 14994 680 1126 685 11341 131 306 682 Culvert sediment 15666 (n=8) Mean 58433 1641 229 184 4618 46990 24667 22642 1211 0 392 6430 3575 se 33737 947 132 106 2666 27130 14242 13072 699 90448 226 3712 2064 AB Produce solids 20920 (n=1) Mean 57601 2198 143 132 4944 10592 23729 25613 0 4 50 1523 1870 se 8121 310 26 21 437 6065 1080 3828 0 88 0 109 45 Mease upstream of 22495 A42 (n=3) Mean 50387 897 165 135 5091 18029 29112 35227 1173 5 49 7925 3942 se 272 57 32 44 102 523 415 365 106 870 16 98 403 Mease downstream 20777 of A42 (n=3) Mean 62125 2888 209 180 5934 24196 28753 37443 1364 5 358 6387 4700 se 319 81 38 50 119 633 446 393 106 876 19 91 438

To assess the potential contribution of road derived sediment to the sediment column of the Mease channel a simple sediment fingerprinting approach was applied to the data listed in Table 7 where the upstream sediment, the culvert sediment and the AB produce sediment were treated as potential sources. The geochemical properties that showed significant difference between sources (based on summary statistics) were used as fingerprints (Ba, Mo, Sr, Zn, Cu, Ca, Cr, Si). This semi- quantitative approach was preferred due because of uncertainty introduced by the dynamic nature of road dust properties and the different temporal scales of accumulation of the road and river source end members within the short study period. While the Goodness of Fit statistic was relatively low (ca. 70%) indicating source characterisation was not optimal (to be expected with unquantified variability in road dust properties in the time window available), the results were illustrative of the situation. Upstream sediment was estimated to contribute 70-75% of material on the bed downstream of the A42 culvert i.e. 70-75% of the material was form the catchment with much of the remainder(23-25%) estimated to be derived from the culvert. Just 0.5 to 8% was derived from material similar to that sampled at AB Produce (i.e. soil washing waste), which was shown to be low in the measured potential contaminants derived from roads (noting that only a specific range of elements was analysed for).

4. Conclusions and recommendations

4.1 Key messages

This short-term monitoring programme across a period of wet weather has demonstrated that sediment material and associated contaminants from the A42 road are reaching the Mease main channel via at least one outfall and there are likely to be additional inputs upstream of this targeted study reach. These are impacting on water and sediment quality with implications for aquatic life. Key observations are:

 Specific Electrical Conductance (SEC), which responds to dissolved substances in the water, generally decreases during peak flows as is typical for temperate rivers but there is evidence of increased SEC at the onset of some events which confirms an early flush of dissolved substances. Road runoff is an obvious source of this, especially at the culvert site  Turbidity (i.e. cloudiness of the water) conforms well to the storm flow response with typical patterns for agricultural systems but rapid pulses in turbidity have been detected at the downstream site suggesting that sediment inputs from the culvert site (where fouling affected the record) could be occurring.  The patterns in metal concentration for Cu, Pd and Pt show fluctuating concentrations in total and dissolved concentrations across a storm event, which is indicative of localised, concentrated source inputs. Estimated

maximum concentrations of metals in suspended sediment are elevated above expected background levels and are comparable to road runoff concentrations shown in the literature, which are above guideline values. Both the upstream and downstream locations show elevated levels of metals, which are potential markers for road sources but the downstream signal is augmented by the sampled A42 culvert input.  Metals in road dust exceed sediment quality guidelines (as would be expected given known sources of contaminants on highways) but the metals concentration of sediment that reaches the river via road drainage and the sampled culvert remain high.  While inputs to the river channel appear to be linked to short discrete flushing episodes i.e. the contamination event is of limited duration, these occur on the early part of the rising limb of the main river hydrograph when potential for dilution is low. The close association of contaminants with sediment means that the contaminants will stay in the River Mease for a long time.  Indeed, at the time of sampling, materials delivered to the river via the road drainage culvert formed a measurable proportion (ca 20 %) of total sediment stored on the river bed downstream of the input and the metal concentrations of this material exceeded guideline concentrations for the protection of aquatic life.

4.2 Recommendations

In terms of mitigating the observed road runoff contaminant inputs, it was not clear from site visits what roadside measures, e.g. filter drains on drainage infrastructure linked to the sampled culvert, are currently in place for retaining solids from runoff prior to discharge into the local waterways. The vegetated green space between the culvert outlet and the road (Figure 15) was not accessible at the time of sampling but it was anticipated that this is a zone for highway runoff and sediment detention. Whatever the measures that are in place, the current situation is allowing metal contamination to enter this sensitive river system. Existing measures in place require review to determine whether current infrastructure needs more frequent servicing or modification to improve efficacy.

Figure 15: Relationship of road runoff drainage lines to the sampled culvert and potential runoff detention area (marked in red) (cf Figure 1)

4.3 Limitations and further work

The study was taken across a 3 month period (which included gathering data to inform water sampling protocols) and it is known that contaminant inputs for roads are highly variable in time. That said, each evidence base described in the report indicates a decrease in environmental quality that can be linked to metal inputs via the A42 culvert studied. There was, however, also evidence that heavy metals were entering the main Mease channel from additional upstream sources. These may include additional road inputs, legacy mine waste or background geology influences. The presence of known road runoff indicators suggest roads play an important role but an extended spatial survey of main channel sediment quality, which would be relatively inexpensive, would (i) demonstrate further highway pollution issues but also (ii) contextualise road contaminant inputs against ‘background’ metal pollution form the local geology and historic industrial activity.

The data presented indicate that road runoff is entering the Mease main channel and that this is affecting the environmental quality of the reach sampled. Given these findings, it would be appropriate to next assess the bioavailability of metals both (i) in the water column (using the Biotic Ligand Model which requires more complex sampling and water analysis processes) and (ii) stored in channel sediment (by sequential extraction and Diffusive Gradients in Thin films (DGT) technology). This could be done in collaboration with ecotoxicologists who specialise in the uptake on contaminants by aquatic organisms. It would also be important to assess the spatial

extent (i.e. the footprint) of channel bed contamination downstream of the outfall and other inputs (linked to the above recommendation), against guidelines for sediment quality and effects on aquatic life.

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Appendix: see Excel spreadsheet for full data: Appendix to Report 3_A42.xlsx