Controls on Trace Metals in Urban Stream Sediments : Implications for Pollution Monitoring Using Sediment Chemistry Data

Controls on trace metals in urban stream sediments – implications for pollution monitoring using sediment chemistry data

Paula Kuusisto-Hjort

Department of Geography Faculty of Science University of Helsinki Finland

Academic dissertation

i Supervisors: Professor Matti Tikkanen, Department of Geography, University of Helsinki, Finland

Dr. Juhani Virkanen, Department of Geography, University of Helsinki, Finland

Pre-examiners: Professor Miska Luoto, Department of Geography, University of Oulu, Finland

Dr. Timo Tarvainen, Geological Survey of Finland, Espoo, Finland

Opponent: Dr. Raimo Heikkilä, Finnish Environmental Institute, Joensuu, Finland

Publisher: Department of Geography Faculty of Science PO Box 64, FI-00014 University of Helsinki Finland

ISBN 978-952-10-5833-2 (nid.) ISBN 978-952-10-5834-9 (PDF) ISSN0300-2934 http://ethesis.helsinki.ﬁ

Helsinki 2009 Hansaprint - Hansa Direct 2009

ii Abstract

Contamination of urban streams is a rising topic worldwide, but the assessment and investigation of stormwater induced contamination is limited by the high amount of water quality data needed to obtain reliable results. In this study, stream bed sediments were studied to determine their contamination degree and their applicability in monitoring aquatic metal contamination in urban areas. Th e interpretation of sedimentary metal concentrations is, however, not straightforward, since the concentrations commonly show spatial and temporal variations as a response to natural processes. Th e variations of and controls on metal concentrations were examined at diff erent scales to increase the understanding of the usefulness of sediment metal concentrations in detecting anthropogenic metal contamination patterns. Th e acid extractable concentrations of Zn, Cu, Pb and Cd were determined from the surface sediments and water of small streams in the Helsinki Metropolitan region, southern Finland. Th e data consists of two datasets: sediment samples from 53 sites located in the catchment of the Stream Gräsanoja and sediment and water samples from 67 independent catchments scattered around the metropolitan region. Moreover, the sediment samples were analyzed for their physical and chemical composition (e.g. total organic carbon, clay-%, Al, Li, Fe, Mn) and the speciation of metals (in the dataset of the Stream Gräsanoja). Th e metal concentrations revealed that the stream sediments were moderately contaminated and caused no immediate threat to the biota. However, at some sites the sediments appeared to be polluted with Cu or Zn. Th e metal concentrations increased with increasing intensity of urbanization, but site specifi c factors, such as point sources, were responsible for the occurrence of the highest metal concentrations. Th e sediment analyses revealed, thus a need for more detailed studies on the processes and factors that cause the “hot spot” metal concentrations. Th e sediment composition and metal speciation analyses indicated that organic matter is a very strong indirect control on metal concentrations, and it should be accounted for when studying anthropogenic metal contamination patterns. Th e fi ne-scale spatial and temporal variations of metal concentrations were low enough to allow meaningful interpretation of substantial metal concentration dif- ferences between sites. Furthermore, the metal concentrations in the stream bed sediments were correlated with the urbanization of the catchment better than the total metal concentrations in the water phase. Th ese results suggest that stream sediments show true potential for wider use in detecting the spatial diff erences in metal contamination of urban streams. Consequently, using the sediment approach regional estimates of the stormwater related metal contamination could be obtained fairly cost-eff ectively, and the stability and reliability of results would be higher compared to analyses of single water samples. Nevertheless, water samples are essential in analysing the dissolved concentrations of metals, momentary discharges from point sources in particular.

iii iv Contents

Abstract iii Contents v 1. Introduction 1 1.1. Contamination of urban water bodies 1 1.2. Sediment assessment 3 1.3. Research objectives 5 2. Study area and sampling catchments 7 2.1. Th e Helsinki Metropolitan region 7 2.2 Th e catchment of the Stream Gräsanoja 8 2.3. Catchments of the areal study 12 3. Materials and methods 13 3.1. Sampling and sample pre-treatment 13 3.2. Chemical and physical analyses 14 3.3. Quality control 16 3.4. Derivation of catchment characteristics utilizing GIS analyses 17 3.5. Data analysis 20 3.5.1. General statistical methods 20 3.5.2. Scales of variation 21 3.5.3. Spatial autocorrelation 22 3.5.4. Linear regression models of the metal concentrations 23 3.5.5. Logistic regression models of the very high metal concentrations 24 3.5.6. Signiﬁ cance of multiple statistical tests 25 3.6. Metal pollution assessment 26 3.6.1. Metal enrichment over baseline 26 3.6.2. Evaluation sediment quality with reference to soil screening values and dredged material quality guidelines 27 3.6.3. Hazard assessments 28 4. Sediment composition results 28 4.1. Chemical and physical properties of the sediments 28 4.1.1. Gräsanoja dataset 29 4.1.2. Areal dataset 32 5. Metal concentration results 32 5.1. Metal concentrations 32 5.1.1. Baseline concentrations 32 5.1.2. Metals at the urban sites 33 5.2. Metal enrichment at the urban sites 33 5.4. Comparison to soil screening values and dredged material quality guidelines 35 6. Results of the spatial and temporal variations 36 6.1. Variation across scales 36 6.2. Spatial autocorrelation 37 7. Results of the control of sediment composition on metals 41

v 7.1. Control of sediment composition on acid extractable metals in the Gräsanoja dataset 41 7.1.1. Regional variations 41 7.1.2. Fine-scale spatial and temporal variation 41 7.2. Th e eff ect of sediment composition on trace metals in the areal dataset 42 7.3. Metal partitioning in the Gräsanoja dataset 44 7.3.1. Metal concentrations in diff erent binding phases 44 7.3.2. Control of sediment composition on metals in diff erent phases 45 8. Results of land use impacts on metals 45 8.1. Relationships between land use and sediment composition 45 8.2. Relationships between land use and metal concentrations 46 8.2.1. Acid extractable metals in the Gräsanoja dataset 46 8.2.2. Easily reducible metals in the Gräsanoja dataset 48 8.2.3. Areal dataset 51 8.2.4. Th e relationship of TIA with metals for both datasets 52 9. Results of the modelling of the trace metal concentrations in urban catchments 53 9.1. Linear regression models 53 9.2. Logistic regression models 57 10. Results of the diff erences between mud and sand size fraction 59 11. Water analysis results 61 11.1. Water chemistry and trace metal concentrations 61 11.2. Controls on metal concentrations in the water phase and SPM 63 12. Discussion 66 12.1. Sediment composition 66 12.1.1. Th e infl uence of surfi cial deposits 66 12.1.2. Flocculation and soil aggregates 68 12.1.3. Sediment transport and deposition 68 12.1.4. Land use impacts 69 12.2. Connections between sediment components and metals 70 12.2.1. Relationships in the sediment of the Stream Gräsanoja 70 12.2.2. Statistical relationships in the areal dataset 72 12.2.3. Th e role of TOC in determining the metal concentrations 73 12.2.4. Control of sediment composition on the partitioning of metals 74 12.3. Sediment quality assessment 75 12.3.1. Metal concentrations at the baseline sites 75 12.3.2. Metals at the urban sites 76 12.3.3. Possible bioavailability and mobilization of metals 79 12.3.4. Hazard assessment 80 12.4. Land use impacts 81 12.4.1. Intensity of urbanization 81 12.4.2. Transport areas and rooftops 82 12.4.3. Impacts of diff erent urban and use types 83 12.4.4. Spatial arrangement of urban areas 85

vi 12.4.5. Point sources 86 12.4.6. Easily reducible metal and land use 86 12.4.7. Predicting metal concentrations 87 12.4.8. Th e role of land use 87 12.5. Spatial and temporal variation 89 12.5.1. Analytical and ﬁ ne-scale spatial variation 89 12.5.2. Temporal variation 90 12.5.3. Spatial autocorrelation of metals 91 12.6. Trace metals in the water phase and SPM 92 12.7. Implications for water pollution studies 94 12.7.1. Objectives of sediment assessment 94 12.7.2. Accounting for variations in sediment composition 95 12.7.3 Th e inﬂ uence of metal transport and temporal variations on sediment sampling and interpretation of the results 98 12.7.4. Applicability of sediment for aquatic pollution monitoring 98 13. Conclusions 100 Acknowledgements 103 References 104 Appendix 1 125 Appendix 2 128 Appendix 3 128 Appendix 4 128 Appendix 5 129 Appendix 6 129

vii

1. Introduction 1994; Gregory 2002). Stormwater quality is also degraded showing high concentrations of nutrients, pathogens, organic pollutants 1.1. Contamination of and trace metals (Line et al. 1996; May et al. urban water bodies 1997; Nurmi 1998; Rose et al. 2001). Th e physical and chemical changes of urban run- Following the urban sprawl of the 20th cen- off and stream channels may further impose tury, human actions have altered the form various biological alterations (Wang et al. and function of many water bodies either 2001; Walsh et al. 2005b). directly or indirectly as a result of catch- In urban areas metals originate from dif- ment land use changes (Walsh et al. 2005a; ferent natural and anthropogenic sources, Gurnell et al. 2007). Today one half of the which include wet and dry deposition, traf- world’s population lives in cities and towns, fi c, industrial areas, various chemicals and while the corresponding fi gure for devel- corrosion products of buildings and paving oped countries is 75% (Th e state of the materials (Svensson & Malmqvist 1995; world population report 2008). Th e con- Bergbäck et al. 2001; Ministry of the envi- tinuously growing urban population leads ronment 2005) (Table 1). In addition to ex- to the spreading of urban land use, which haust fumes, the traffi c sources include leak- is bound to aff ect increasingly more streams age of motor oil and corrosion and abrasion and rivers. In addition, the current aims to- of vehicle parts (Brinkmann 1985; Sörme & wards more sustainable cities in many west- Lagerqvist 2002). Th e dramatic increase of ern countries tend to emphasize the need for traffi c has made it a major source of many higher compactness (Hall 2002; Jabareen metals in addition to the roofs and siding 2006; Laine 2008), which may intensify the materials of buildings (Good 1993; Svensson anthropogenic infl uence on existing urban & Malmqvist 1995; Gromaire-Mertz et al. water bodies. 1999; Davis et al. 2001; Sörme & Lagerqvist Th e aquatic eff ects of urbanization are 2002). During dry periods metals from dif- nowadays often driven by the quantity and ferent sources build up on impervious sur- quality of urban stormwater discharges faces that typically cover a high proportion (Walsh et al. 2005a), while other possible of urban areas. When rainfall starts, these anthropogenic impacts of urban areas in- metals are eff ectively washed off the surfaces clude for instance sanitary and combined to the stormwater system and receiving wa- sewer overfl ows, wastewater treatment plant ter bodies. In natural areas a higher propor- effl uents or industrial discharges (Wei & tion of the metals are retained in the soil and Morrison 1993; Bubb & Lester 1994; Paul vegetation following the downward percola- & Meyer 2001; Ramessur & Ramjeawon tion of water and uptake by plants. 2002; Miltner et al. 2004). Channelization Some trace metals like Cu and Zn are often leads to rerouting of urban runoff and essential for the metabolism of aquatic or- increased drainage density. Furthermore, ganisms in small amounts, but in high con- covering of soil by impermeable surfaces centrations they can pose acute or chronic (soil sealing) interferes with the functioning toxic eff ects (Adriano 2001; Londesborough of soil and the natural hydrological cycle by 2003). Indirectly, high contaminant concen- reducing infi ltration and fi ltering of rain- trations can cause changes in community water (European Commission 2002). Th ese structure and biodiversity (Reynolds 1987; changes in the hydrological conditions may Förstner 1989). Th e toxicity of metals de- cause increased fl ooding and morphological pends on their bioavailability rather than modifi cations of stream channels (Hammer their total concentration, i.e. the amount 1972; Neller 1989; Booth 1991; Schueler of metal available for biological action, such

1 Table 1. Major (bold) and minor sources of metals in urbanized areas (Good 1993; Bergbäck et al. 2001; Davis et al. 2001; Sörme & Lagerqvist 2002).

Cu Zn Pb Cd Buildings Copper roofs, Galvanized steel (roofs, Brick walls, old Galvanized gutters, build- gutters), building walls (Pb contain- materials ing walls ing) paint (roofs, gutters), building walls Traffi c Brake wear Tire wear, brake wear, Brake wear, tire Petrol, tire asphalt, motor oil wear, asphalt wear, asphalt Others Atmospheric Atmospheric deposition Atmospheric Atmospheric deposition deposition deposition as uptake by organism. Due to the high risk practices and low-impact design (EPA 2000; they cause to the aquatic environment, Pb, Ahponen 2005; Kloss & Calarusse 2006). Cd, Ni and Hg have been selected as prior- Anthropogenic activity also increases the ity substances by the European Union (EC release of trace metals from natural sources, 2001). Cu, Zn and Cr are additionally re- such as acid sulphate soils, which are com- garded as essential to water protection goals mon in the western and southern coastal in Finland, since they typically occur in rela- regions of Finland (Åström & Björklund tively high concentrations and have adverse 1995; Tarvainen et al. 1997). Ditching and eff ects on surface water ecosystems. How- construction activity related to urbanization ever, these metals were not classifi ed as na- may enhance the oxidation of sulfi de miner- tional priority substances and no quality ob- als and leaching of trace metals (e.g. Co, Ni, jectives were laid down. Instead, they should Zn, Cu and Ti) from acid sulphate soils (see be taken into account while considering the Åström 2001). ecological status of waterbodies (Ministry of Metal concentrations of the water phase the environment 2005). show strong temporal variations which can Stormwater related metal contamination reach several orders of magnitude within of aquatic systems has been recognized as a a short time period (Förstner & Wittman major issue since the 1970’s (Malmqvist & 1981; Dong et al. 1984; Horowitz et al. Svensson 1978; Wilber & Hunter 1977; 1990; Ruth 2004). Consequently, individ- Melanen 1981; Brinkman 1985). In the past ual samples are involved with considerable urban diff use pollution was overshadowed by uncertainty, which may impede the mean- excessive discharges of treated or untreated ingful interpretation of spatial variations or sewage that were primarily responsible for temporal monitoring data (see Wilber & deterioration of urban water bodies (Ruth & Hunter 1977; Bubb & Lester 1994). Due to Tikkanen 2001; Taylor et al. 2008). How- the varying nature of water quality, intensive ever, following the decline in point sources, sampling and high costs are associated with diff use metal sources and stormwater quality water monitoring, and information con- have gained increasing attention worldwide, cerning the metal loads of diff erent urban as well as in Finland (Falk & Stahre 2000; areas is still limited. Th erefore, the extent of Ministry of the environment 2005; Nurmi stormwater induced metal pollution and its et al. 2008; Taylor et al. 2008). Numerous dependence on land use have remained rela- studies have demonstrated high stormwater tively unclear, and further research on the metal concentrations (Melanen 1981; Gro- topic is urgently needed (Williamson 1986; maire-Mertz et al. 1999; Tuccillo 2005), and Goonetilleke et al. 2005; Ministry of the en- it is nowadays widely recommended to de- vironment 2005; Taylor et al. 2008). crease metal loading using best management

2 1.2. Sediment assessment Garnaud et al. 1999; Tuccillo 2005). As a consequence of the tendency to ac- One possibility to study the aquatic metal cumulate in the particulate phase, a major pollution is using sediment chemistry analy- part of the metals carried by streams are sis (e.g. Förstner & Wittman 1981; Heik- eventually deposited in stream beds, fl ood- kilä 1999). Most trace metals tend to be plains or reservoirs as sediments (Tessier & associated with particulate matter and their Cambell 1987). In the active channels fi ne transport and deposition is strongly control- grained sediments of the depositional and led by the behaviour of suspended sediment. near-bank sites best represent the mate- Metals may be associated with solids already rial carried in suspension (Ciszewski 1998; as they enter the stream, or the partitioning Horowitz et al. 1999). Th e most recently of metals between dissolved and particulate deposited fl occulated material often forms phases may be altered following physical or an unconsolidated surface fl oc layer (or sur- chemical processes (Fig. 1). Th us, the dis- face fi ne-grained lamina) characterized by solved metals can be gradually assimilated by a porous structure and high water content the suspended sediments within the stream (Droppo & Amos 2001). Floodplains and system (Droppo et al. 2001). In stormwater reservoirs mostly act as longer-term storages the particulate bound proportion of metals of metals and sediments, while sediments are can be very variable typically exceeding 50% usually stored only temporarily in channel (Williamson 1985; Morrison et al. 1990; bed (Owens et al. 2001; Walling et al. 2003;

Figure 1. Key factors and processes controlling the composition and metal concentrations of stream bed sediments.

3 Taylor et al. 2008). Although biostabiliza- sediments is aff ected by the properties of tion often increases the strength of fl uvial the primary sediment sources as well as the sediments (Droppo et al. 2001), the loose proportion of remobilized sediment (Fig. 1). top layer of the fi ne grained sediments is In urban environments sediment may be de- remobilized during high discharge events. rived from diff erent parts of the catchment, Th e channel bed sediments rarely last longer and from numerous anthropogenic and nat- than a year (Owens et al. 2001; Taylor et al. ural sources. Sediment supply is also regu- 2008). Th us, the fi ne grained stream sedi- lated by human actions such as street cleans- ments off er a media to trace the quality of ing (Charlesworth & Lees 1999a, 1999b). recently deposited suspended sediments. Suspended material is additionally control- Sediments can indicate the spatial dis- led by chemical processes (e.g. dissolution) tribution of contamination and thus help which are often driven by the prevailing pH in identifying the sources and pathways of or redox-conditions (Jenne 1968) (Fig. 1). contaminants (Förstner & Wittman 1981). Complex processes, such as fl occulation, Sediments have been suggested to be more may enhance or impede the deposition of stable media for tracing metal sources com- fi ne grained particles by altering the hydro- pared to water (Förstner & Wittman 1981; dynamic characteristics of suspended sedi- Bubb & Lester 1994; Owens et al. 2001). ment (Nicholas & Walling 1996; Droppo While individual samples of the aqueous et al. 1997). Moreover, variations in the en- phase usually represent metal concentra- ergy of the environment lead to contrasting tions on a time scale from minutes to hours depositional patterns for diff erent sediment (Breault & Granato 2000), sediments may components (Kersten & Smedes 2002). represent time-integrated concentrations. Th e stream velocity and thus, the deposi- Furthermore, concentrations of metals are tional conditions are controlled by various often orders of magnitude higher in sedi- environmental factors, including gradient ments than solution (Luoma 1989; Arakel and morphology of the channel (Knighton 1995). Th us, sediments have also been in- 1980; Th oms 1987; Webster 1995; Ciszews- creasingly employed to assess the contami- ki 1998; Ladd et al. 1998; Rhoads & Cahill nation of fl uvial systems also in urban areas 1999; Church 2002) (Fig. 1). A deeper in- (Wilber & Hunter 1979; Wei & Morrison sight into the relationship of sediment com- 1993; Callender & Rice 2000; Sutherland ponents with trace metals is needed in order 2000; Mellor 2001; De Carlo & Anthony to predict the areas of naturally high metal 2002; Ramessur & Ramjeawon 2002). In accumulation. Th e ability to distinguish the addition to sediments, also biogeochemi- anthropogenic eff ects from natural varia- cal samples, such as plant roots and mosses, tions is a prerequisite for utilization of sedi- have been used in metal contamination as- ment for pollution monitoring and control. sessments (Lax & Selenius 2005). Th e sampling procedure and sampling Th e sediment assessment approach is, site selection further controls the eff ect of however, not without some drawbacks. Th e sediment composition on trace metals. Th e most serious problems concerning the inter- selection of the most appropriate methods pretation of results are created by the spatial is dependent on the purpose of the study. variations of suspended sediment compo- For geochemical mapping, inorganic sedi- nents and their deposition. Contaminant ments are sampled, while organic sediments concentrations in the settled bed sediments are preferred for environmental assessments may be controlled by the sediment composi- (Salminen et al. 1998; Lahermo et al. 1996). tion rather than the location of contaminant In source detection studies samples are often sources (Combest 1991; Bubb & Lester collected from depositional zones where the 1994; Breault & Granato 2000; Walling et sediment is homogeneous and fi ne-grained, al. 2003). Th e composition of suspended while other types of sediments should also be

4 sampled for more comprehensive sediment (Arakel 1995; Birch et al. 2001). quality studies, such as risk assessments (Cis- Th e importance of sediment bound zewski 1998; Ladd et al.1998; Förstner & metals goes beyond the practical aspects Heise 2006). of sediments as archives of past and recent Sediment resuspension or chemical and contamination. Th e sediments are also an biological reactions altering the composition essential part of the aquatic ecosystem, since of bed sediments (diagenesis) may cause they form habitats for a variety of organisms post-depositional changes in metal concen- dwelling on the surface and within the sedi- trations (Breault & Granato 2000; Tao et al. ment (Brils & de Deckere 2003; Stronkhorst 2005). Th is may further increase the discrep- et al. 2004; Allan & Castillo 2007). High ancy with the aquatic contamination loads. metal concentrations of the sediment are As a result of these processes, sediments may particularly dangerous for organisms living also become a secondary source of pollution within the sediment (Förstner & Wittman in the future (Förstner & Heise 2006). Th e 1981), since many of them are exposed to consequent release of contaminated parti- contaminants via ingestion of the sediment cles and associated metals to the water phase (Luoma 1989; Allan & Castillo 2007). may have impacts on the entire downstream However, these invertebrates also serve as water body. food for higher animals. Meaning that the Compared to terrestrial environments, quality of sediment may directly aff ect the identifi cation of metal sources in fl uvial biotic health of the stream. environments is additionally hampered by the transport of sediment and metals (Fig. 1.3. Research objectives 1). Mixing of material of local origin with sediment transported from upstream reaches An assessment of metal contamination (Cu, may disguise the spatial distribution of met- Zn, Pb, Cd) of urban streams in Helsinki al sources. Metropolitan region was conducted using Th e factors and processes described sediment analysis. Th e aim of this study is above act on the sediment at diff erent spa- to determine the key factors controlling the tial scales and therefore lead to diff erent bed sediment metal concentrations, and to spatial patterns of metal concentrations. Th e contribute to the current discussion on the metal concentrations may change gradually role of sediment assessment in pollution in response to regional changes in control- monitoring particularly in urban areas. ling factors, or form local clusters of high or Helsinki is the capital of Finland and is low concentrations (Combest 1991; Ladd et surrounded by the largest continuous urban al. 1998; Mellor & Bevan 1999; Callender area of the cities in Finland (Ristimäki et al. & Rice 2000; Sutherland 2000). On the 2003). Numerous streams in the region are other hand, diff erences in the intensities of primarily located within urban settlement, controlling factors and processes within a and so the impacts of urbanization on metal small area may locally induce highly vari- concentrations in the streams are likely to be able metal concentrations (van der Perk & pronounced. However, the extent of aquatic van Gaans 1997; Rhoads & Cahill 1999). metal contamination has not been com- Th e spatial variations of the controlling fac- prehensively determined in the region. Th e tors and consequently diff erent spatial pat- information available is restricted to a few terns determine how the interpretation and studies on the metal concentrations of urban reliability of the metal concentration results pond sediments (Marttila 2007), stormwa- are aff ected. Th e information of the spatial ter (Nurmi 1998, Karvinen 2009 (unpub- patterns and the dominating scale of varia- lished data)) and stream water (dissolved tions can be utilized to adjust the sampling metals) (Ruth 2004). Th is study also aims scheme to obtain the desired information at increasing the knowledge of the spatial

5 distribution of metal contamination in the on metal concentrations, 4) assess the main region especially with reference to diff erent scales of metal concentration variations and land use patterns. local scale metal patterns and 5) evaluate the So far, studies dealing with the controls, applicability of sediment chemistry analysis particularly of land use, on metal concentra- for spatial contamination monitoring and tions in urban fl uvial sediments have mostly source detection in urbanized areas. been descriptive. Th erefore, knowledge of Th is study reports Cu, Zn, Pb and Cd land use relationships with sedimentary concentrations of depositional bed sedi- metal concentrations is basic. Th is study ments. Th ese metals have previously been aims at utilizing statistical relationships to shown to be good indicators of anthropo- obtain a quantitative assessment of the con- genic contamination, stormwater and traffi c trols of sediment composition and land use sources in particular (Williamson 1986; De on metal concentrations. Carlo & Anthony 2002; Singh et al. 2002; Spatial variations are a phenomenon Bäckström et al. 2004). In addition, Cr and that is intrinsically accepted as interfering Ni concentrations were measured, but the with sediment chemistry analyses, but the concentrations were very low and the ana- magnitude and consequences for sampling lytical method used did not provide reliable and interpretation of results are rarely quan- results. Th e data of the study can be sepa- tifi ed. Spatial patterns are often the focus rated to two sets. Th e fi rst (the Gräsanoja of urban stream sediment studies, but only dataset) contains sediment samples collected the broad-scale patterns are identifi ed and from the Stream Gräsanoja and its tributar- explained (c.f. Mellor & Bevan 1999; Cal- ies. Th e stream drains a catchment that is lender & Rice 2000; Sutherland 2000). Th e dominated by residential land use with small fi ner scale patterns, such as local clustering commercial and industrial districts particu- of high and low values (autocorrelation) are larly in the lower part of the catchment. nearly always neglected in sediment stud- Th e second dataset (areal dataset) includes ies (for exceptions see van der Perk & van sediment samples collected from independ- Gaans 1997; Ladd et al. 1998). Th e infl u- ent headwater streams whose catchments ence of fi ne-scale spatial variations and tem- range from mainly forested to industrial and poral variations on metal concentrations commercial land use. According to Walsh have been studied more frequently even (2000), small streams are particularly valu- in urban environments, but mostly from a able for assessing the impacts of catchment geomorphological viewpoint (e.g. Rhoads urbanization, since they are located close to & Cahill 1999; Birch et al. 2000, 2001). the built-up areas. To be able to distinguish Here, an attempt is made to quantitatively the diff erences in metal concentrations be- evaluate the dominating scales of variation, tween land use types, the streams of the areal and to identify the nature of the local scale dataset were selected so that the catchments spatial autocorrelation structure in stream would represent diff erent land use types. sediments, and consequently to assess the Each catchment should, however, be as ho- implications for contaminant source detec- mogeneous as possible with regard to land tion studies. use. Th e specifi c objectives of this study are to 1) quantify the metal contamination degree of urban stream bed sediments and establish a preliminary estimate of the possible biological eff ects, 2) investigate the controls on sediment composition and determine the infl uence of sediment on metal concentrations, 3) determine the impact of land use

6 2. Study area and sampling tre and only a few suburban areas were locat- catchments ed along the railroads (Schulman 1990). By 1965 urban areas consisting of single-family houses were already common in the region 2.1. Th e Helsinki comprising the urban areas today. Despite a Metropolitan region couple of exceptions, the high density apartment building areas and industrial, commer- Stream bed sediment samples were collected cial and institutional areas were constructed from the Helsinki Metropolitan region on after 1965. Urbanization in the Helsinki the southern coast of Finland (Fig. 2). In ad- Metropolitan region was most intense dur- dition to Helsinki, the metropolitan region ing the 1960’s and 1970’s (Schulman 1990), of Helsinki includes the surrounding munic- but the area continues to face population ipalities of Espoo, Vantaa and Kauniainen. growth and construction of built-up areas At the beginning of 2008 the population of (Halonen et al. 2002). However, despite the the region was 1 007 600 inhabitants (YTV urbanization process, green areas still com- 2009). prise 56% of the Helsinki Metropolitan re- Th e metropolitan region consists of ex- gion (Halonen et al. 2002). In the future, tensive suburban areas surrounding the cen- new urban construction will be located in tral business district of Helsinki. Th e sub- the fringes of the Metropolitan region as urbs extend almost continuously to 15–20 well as within the existing urban structure km distance from the Helsinki city centre, (City of Espoo 1994, 2008; City of Helsinki and the suburban fringes are still expanding 2003; City of Vantaa 2007). outwards (Halonen et al. 2002). Until the Th e climate of the region is boreal with a Second World War, urban settlement was large temperature diﬀ erence between winter rare in the region surrounding the city cen- and summer. Th e mean annual temperature

Figure 2. Location of the sampling sites within the Helsinki Metropolitan region. Th e adminis- trative boundaries represent the situation in 2008.

7 of the normal period 1971–2000 was 5.6 third-order perennial stream with a catch- °C while monthly mean temperatures fl uc- ment area of about 25 km2. In addition to tuated from 15.8 °C in July to -4.9 °C in the 9-km-long main channel, the stream February. Th e area received rainfall on the network consists of two major tributar- average 642 mm/a (Finnish Meteorological ies (Stream Lukupuro and Stream Meritu- Institute 2002). Rain is fairly evenly distrib- ulenoja) and several smaller perennial tribu- uted throughout the year, but highest rain- taries (Fig. 3). Numerous seasonal ditches fall is received in summer and autumn. Av- also bring drainage water to the streams in erage monthly runoff is highest in the spring the high fl ow periods. (in March and April) during the snow melt A majority of the catchment is located period, but high average values are also re- <20 m above the sea level (Fig. 4a). Th e hills corded for late autumn months (October to between the low-lying sediment plains rise December) (Korhonen 2007). Despite the just above 40 m asl. Th e upper slopes of the lower average runoff in summer and winter, hills are mostly rocky or covered by a thin high momentary runoff peaks often occur layer of glacigenic till, while the lower slopes throughout the year as a response to indi- and the valley bottoms are covered by thick vidual storms or snow melt (Ruth 2004). clay and silt deposits reaching depths more than 5 metres (Maaperäkartta 1:10 000 GEO 2.2 Th e catchment of the 10 M Espoo) (Fig. 4c). In the upper reaches Stream Gräsanoja the stream fl ows through a small coarse silt deposit. Peat deposits are located in the up- Th e catchment of the Stream Gräsanoja is per reaches of the catchment, as well as in the located about 15 km west of Helsinki city catchment of the Stream Lukupuro (Map of centre (Fig. 2). Th e Stream Gräsanoja is a Quaternary deposits 1:20 000, sheets 203403

Figure 3. Th e catchment of the Stream Gräsanoja.

8 Figure 4. a) Topography, b) land use and c) surfi cial deposits of the Stream Gräsanoja catchment (Map of Quaternary deposits 1:20 000, sheets 204303 and 203212). and 203212). Th e southern and central parts surfi cial layers (Ojala & Palmu 2007). Th e of the Stream Lukupuro catchment and the bedrock of the catchment is dominated by areas surrounding the main channel close granite, particularly in the southern parts. In to the confl uence of the Stream Lukupuro the northern parts of the catchment gneisses are covered by organic rich gyttja deposits. and amphibolite can also be found (Bedrock In the central parts of the catchment of the map 1:100 000, sheet 2032 Siuntio). Stream Lukupuro sulphide rich sediments, Th e main channel has steeper upper accumulated during the Litorina phase of reaches (average gradient 5.8 m/km) com- the Baltic Sea, have been observed below the pared with a very low-gradient in the lower

9 reaches (average gradient 0.6 m/km) (Fig. simplifi ed bank profi le. 5a). Th e elevation drop in the main channel Most of the catchment is drained by a totals 24 m. In the upper reaches the stream separate sewer system. Th erefore, stormwa- is narrow and consists of alternating riffl es ter is discharged directly and untreated into and pools. At 6.5 km distance the stream the receiving waters, while sewage is chan- channel becomes wide and shallow-watered nelled into the wastewater treatment plant. (Fig. 5b, 6), and the gradient decreases. Th e majority of the built-up areas in the Apart from the upper reaches, the stream Stream Gräsanoja catchment are drained by channel is rather uniform and lacks any underground storm water sewers. Th e den- well-developed geomorphological features. sity of the storm sewer network is highest in Th is is partly due to artifi cial modifi cation of the southern parts of the catchment, where the stream reaches. Particularly in the mid- the tributaries are fed by numerous strorm- dle and lower reaches, the stream consists of water outlets. an artifi cially straightened channel with a Th e catchment of the biggest tributary,

Figure 5. a) Longitudinal profi le of the main stem of the Stream Gräsanoja and b) channel cross-section profi les at sites located in the upper, middle and lower reaches (the locations of the cross-sections are shown in the longitudinal profi le).

10 Figure 6. Th e Stream Gräsanoja a) in the upper reaches, b) in the middle reaches and c) in the lower reaches. Stream Lukupuro, is mostly covered by sity residential areas (5%) and small indus- forested headwater areas and extensive, for- trial, commercial and institutional districts merly cultivated fi elds in the lower parts of (11%) can be found scattered around the the catchment (Fig. 4b). Th e fi elds were still catchment. Th e source of the main channel mostly cultivated at the time of the sam- is located in an industrial area that is mostly pling. Two sampling sites, located in the least dominated by warehouse and industrial ac- disturbed, forested parts of the catchment, tivity. Th e industrial area in the lower parts were considered to represent baseline condi- of the catchment is a combination of com- tions, since their catchments are only weakly mercial activities and light industry. Th e impacted by direct anthropogenic activity. lower reaches of the Stream Merituulenoja is Th e rest of the Stream Gräsanoja catchment surrounded by commercial areas. With the is mostly suburban and the built-up areas exception of fi ve sites in the Stream Luku- comprise 46% of the catchment area. Low puro catchment (sites 49, 50, 54, 55, 56, see density residential areas are the dominant Fig. 4b), all sites were categorized as urban type of urban land use (27%). High den- sites in the statistical analyses.

11 Each of the three highways with traffi c Gräsanoja was brownish and contained high densities of over 30 000 vehicles per day run concentrations of N, Fe and Mn. Th e con- across the study catchment (Fig. 3). Ring centrations of Cu, Z, Pb and Cd were low Road II was opened for traffi c in 2000, while and showed no apparent spatial trends. Th e the other two highways are considerably old- average suitability of the water bodies for er. A municipal solid waste landfi ll, closed in recreation was passable or poor (Jäppinen 1986, is located close to the main channel in 2001, 2005). the upper part of the catchment. Except for occasional overfl ows, the leachate discharges from the landfi ll have been channeled to 2.3. Catchments of the areal study the wastewater treatment plant since 1980 (Jäppinen 2001). A municipal wastewater Sediment samples were additionally collect- pumping station is located adjacent to the ed from 67 small streams located throughout main channel of the Stream Gräsanoja just the Helsinki Metropolitan region (Fig. 1, 7, above the confl uence of the Stream Luku- 8). Th e study sites represent a wide range of puro. diff erent land use types (Appendix 1). Seven Water quality in the Stream Gräsanoja is sites are located in unbuilt catchments that monitored at fi ve sites by Espoo Water twice could be regarded to represent baseline con- a year. In general, the water in the Stream ditions. Th e rest of the catchments were

Figure 7. Examples of the urban streams of the areal dataset. Samplings sites located in a) Varisto, b) Kannelmäki (West), c) Olari, d) Rajakylä and e) Hakuninmaa (for more details of the sites see Appendix 1).

12 Figure 8. Examples of the baseline streams of the areal dataset. Sampling sites located in a) Karhusuo and b) Furubacka. the main channel of the Stream Gräsanoja dominated by residential or industrial land and its tributaries in mid – late September use types. 2003 (Fig. 4b). Th e average distance between Th e bedrock of the Metropolitan area adjacent sites was approximately 200 metres. is mainly composed of granite and gneiss In order to study the temporal variation of (Bedrock map 1:100 000, sheets 2032, 2034 sediment quality, seven sites were re-visited and 2043). Most of the catchments were lo- and re-sampled in late September – mid Oc- cated in the upstream reaches of the streams, tober. Th ese sites were also used for the study and the drainage areas upstream of the of fi ne-scale spatial variation. Th erefore, two sampling locations cover mostly less than 2 fi eld replicate samples were collected at these km2. At the sampling sites the streams are sites during the latter sampling period. Th e small, approximately 0.5–1.5 m wide with spatial replicate samples were collected from water depths up to 0.5 m. No study catch- one depositional zone at the sampling site ments were located in the highly urbanized within a distance of about ten metres. Alto- central areas of the city of Helsinki, where gether 67 samples were collected from the a combined sewer system is in use. Th e in- Stream Gräsanoja and its tributaries. Th e dustrial, commercial, high density residen- summer and autumn were generally fairly tial and modern low density residential areas dry with average monthly rainfall of 48 mm, are drained by underground storm sewers, both during the summer months (June-Au- whereas the oldest low density residential ar- gust) and in the fi rst two autumn months eas are partly drained by open ditches. (September-October) (Finnish Meteorologi- cal Institute 2009). However, a few episodes 3. Materials and methods of heavy rainfall were received during the entire sampling period and consequently between the collection of the two temporal 3.1. Sampling and sample replicates. pre-treatment Th e areal dataset includes 67 sediment and 67 water samples collected from small Th e Gräsanoja dataset comprises bed sedi- streams in the Helsinki metropolitan area ment samples collected at 53 locations along (Fig. 2) in July – August 2004. All sediment

13 sampling was conducted during low-fl ow ants, and represents the fraction of sediment periods. However, a very wet period preced- that is transported in suspension (Salomons ed the sampling session, since the summer & Förstner 1984; Horowitz & Elrick 1987). 2004 was unusually wet with montly rainfall Th is fraction has also been shown to be less of 104, 201 and 78 mm in June, July and prone to small scale spatial variation com- August, respectively (Finnish Meteorological pared to bulk sediment (Birch et al. 2001). Institute 2009). Additional water samples of Th e water samples of the areal dataset the areal dataset were collected during snow- were collected into new or acid washed 1000 melt related high-fl ow periods in March ml polyethylene or polypropylene bottles. – April 2009. Th e samples were transported to the labora- Only depositional zones of the streams, tory in a cooler as quickly as possible after where the sediment is fi ne-grained, were se- the sampling. In the laboratory water sam- lected as sampling sites in order to reduce ples were stored deep-frozen until analysed. the natural variability of the sediments in- Electrical conductivity, pH and oxygen con- duced by granulometric diff erences. Th ree tent of the water were measured in the fi eld to fi ve subsamples collected within a stream during the sampling. distance of a couple of metres were composited at each site. Th e number of subsam- 3.2. Chemical and physical analyses ples and the size of the sampling site were dependent upon the availability of suitable Th e chemical and physical analyses of the fi ne grained sediment at the site. Th e top sediment samples were conducted in the layer of the sediment (approximately 1– 2 Laboratory of physical geography at the cm) was collected using a plastic spoon from University of Helsinki. For the determina- shallow waters near the shores of the stream tion of the acid extractable concentrations of (Shelton and Capel 1994). Th ese localities metals (FeAE, MnAE, Al, Li, CuAE, ZnAE, have been proposed as being most suitable PbAE, CdAE), the fi ne fraction of the sedi- for geochemical sampling and monitoring of ment (<63 µm fraction) was digested with river pollution (Ciszewski 1998; Salminen 14M HNO3 using the method EPA 3051. et al. 1998). Th e dissolution technique is non-selective, First the sediment was sieved through a in that it extracts various labile and relatively 2 mm sieve to remove the coarse plant frag- stable fractions of metals, including metal in ments and inorganic particles. Th en the organic matter and a part of metals incorpo- sediment was wet sieved through a 63-µm rated with amorphous metal hydroxides, bi- nylon mesh using the ambient water. Th e otite and clay minerals (Lahermo et al. 1996; sediment left on the sieve corresponds to the Siiro & Kohonen 2003). Although some of sand fraction (63µm – 2mm) (in the Gräsa- the silicate minerals may also be partially di- noja dataset). Th e sediment that passed the gested (see Hämäläinen et al. 1997), quartz sieve (the mud fraction, <63µm) was then minerals and unweathered feldspars are allowed to settle for a few days at 8oC until mostly unaff ected (Lahermo et al. 1996). the water was clear. Th e water was decanted, Metal speciation studies provide a bet- and most of the sediment was stored frozen. ter insight into the binding behavior and Th e sediment samples (both size fractions) possible mobility and pollution sources of were freeze-dried before any chemical analy- metals (Tack & Verloo 1995; Filgueiras et ses were conducted. For the areal dataset, a al. 2002). Among the available methods to part of the sediment was stored in the re- partition the metals into diff erent chemical frigerator before any grain-size analysis was phases are numerous single and sequential carried out. Th e use of <63µm sediment extraction techniques (Tessier et al. 1979; fraction is recommended, because it usually Chao 1984; Förstner & Kersten 1988; Ure contains the highest concentration of pollut- et al. 1993; Usero et al. 1998). However, the

14 extractants dissolve operational phases that the pseudoresidual metal concentrations, the are determined by the method used, rather metals in the easily reducible, reducible and than well-defi ned chemical forms of metals organically bound phases were subtracted (Tessier et al. 1979). Sequential extraction from the acid extractable metal concentra- techniques, where extractants are applied se- tions. Since the HNO3-extraction does not quentially to the sample with progressively produce “total” metal concentrations, some increasing dissolution strength, are more of the metals bound to the resistant silicates commonly used than single or simultane- are not included in the pseudoresidual metal ous selective extractions (Chao 1984; Hl- concentrations calculated here (CuRes, Zn- avay et al. 2004). However, the effi ciency of Res) (Andersen & Kisser 2004). these techniques suff ers from non-selectivity Flame atomic absorption spectrometry or the dissolution, re-adsorption of met- (FAAS) was used for the determination of als and losses of metals along with sample Fe, FeER, (FeER+FeR), Mn, MnER, Li, Al, washing (Tessier et al. 1979; Rendell et al. ZnAE, CuAE, ZnOrg, CuOrg, ZnR, CuR 1980; Kheboian & Bauer 1987). Although and ZnER (SFS 3044). Graphite furnace the simultaneous extractions are also associ- atomic absorption spectrometry (GFAAS) ated with non-selectivity problems (Chao (SFS 5074) was used for the determination 1984; Kersten & Förstner 1995; Dong et of PbAE and CdAE. ICP-MS was used to al. 2000), they are less time-consuming and analyze all trace metals from the water and involve less sample manipulation compared SPM samples, and for the determination to sequential extraction (Bendell-Young et of CuER, PbER and CdER (ISO 17294- al. 1992). 1; ISO 17294-2). For the water and SPM Th us, the metals bound to diff erent op- samples, the mean of the reagent blanks was erational phases of the sediment were here subtracted from the ICP-MS results. For the determined utilizing a simultaneous selec- bed sediment samples no subtraction was tive extraction procedure (Table 2) as de- performed (the blanks showed very low con- scribed by Bendell-Young et al. (1992): (1) centrations compared to the samples) but easily reducible Mn and Fe (MnER, FeER) the blanks were used to ensure that no con- and associated metals (CuER, ZnER), (2) tamination of the reagents was evident. reducible Fe (FeR) and associated metals Th e total organic carbon (TOC) was (CuR, ZnR), (3) organically bound metals measured by a sulfochromic oxidation (CuOrg, ZnOrg) and (4) pseudoresidual method (ISO 14235). Th e primary grain metals (CuRes, ZnRes). Th e easily reducible size distribution of the mineral compo- Mn and Fe correspond to the Mn-oxides nent of <63µm fraction (clay and silt) and and freshly precipitated, “reactive” amor- 63µm–2mm fraction (sand) was measured phous Fe-oxides, while the reducible Fe by laser diff raction using a Coulter LS200 includes crystalline Fe-oxides (Chao 1984; particle size analyzer, after organic matter Stecko & Bendell-Young 2000). To obtain removal and chemical (sodium pyrophos- Table 2. Simultaneous extraction scheme (Bendell-Young et al. 1992) used for the partitioning of metals in sediments. Extractant Phase extracted 0.1N NH2OH.HCl in 0.01 Easily reducible (MnER, FeER, N HNO3 for 0.5 hrs. CuER, ZnER, PbER, CdER) 0.1N NH2OH.HCl in 25% Easily reducible + reducible (FeER+FeR, HOAc at 95°C for hrs. CuER+CuR, ZnER+ZnR) 1N NH4OH for 1 wk. Organically bound (CuOrg, ZnOrg) 14N HNO3 (EPA 3051) Acid extractable (CuAE, ZnAE, PbAE, CdAE)

15 phate) and ultrasonic dispersion (Agrawal et the particulate metal concentration by the al. 1991). For the Gräsanoja dataset, dried total metal concentration in water. sediment was used, and organic matter was removed using KOH (10%), since the com- 3.3. Quality control monly used H2O2 digestion did not disperse the aggregates of the dried sediment. For the Th e quality control procedure of sediments areal dataset, wet sediment was used for the included analyses of a certifi ed reference particle size analysis (see Förstner 2004), material with a roughly similar composition and organic matter was removed using to the study samples (NIST River sediment H2O2-digestion. Th e percentages of the clay 4350b) and analytical duplicates of samples. (<2µm) and coarse silt (10-63µm) fractions In the HNO3-digestion one reference sam- of the material <63µm were used as grain- ple was added in every second digestion and size parameters in further statistical analyses. two analytical replicates were analyzed for Despite the disperion methods used, slight every sample. In the selective extractions, diff erences in grain-size parameters between analytical duplicates were analyzed for sev- the datasets could be assigned to the diff er- eral samples (altogether 14 duplicates for ences of methods used, since freeze-drying the extraction of the easily reducible fraction can result in particle aggregation (Barbanti and 20 duplicates for the other two extrac- & Bothner 1993). tions). Th e suspended particulate matter con- Th e precision of the replicate analyses was centrations (SPM) in the water samples quantifi ed by calculating the relative stand- were determined by fi ltration of accurately ard deviation (RSD, %). For more than two determined volume of water through 0.4 replicates RSD was obtained by dividing the µm polycarbonate membrane (Whatman standard deviation of the replicates by their 111107 for low-fl ow samples and Whatman average. RSD was expressed as percentages Cyclopore Track Etched Membrane for both by multiplying the obtained value by 100. low-fl ow and high-fl ow samples). Th e trace RSD between duplicate result a and b was metal content of the SPM was determined calculated as (Clesceri et al. 1998): by digestion of the fi lter membranes, using 14 M HNO3 (EPA 3052). Th e digest and fi ltered water samples were then analyzed for trace metals by ICP-MS (ISO 17294-1; ISO Th e median of the relative standard de- 17294-2). viations (RSD) obtained for the duplicates Th e metal concentration of the mate- were <5% for Al, Li, ZnAE, CuAE, CdAE, rial left on the membrane, measured as dry FeAE, MnAE, MnER, ZnER, PbER, weight, was called the SPM metal concen- CdER and CuOrg, <10% for PbAE, FeER, tration. Th e concentration of these metals FeR+ER, ZnR+ER, and <25% for the rest (left on the membrane) in the water sample, of the measured concentrations. Th e RSD measured as mg/l, was called the particulate values of the 14 replicate samples of the metal concentration. It was obtained as the reference material were <5% for Zn, <15% product of the SPM metal concentration for Li, Cd, Pb, Mn, Fe, and <25% for Cu and the suspended sediment concentration. and Al. For the TOC analysis, two analytical Th e metal concentration of the water that replicates for every sample were determined, passed the fi lter membrane was denoted and the relative standard deviations were as the dissolved metal concentration. Th e <4%. sum of the particulate and dissolved metal Th e quality control procedure of the wa- concentrations was called the total metal ter samples collected in 2004 included rep- concentration in water. Th e proportion of licates of ten samples for the determination particulate metals was calculated by dividing of suspended solids and particulate metals,

16 and replicates of 23 samples for the determi- 3.4. Derivation of catchment nation of dissolved metals. For the dissolved characteristics utilizing GIS analyses metals, medians of the RSD were <10% for Cu, Zn and Cd and <25% for Pb. Th e RSD- Th e catchments of the areal dataset and the values of suspended particulate matter and subcatchments of each sampling site in the metal concentrations were very high (medi- Gräsanoja dataset were delineated in Map- ans for particulate metals 33-48% and for Info 8.5. Th e delineation was based on the SPM 100%). Th e SPM concentration of the contour lines of the 1:20 000 topographical fi rst replicate was consistently higher than maps (Basic map 1:20 000, sheets 203211, that of the second replicate. Accordingly, 203212, 203403, 203406, 203409, 204110, the particulate metal concentrations were 204301, 204304, 204307) and maps pre- consistently lower for the fi rst replicate. Th e senting the location of storm sewers in the high variation between the replicates was in- municipalities of the study area. Th e catch- terpreted as resulting from the higher con- ments were assigned a number correspond- tent of metal poor coarse particles in the fi rst ing to the number of the sampling site. replicate. Th e water samples were very di- Th e catchment characteristics were in- lute, and the occurrence of individual coarse vestigated utilizing two diff erent scales: the particles with high settling velocities did ap- entire catchment scale and the partial catch- parently cause substantial variation between ment scale. In previous studies a number replicates. Th us, in order to maximize the of diff erent relatively simple methods, such comparability of the results between sam- as diff erent buff ers and circles, have been pling sites, only the results of the fi rst repli- presented to defi ne a partial catchment (Al- cates were used for further analyses. lan et al. 1997; Wang et al. 2001; Walton For the water samples collected in 2009, et al. 2007). However, these methods lack replicates were analysed for 5–10 samples. hydrological foundation and therefore, a Th e median RSD values between the repli- region (immediate upstream catchment) cates were <5% for particulate and dissolved representing the part of the catchment that Cu and Zn, <10% for particulate Pb and Cd is hydraulically close to the sampling site and dissolved Cd and <15% for SPM and was delineated (Fig. 9). Th e region was de- dissolved Pb. In addition, certifi ed refer- fi ned as areas in the contributing catchment ence material (HR-1, NMRI Canada) was that are located up to 50 metres from the also fi ltered (fi ve replicates) and analyzed stream channel up to 150 metres distance for SPM and particulate metals. More than upstream from the sampling site. Th e im- 90% of the mass of the reference material mediate upstream catchment also included was retained in the fi lter membrane, and in areas surrounding (50 m buff er) stormwa- total 66–95% of the reference material was ter pipes which discharge to the stream (up recovered (dissolved and particulate matter to 150 metres upstream from the sampling together). Th e average RSD-value of the site). Catchment characteristics of the entire SPM concentration of the reference materi- upstream catchment are indicated by c (for als was 14%. Th e trace metal concentrations example the proportion of institutional land of the particulate reference material (retained in the entire upstream catchment=INST_c, in the fi lter) were on the average 120-126% see Table 3 for the abbreviations of land use of the certifi ed values for HNO3-digestion classes). Characteristics of the immediate (EPA 3051), and RSD between the repli- upstream catchment are indicated by i (e.g. cates was less than 5%. the proportion of institutional land in the immediate upstream catchment=INST_i). For the Gräsanoja dataset, the local riparian buff er zone was delineated as the 20 metre

17 wide (in each side of the stream) area sur- of the total catchment area were assessed rounding the stream up to 50 metres up- for the catchments of the areal dataset. Th e stream from the sampling site. Quaternary sediment (vector format) data was obtained from the Geological Survey of Finland (Map of Quaternary deposits 1: 20 000, sheets 203211, 203212, 203403, 203406, 203409, 204110, 204301, 204304, 204307). Quaternary sediment data was missing from some of the densely built areas, where the data was supplemented by fi eld inspections and utilising information obtained from the maps of Quaternary deposits produced by the municipalities of Es- poo and Helsinki (Maaperäkartta 1:10 000 GEO 10 M Espoo ; Geotekninen kartta 1:10 000 GEO 10 M Helsinki). Th e Quaternary deposit data was transferred to raster data (20 m grid) using ESRI ArcMap 9.3, and Figure 9. Determination of the immediate the areas of the diff erent sediment types in upstream catchment boundaries. each catchment and subcatchment (including immediate upstream catchments) were Topography has an infl uence on sedi- calculated using the Tabulate area command ment transport and deposition processes, of ArcGIS Spatial analyst. Percentages were and thus on the sediment composition. In obtained by dividing the areas by the total order to assess the control of topography area of the catchment. on sediment composition, topographical Land use data (Table 3) was obtained indices (mean altitude and mean slope an- by digitizing the regions of seven diff erent gle) were calculated from a digital elevation land use types using MapInfo 8.5 software. model (DEM). Th e raster DEM with 20 m Identifi cation of diff erent land use regions grid size was calculated by linear interpola- was conducted based on the information tion from digital contour lines, provided by provided by topographical maps (Basic map the National Land Survey of Finland, Uusi- 1:20 000, sheets 203211, 203212, 203403, maa district survey offi ce (011/UUMA/02), 203406, 203409, 204110, 204301, 204304, using Arc/Info’s TOPOGRID command. 204307) and database of buildings pro- A slope map (20 m grid) of the study area vided by the National Land Survey of Fin- (the Metropolitan region of Helsinki) was land, Uusimaa district survey offi ce (011/ produced from the DEM by using ArcGIS UUMA/02). Internet sites (Vantaan kartta- Spatial Analyst toolbar. Mean of the altitude ja paikkatietopalvelu SpatialWeb 5 2008, and mean slope angle in degrees for each Igglo/karttahaku 2008) were also utilized catchment or subcatchment was calculated to identify the land use types of the catch- using the Zonal statistics as table command ments. In addition to the buildings, the land of ArcGIS Spatial analyst. use regions included the surrounding yards, Th e composition of sediments is a func- parking areas and local and collector streets. tion of the characteristics of various sedi- Individual buildings with surroundings ment sources in the catchment. Th e erosion were, in most cases, not digitized as land use of surface soils in the catchment is an im- regions, but the land use regions contained portant source of both mineral and organic several buildings. Finally, the land use re- sediments. Th erefore, the proportions of gions were transformed to raster data (20 m diff erent Quaternary sediment types out grid) in ESRI ArcMap 9.3, and the areas of

18 Table 3. Variables of land use (%) and their description. Abbreviation Land use type Description LDR Low density Single family houses, row houses and semi-detached houses residential HDR High density Apartment buildings residential INST Institutional Institutional buildings, such as churches, schools, ad- ministrative buildings or offi ce buildings COMM Commercial Stores and shopping centres, commercial warehouse & traffi c buildings and commercial sport centres, highways, major roads and collector streets within forested areas IND Industrial Industrial buildings and surrounding streets, parking lots and warehouse areas. UNB Unbuilt Forests, mires, cultivated fi elds, meadows, parks, football fi elds the land use regions within each catchment percentage of impervious area (TIA) was were calculated using the Tabulate area com- obtained as the sum of TIA_roof and TIA_ mand of ArcGIS Spatial analyst. Proportions trans. Th e calculation of impervious surface (%) of the land use types were obtained by metrics was conducted using MapInfo 8.5 dividing the areas by the total areas of the software. Th e proportion of eff ective imper- catchments. vious areas (EIA, those directly connected to For further analysis, several land use watersheds) was estimated using the method types were combined to form more gen- proposed by Sutherland (1995), where the eral land use metrics. Urban land use EIA is a function of TIA and the intensity of (URBAN) includes all built-up areas stormwater drainage of the catchment (see (LDR+HDR+INST+COMM+IND), and Table 4): dense urban land use (DENSE) refers to the sum of HDR, INST, COMM and IND. A land use classifi cation was created for Th e TIA of the riparian zone in the the study catchments of the areal dataset. Gräsanoja dataset was calculated by digitiz- Firstly, in order to decrease the number of ing the impervious surfaces utilizing the are- land use classes, land use types HDR, INST al photographs (photo taken 2007) obtained and COMM were combined to form a new from the internet site for municipality of Es- land use class: high density residential, com- poo (Th e City of Espoo Map Service 2008). mercial & institutional. Th en the sites were Traffi c density (TRAFFIC) was calculat- grouped into four land use classes according ed for each study catchment and subcatch- to the dominating land use type of the entire ment. First, a traffi c volume estimate was upstream catchment. assigned for each street and highway of the Th e percentage of impervious surfaces street database (vector data) of the National associated with rooftops (TIA_roof) was Land Survey of Finland (Uuma/109/03). obtained as the total area of buildings inside Measured traffi c volume values were ob- the catchment using the buildings database tained for highways, main streets and collec- (National Land Survey of Finland, Uusimaa tor streets (Ajoneuvoliikenne Espoossa 2002, district survey offi ce 011/UUMA/02). Th e 2003, 2007; Autoliikenne Vantaalla 2006; impervious surfaces associated with trans- Liikennemäärät Helsingin pääkatuverkossa port areas (TIA_trans) were digitized uti- 2007). Only some traffi c volume measure- lizing a Spot satellite image (pixel size 2.5 ments were available for local streets. Th e metres) produced by Metria in 2002. Total average traffi c volumes of local streets were

19 Table 4. Catchment properties and corresponding coeﬃ cients used in calculating EIA (Suther- land 1995). Catchment type Description ab Average catchment Mainly curbs and gutters, roofs of single family hous- 0.1 1.5 es not connected to storm sewer or street curb Highly connected As average basin, but residential rooftops most- 0.4 1.2 catchment ly connected to streets or storm sewers. Totally connected All impervious surfaces are directly con- 11 catchment nected to storm sewer system Somewhat discon- At least 50% of urban areas are not storm sew- 0.04 1.7 nected catchment ered but are sewered by grassy swalles or roadside ditches. Rooftops not connected. Extremely discon- Only a small percentage of the urban area is storm sewered. 0.01 2.0 nected basin

650 and 210 vehicles per day in Helsinki percentages of land use and surfi cial sedi- and Espoo respectively (Ajoneuvoliikenne ment types, and proportions of metals in Espoossa 2007; Hellman 2008). Th e traf- diff erent binding phases (y’ = arcsin(sqrt(y))) fi c volumes of local streets were estimated (see Legendre & Legendre 1998). Th e sta- to be 500 vehicles per day for commercial, tistical analyses were conducted using SPSS industrial, institutional and high density 12.0.1 for Windows. Some of the dissolved residential (apartment buildings) areas, 300 metal concentrations and metals of the sus- for medium-density residential (row houses pended particles were below the detection and semi-detached houses) areas and 100 limit of the analysis, or above the calibration for low density residential areas (single-fam- range. If the percentage of censored data ily houses). Th e average daily vehicle kilo- was relatively small (<12%), these missing metres of each street were calculated as the values were removed from correlation analy- product of the traffi c volume and the length ses and box-plot representations. Before this of the street. Traffi c density was obtained by operation the data was analysed carefully to dividing the average daily vehicle kilometres make sure that the missing values would not of travel by square kilometre of land area for strongly impact the results. If the percentage each catchment. of censored data was high (>25%), no correlation analyses were performed, because the 3.5. Data analysis results would involve too much uncertainty due to missing observations from the low and high ends of the data range. 3.5.1. General statistical methods Principal component analysis (PCA) was applied in order to summarize the data Th e statistical methods used in this study and relationships between diff erent variables were based on covariances or linear correla- (Table 5). Th e principal components were tions (Legendre & Legendre 1998). Th ere- extracted from the correlation matrixes, and fore some of the variables were transformed the components with eigenvalues larger than in order to achieve normal distributions and one were interpreted (Legendre & Legendre linear relationships between variables. Trace 1998). Because the initial principal com- metal concentrations (in both datasets), ponents were relatively easy to interpret, Clay-%, Fe and Mn (in the areal dataset), the component solutions were not rotated. traffi c density were log10-transformed. Th e Correlation coeffi cients were further used to arcsine transformation was applied for the describe the relationship between diff erent

20 variables. Th e relationships of metals with able exceeded the signifi cance level of 0.05. sediment components at the fi ne spatial and For dummy variables, the selection between temporal scales were calculated as partial two models (one includes the dummy vari- correlations (Legendre & Legendre 1998). able/variables and the other does not) was Th e dominating impact of concentration conducted based on the F-test of nested diff erences between sites was removed by models (p<0.05). controlling for the site variables (seven dummy variables indicating the site where the 3.5.2. Scales of variation concentration has been measured). Linear regression models were built us- Th e overall variability describes the total ing forward selection. Variables were entered variation in the data without any reference to the model one by one according to which to the spatial arrangement of the values, but variable produces the largest increase incoef- the total variation can be disintegrated into fi cient of determination (R2). Variables were diff erent spatial or temporal scales. In this added to the model as long as the new vari- study, overall variability is expressed using Table 5. Th e data and data analysis methods utilized to fulfi l the main objectives of the study.

Objective Data Data analysis 1 - Metal contamina- Both datasets: metal concentra- Enrichment factors, comparison tion degree of urban tion (Cu, Zn, Pb, Cd) (acid to dredged material quality guide- stream bed sediments. extractable and easily reducible). lines and soil screening values. - A preliminary estimate of the possible biological eﬀ ects.

2 -Controls on sedi- -Both datasets: particle size meas- - Both datasets: PCA ment composition. ures and chemical composition of - Areal dataset: correlations -Th e infl uence of sediments, acid extractable metals. of metals with surfi cial sedi- sediment on metal -Gräsanoja dataset: spe- ments and topography. concentrations. ciation of metals. - Relationships between sediment -Areal dataset: percentages of components and metals: PCA, correla- surfi cial sediment types and tions, partial correlations. Comparison topographical indexes. of the results with the speciation data.

3Th e impact of land Both datasets: Land use metrics of Relationships between metals and use on metals. the entire and immediate upstream land use metrics: correlations, linear catchments, acid extractable metals regression, logistic regression. Gräsanoja dataset: easily reducible metals. 4 Assess the main scales Gräsanoja dataset: acid extract- Gräsanoja dataset: the magnitude of of metal concentration able metal concentrations at all metal variations at diﬀ erent scales. variations and local sites, including the spatial and Spatial autocorrelation of metals. scale metal patterns. temporal replicates at seven sites.

5 Applicability of Both datasets: data of Both datasets: results from sediment chemistry objectives 1–4. objectives 1–4 . analysis for spatial con- Areal dataset: metal concentra- Areal dataset: comparison of tamination monitoring tions in water and SPM. metal concentrations in water and source detection Gräsanoja dataset: easily reduc- and SPM with sedimentary met- in urbanized areas. ible and acid extractable metals and land use metrics. als of the mud fraction and Gräsanoja dataset: Comparison of the acid extractable metals of the various sediment analysis methods sand fraction at 20 sites. with regard to ﬁ ne scale variation of metals and correlations with land use and sediment composition.

21 the coeffi cient of variation, or relative stand- calculated as the value of Moran’s I (Upton ard deviation. Here, the overall variability & Fingleton 1985): was expressed at diff erent scales: 1) the total variation in the Gräsanoja dataset (n=53), 2) the fi ne-scale spatial variation between two fi eld replicate samples at seven sites, and 3) the temporal variation between two where n is the number of samples, yi and temporal replicates at the seven sites. Th e yj are the concentrations of a metal at sites total variation was calculated as the distri- i and j, ȳ is the average of y and Wij is the bution of RSD-values calculated between measure of spatial proximity of sites i and j. two randomly selected sites within the ur- When water fl ows from site i to site j, and ban sites, since the aim was to compare the they are located within the distance of lag d, fi ne-scale and temporal variations with the Wij=1, and otherwise Wij=0. regional variations in the urban reaches. Th e fi ne-scale variation corresponds to variations within a site with spatial extent of about 5– 10 m, and the temporal variation describes Moran’s I varies between -1 (maximum the variation at a certain site between two negative autocorrelation) and +1 (maximum sampling campaigns separated by about a positive autocorrelation). Th e mean and month. variance of the sampling distribution of I are obtained from: 3.5.3. Spatial autocorrelation

Th e spatial patterns of metal concentrations were further studied using the concept of spatial autocorrelation. It is a measure of the similarity of a variable between closely located sites (Goodchild 1986). Spatial autocorrelation has been commonly assessed for terrestrial environments, but rarely in aquatic systems, including fl uvial environments (Peterson et al. 2006). In stream networks one of the diffi culties involved in assessing the magnitude of spatial autocorrelation is the determination of distances between sites. Symmetric straight-line distances are not applicable to systems, where connections between sites are strongly determined by a di- Th e signifi cance of the I values can be ob- rectional process, such as stream fl ow (Ganio tained by comparing the probabilities of nor- et al. 2005; Peterson et al. 2006; Ver Hoef mal distribution with z (z=(I-E(I))/√var(I)). et al. 2006). Th us, unidirectional hydrologi- Th e spatial autocorrelation was calculated cal distances between sites were measured for the raw data of trace metals and the here, i.e. distance from upstream site to standardized metal concentrations. downstream site along the stream channel For the residuals of a linear model, the network (for discussion on distances along calculation of Moran’s I is slightly diff erent. networks see Peterson et al. 2006; Ver Hoef Th e residuals (ê) of a model y=bx+e (E(e)=0) et al. 2006). are given by ê=y-ŷ, where ŷ is the estimated Here, spatial autocorrelation between value of y. In the case of residuals, the spa- sites separated by a certain distance (d) was tial autocorrelation is calculated for êi values

22 that are subject to k linear constraints (k re- in order to predict the pure impact of land gression coeffi cients in the model) instead of use on metal concentrations. Th e set of land n independent observed values (yi-ȳ). Th us, use variables used for modelling included the Moran’s I for residuals (Ik) is defi ned by the traffi c density and percentages of impervious areas and diff erent land use types (low density residential, high density residential, institutional, commercial and traffi c, and where W is a matrix containing the distances industrial) in the entire upstream catchment between sites (0/1). Th e mean and variance and in the immediate upstream catchment. of the residuals are calculated by: Th e rest of the land use metrics were deriva- tives of the above mentioned variables or their constituents, and were omitted from the analysis due to problems of multicol- linearity. In the fi rst stage, the complete dataset was used to construct the model, and the fi t- where ted model is called the fi nal model. Th e studentized residuals of the fi nal models were examined in order to reveal heteroscedas- Th e magnitude of spatial autocorrela- ticity, unlinear behaviour or the existence tion was measured for acid extractable metal of outlier observations. Knowing that the concentrations and standardized metal con- studentized residuals follow tn-k-2 distribu- centrations, and residuals obtained from tion, the outlier observations were identifi ed a regression model that related metal con- using the confi dence level of 0.05 (Weis- centrations to sediment composition. Th us, berg 1985). To evaluate the performance these residuals represent the variation of of the model for the calibration data, three trace metals that is left when the infl uence fi t statistics were used: coeffi cient of deter- of sediment composition is removed. mination (R2), Spearman’s rank correlation coeffi cient (rs) between the observed and 3.5.4. Linear regression models predicted values, and the average absolute of the metal concentrations bias (AB) (Guisan & Zimmerman 2000; Kozak & Kozak 2003). AB was obtained by Th e relationships between metal concentrations and diff erent environmental factors were studied using several explanatory methods. Further, it is of intrest to determine how well the metal concentrations could be Where yi is the observed value and ŷi is the predicted using the information provided by estimated value of the response variable at sediment composition and land use metrics. site i. Th erefore, linear regression models were Since the predicted errors of the fi nal created. Firstly, the metal concentrations model were not independent of the data used were modelled using sediment composition to construct the model, the predictive accu- and land use metrics as explanatory variables. racy of metal concentration models were as- Th us, in addition to the land use data, the sessed using tenfold cross-validation (Kozak area specifi c impact of sediment composi- & Kozak 2003). Although cross-validation tion was accounted for. Secondly, metal con- involves some weaknesses, including loss of centrations standardized for the contents of information and stability (Harrell 2001; Ko- clay and organic matter (see chapter 3.6.1.) zak & Kozak 2003), it provides a way to de- were modelled as a function of land use data termine the predictive ability of the models

23 and variability associated with model build- observed values. Th e back-transformation of ing (variable selection and estimation) (Har- the predicted value ŷi produced the median rell 2001). Because the dataset of this study of the predictive distribution of metal con- was fairly small, cross-validation was more centrations (Weisberg 1985). suitable for model evaluation compared to data splitting (Harrell 2001). However, 3.5.5. Logistic regression models of similarly to data splitting techniques, cross- the very high metal concentrations validation does not validate the model of the complete dataset but models developed on Th e occurrences of very high metal concen- subsets of the data (Harrell 2001). trations, i.e. those that exceed the pollution Here the complete dataset was divided level (for Cu and Zn) or the intervention level into ten subsets at random. Th e first nine (for Pb and Cd) of dredged material (Table subsets were used to construct a calibration 7) were modelled by logistic regression. Th e model. Th e calibration stage included the se- metal concentrations were transformed to lection of variables and estimation of model binary (0/1) variables (called here CuHigh, parameters. Th en, the calibration model was ZnHigh, PbHigh and CdHigh) according used to predict the response for the remain- to the observed concentration in reference ing 1/10th of the samples, and the accuracy to the sediment quality guidelines. Th us, of the prediction was assessed by compari- the new variables obtained value 1 (present son to the observed values. Th e linear model sites, presence of high concentration) if the (calibration model) created using the con- standardized metal concentration at the site struction set is was above the sediment quality guideline in question, and 0 (absent site, absence of high concentration) if the concentrations was be- Th e predicted value of an observation i for a low the guideline. given set of values of the predictor variables In a logistic regression model the re- (xi) in the estimation set is sponse variables are the logarithms of odds. is the estimate of β obtained from the fi t- Th e odds express the likelihood of an occur- ted model of the construction set (Weisberg rence (or presence) (pi) relative to the like- 1985). Prediction bounds for an individual lihood of a non-occurrence. Th e odds are response in the estimation set are obtained linearly dependent on the exposure to a set by of risk factors (the explanatory variables). A linear combination of the explanatory variables is also called the linear predictor (LP) Where tα/2 is the appropriate point based on (Backhaus et al. 2000; Dobson 2002). Th e Tn-k-1 distribution (Milton & Arnold 1990). logistic model is of form: Since was close to zero in this study, the prediction bounds were calculated as S was estimated as the square 2 root of the residual mean square σ of the Where xi is a vector containing the values of calibration model (see Weisberg 1985). Th e the explanatory variables at site i and β is the performance of the set of calibration models parameter vector. Th e parameters are esti- was assessed using three prediction statistics, mated using maximum likelihood. In order comparable to those for the fi nal model: R2, to obtain the probabilities of occurrence, the rs and average absolute bias (AB). Finally, model may be transformed to form: the predicted values and prediction bounds (log-transformed) were transformed back to metal concentrations in order to graphically present the agreement of the prediction with Th e logarithmic regression produces, thus,

24 probabilities pi of occurrence (presence) that the value. AUC, on the other hand, provides range between 0 and 1 (Dobson 2002). A a threshold independent measure of the dis- classifi cation of sites to those with predicted crimination ability. A ROC plot presents the occurrence (presence) and predicted non-oc- sensitivity values against the corresponding currence (absence) is obtained by comparing value of false positive fraction (1-specifi city) the probabilities of occurrence to a threshold for all threshold values (Pearce & Ferrier value. 2000). AUC is the proportion of the area of Th e explanatory variables were selected the entire graph that lies beneath this ROC using backward elimination with a criterion curve (Swets 1988). If the value is 0.5, the of 0.05 for variable inclusion. Th e model predicted probabilities of presence do not thus created was called the fi nal model. Sim- diff er between the actual present and absent ilarly to the validation of the linear regres- sites, and the model has no discrimination sion models, the predictive accuracy of the ability (Fielding & Bell 1997). AUC values logistic regression models was assessed using 0.5-0.7 indicate poor discrimination capac- tenfold cross-validation. ity, values 0.7-0.9 indicate discrimination Th e fi t statistics (fi nal model) and predic- ability that is useful for some purposes, and tion statistics (cross-validation) for the mod- values greater than 0.9 indicate very good els included the area under the ROC curve performance (Swets 1988). statistic (AUC), Cohen’s Kappa, sensitivity, specifi city, positive prediction power and 3.5.6. Signifi cance of overall accuracy. All statistics, except AUC, multiple statistical tests were based on the predicted presences and absences. Th e cutoff threshold used in order Th e signifi cance of a single statistical test was to obtain presence or absence predictions assessed using the p-value which describes based on the probabilities of presence was the probability that the null hypothesis will calculated as the prevalence (proportion of be rejected when the null hypothesis is actu- present sites) across the entire data (Liu et al. ally true (Type I error). Alpha (α) is a thresh- 2005). Cohen’s Kappa coeffi cient measures old probability which defi nes the maximum the correct classifi cation rate after the prob- p-value considered to indicate a signifi cant ability of chance has been removed (Cohen result. When several tests are performed, 1960). Th us, kappa measures weather or not each test has a change to yield a signifi cant the model predicts better than chance. Based result, and the change of obtaining at least on the Kappa coeffi cient, the discrimination one signifi cant result increases with the nu ability of the model can be described as poor mber of tests performed (even when H0 is (κ<0.4), good (0.4<κ<0.75) or excellent true for all of the tests). Traditionally, the (κ>0.75) (Landis & Koch 1977). change of declaring tests falsely signifi cant is Sensitivity measures the proportion of controlled using the Bonferroni correction correctly predicted sites among the present meaning that, the α-thresholds of single (observed) sites. Specifi city describes the tests are adjusted so that the probability of proportion of correctly predicted sites obtaining at least one signifi cant result is α among the absent sites. Positive predictive (the familywise error rate or the overall sig- power is the probability that a site is actu- nifi cance level) (Verhoeven et al. 2005). Th is ally a present site if the model classifi es the method does not, however, take into account site as present. Accuracy is the proportion of the number of tests declared signifi cant, or all correctly predicted sites (Fielding & Bell the lack of independence between the tests 1997; Pearce & Ferrier 2000). (Moran 2003; García 2004). As a result, it All the above mentioned fi t and predic- is unlikely to obtain signifi cant results when tion statistics use only one cutoff threshold a large number of tests are performed and value, and are dependent on the choice of the probability of Type II errors (accepting

25 H0 when it is actually false) is high (Cabin include 1) average shale content, 2) average & Mitchell 2000; Moran 2003; Nakagawa crustal abundance, 3) preindustrial lake, ma- 2004; García 2004, Verhoeven et al. 2005). rine and river sediments or 4) recent sedi- Th us, the errors in multiple testing (in ments of fairly natural and unpolluted areas correlation tables, regression analyses and (Förstner & Müller 1981; van der Weijden pairwise comparisons of means) were con- 2002). Th e use of average shale content is trolled here utilizing the false discovery problematic due to varying local geology and rate (FDR), introduced by Benjamini and thus high spatial diﬀ erences in the natural Hochberg (1995). It controls the fraction metal concentrations (Salminen & Tarvain- of false rejections among the signiﬁ cant hy- en 1997; Matschullat et al. 2000; van der potheses, instead of protecting against any Weijden 2002). Preindustrial lake or marine false rejections, and provides a compromise sediments may be used to obtain the geo- between Type I and Type II errors (García chemical background concentration which 2004; Verhoeven et al. 2005). Th e follow- represents the conditions prior to wide- ing procedure controls FDR at a level less spread industrial revolution. On the other than or equal to α (Benjamini & Hochber hand, recent sediments even on remote sites

1995; Benjamini & Yekutieli 2001): Let p(1) are impacted by regional atmospheric pollu-

≤ p(2) ≤ . . . ≤ p(m) be the observed p-values in tion. Th erefore, the concentrations of recent ascending order, and denote by H(i) the null unpolluted sediments can not be expected hypothesis corresponding to p(i). Let k be the to refl ect the geochemical background, but largest i for which rather the baseline concentration (see defi ni- p(i) ≤ i/m*α; (16) tion by Rice 1999) that refers to the concen- then reject all H(i) i=1, 2, . . . , k. Th is pro- tration that has not been aff ected by direct cedure controls FDR in the case where all anthropogenic inputs. tests are independent or positively correlated In this study, recent stream sediments (Benjamini & Hochber 1995; Benjamini & of relatively unpolluted areas, where direct Yekutieli 2001, Verhoeven et al. 2005).Th e contamination from anthropogenic activ- FDR controlled statistical signifi cance (table- ity is assumed to be weak, were selected to wise control) of the tests presented here were estimate the baseline concentration of the indicated as follows: *** (FDR<0.001), ** area. While assessing the anthropogenic ef- (0.001≤FDR<0.01), * (0.01≤FDR<0.05). fect of urban land use, it is more meaningful to compare the concentrations of the 3.6. Metal pollution assessment urban sites to these baseline concentrations than to the geochemical background. Th us, the baseline concentrations were calculated 3.6.1. Metal enrichment over baseline as the average metal concentrations of the two baseline sites in the Stream Gräsanoja Th e severity of contamination related to catchment (in the forested headwaters of the urban land use was assessed by comparing Stream Lukupuro) and seven baseline sam- the metal concentrations in sediments to pling sites of the areal dataset (the locations the natural level of these metals. One of the of the baseline sites are presented in Fig. 13). most critical issues in contamination assess- Th e catchments were dominated by forests ment is how the natural concentrations of or old fi elds which were not cultivated at the metals are estimated (Förstner & Müller time of the sampling. It was estimated, that 1981; Prohić & Juračić 1989; Covelli & these sites did not receive substantial inputs Fontolan 1997; Rubio et al. 2000; Apitz & of metals directly from anthropogenic sourc- Power 2002). Natural background concen- es. For the sediment contamination assess- trations of metals in fl uvial sediments can ment using easily reducible metal concentra- be estimated using several methods. Th ese tions in the Stream Gräsanoja, the baseline

26 concentrations were calculated as the average organic matter, 25% clay) (Ministry of the metal concentrations of two baseline sites of Environment 2004). For acid extractable the Stream Gräsanoja catchment. metals, standardized metal concentrations Several measures, such as enrichment were used for the calculation of enrichment factors, geoaccumulation indexes and sedi- factors, and the best estimate of baseline ment pollution indexes, have been used to concentration was calculated as the average establish the extent of contamination over of the standardized baseline concentrations. background or baseline (Müller 1979, Suth- For easily reducible metal concentrations no erland 2000, Singh et al. 2002). In this study standardization was performed prior to the the contamination over baseline was estimat- calculation of enrichment factors (baseline ed by calculating the enrichment factors of concentration was estimated as the average the metal concentrations in the sediments. of the two baseline sites of the Gräsanoja Enrichment factors for metal concentrations dataset). (EF) were calculated as follows: 3.6.2. Evaluation sediment quality with reference to soil screening values and dredged material quality guidelines where Metalsample is the concentration of metal and Metalbaseline is the best estimate of Since no sediment quality criteria or envi- concentration (average) of metal in the base- ronmental quality standards (EQS) have yet line samples (Sutherland 2000, Andrews & been proposed for metals in the freshwater Sutherland 2004). A fi ve category ranking sediments in the EU or in Finland, the sedi- system of pollution proposed by Sutherland ment metal concentrations were compared (2000) was adopted: with the quality guidelines proposed nation- 1. EF <2 Minimal enrichment, minimal ally for soil and dredged material (Table 6). or no pollution Th e soil screening values concerning 2. EF 2 – 5 Moderate enrichment, mod- Cu, Zn, Pb and Cd are mainly based on erate pollution ecotoxicological grounds. Th ree levels have 3. EF 5 – 20 Signifi cant enrichment, sig- been established, an threshold level, and two nifi cant pollution guideline levels for diff erent land use catego- 4. EF 20 – 40 Very high enrichment, ries (Table 6). Th e threshold level is defi ned very strong pollution as ecotoxicologically relatively safe concen- 5. EF > 40 Extreme enrichment, extreme tration level (Reinikainen 2007). Th e lower pollution signal guideline level can be assumed to be safe to Metal concentrations of both the urban 50% of the species. stream sediments as well as natural base- Th e quality criteria of dredged material line sediments vary according to sediment are established as guidelines for managing properties. Th us, normalization of metal disposal of dredged material (Ministry of concentrations is usually needed in order to the environment 2004). Th e criteria include diff erentiate between natural variability and two concentration levels. If the metal con- anthropogenic input of contaminants (Ru- centrations of the dredged material are lower bio et al. 2000; Kersten & Smedes 2002). than the lower level (intervention level), the A normalization method recommended for material can be regarded as harmless. If the coastal marine sediments in Finland was metal concentrations fall between the two adopted here (Siiro & Kohonen 2003), i.e. levels, the material is defi ned as possibly metals were normalized to organic mat- polluted. Polluted material whose metal ter and clay percentage (metal concentra- concentrations exceed the higher level (pol- tions were converted to correspond with lution level) cannot usually be disposed to concentrations of standard sediment (10% sea (Ministry of the environment 2004).

27 Table 6. Metal concentration guidelines (in µg/g) for the quality of soil and dredged material in Finland. Criteria for dredged material are reported for standard sediment (clay content =25%, organic matter content = 10%). Reference Size Dissolution Zn Cu Pb Cd frac- method tion Th reshold value of soil contamination1) bulk Total 200 100 60 1 Higher guideline value of soil bulk Total 400 200 750 20 pollution (industrial/commercial and traﬃ c areas)1) Lower guideline value of soil pol- bulk Total 250 150 200 10 lution (other land use types)1) Intervention level for dredged material 2) <2mm Partial dis- 170 50 40 0.5 solution Pollution level for dredged material 2) <2mm Partial dis- 500 90 200 2.5 solution 1) Degree 214/2007, 2) Ministry of the environment 2004.

Prior to comparison with the quality crite- concern based on acid extractable metal con- ria (Table 6), sediment metal concentrations centrations in the sediment. Th e sites were have to be normalized (Metalst) using clay classifi ed into 3 classes: and organic matter content to correspond Class 1: indication of potential hazard, with standard sediment (clay content 25%, indicating that one metal exceeds the organic matter 10%). Th e criteria are based PLDM, but only by a small extent on background concentrations and interna- (up to 1.5 times with Cd and Pb; up tional toxicity assessments (Ministry of the to 2 times with Cu and Zn) environment 2004). Class 2: higher certainty that hazard is present, when the PLDM is exceeded 3.6.3. Hazard assessments by at least two metals Class 3: indication of potentially high hazard Th e ecological risk assessment of sediments when concentration of at least one usually includes the determination of hazard metal is more than 1.5 times (Cd and risk that the sediment poses on the envi- and Pb) or 2 times (Cu and Zn) the ronment. Hazard is the potential for adverse PLDM ecological eff ects. Risk, on the other hand, Class 0 was assigned to the sites not included is defi ned as the product of hazard and the in the group of sites of concern (no probability of exposure (Apitz & Power or weak indication of hazard). 2002, Förstner & Heise 2006). In this study a hazard assessment was conducted using 4. Sediment composition results sediment chemistry information. Th e quality guidelines for dredged material were utilized to identify the sites that 4.1. Chemical and physical show highest pollution potential. Hence, the properties of the sediments sites where at least one of the metals exceeded the pollution level of dredged material Th e general composition of the sediments (PLDM) were denoted as sites of concern. A was fairly similar between the datasets (Table further hazard ranking (modifi ed after Heise 7). Al showed highest variation between sites et al. (2004)) was employed to the sites of – and between datasets. While Al concen-

28 trations are generally low in the areal data- variations of sediment composition in the set, the sediments of the Gräsanoja datasets Stream Gräsanoja and its tributaries were show higher concentrations and variability. determined using principal component Th e range of variation was relatively high analysis. Th ree factors were extracted (Table for clay-% in the areal dataset, but for both 8), and together the explained 74% of the datasets clay-% lay between 15 and 25% at variation in the data. Th e first component most sites. is strongly associated with fi ne grained sediment, since strong loadings are attained by 4.1.1. Gräsanoja dataset clay-% and coarse silt-% (negative loading), as well as Li and Al. Th e high loadings of Figure 10 demonstrates the spatial pattern Fe- and Mn-oxides as well as TOC further of the sediment properties in the Stream show that, metal oxides and organic matter Gräsanoja and its tributaries. Th e spatial are associated with the fi ne sediment. Th e variations of the grain size distribution, component therefore refl ects the general clay-% in particular, were low, but one very accumulation of fi ne grained fl ocs which distinct pattern can be identifi ed. In the up- consist of soil aggregates as well as organic per parts of the catchment, the sediment is matter and metal oxides. Th e component particularly coarse grained, and the propor- is, thus, named as the component fl ocs and tion of coarse silt reaches almost 60%. Th is aggregates. Th e spatial distribution of the pattern is apparently associated with strong component scores showed that, the compo- supply of coarse silt from the small coarse nent of fl ocs and aggregates increases in the silt deposit around site number 8. Th e eff ect downstream direction in the main channel can be detected in the sediment for 1 km (Fig. 11). High scores were also recorded at downstream. Th e Li and Al concentrations the sites of the Streams Merituulentie and roughly follow the relative variations in the Lukupuro. Accumulation of fl ocs and ag- proportions of coarse and fi ne silt, increas- gregates were particularly low in the upper ing with decreasing coarse silt-% (the pro- reaches of the main channel (sites 8–13). portion of particles with size between 10 and PCA2 was mostly defi ned by Fe-oxides, 63µm in the silt and clay fractions (<63µm)). amorphous Fe-oxides and TOC, which in- However, Al concentrations are very high in dicates that, the component further refl ects the sediments of Lukupuro, compared to the the occurrence of fl ocs composed of metal grain-size parameters. oxides and/or organic matter. However, this Th e major factors behind the spatial component has a weakly negative associated Table 7. Medians and range (in parenthesis) of sediment properties in the two datasets.

Gräsanoja dataset Areal dataset TOC (g/kg) 59.1 (20.6–90.2) 68.9 (21.3–126.1) Fe (%) 5.37 (2.68–8.56) 4.08 (1.90–10.90) Fe-oxides (mg/kg) 19.3 (7.3–56.2) Amorphous Fe-oxides (mg/kg) 1.31 (0.60–3.31) Mn (mg/kg) 0.48 (0.24–0.93) 0.44 (0.19–1.83) Mn-oxides (mg/kg) 0.16 (0.01–0.48) Al (%) 3.17 (1.53–8.31) 2.28 (1.12–5.07) Li (mg/kg) 33.1 (15.6–48.7) 25.7 (11.7–40.1) Clay-% (<2µm) 24.0 (16.0–35.6) 20.9 (13.5–64.6) Coarse silt-% (10–63µm) 36.0 (22.5–56.6)

29 Figure 10. Composition of the sediment samples of the Stream Gräsanoja. Clay fraction: <2µm, fi ne silt: 2–10µm and coarse silt: 10–63µm. with Li and Clay-%, which indicates that matter is associated with Al-rich clay miner- fi ne mineral matter, i.e. soil aggregates, are als or precipitates of Al-oxides and hydrox- not incorporated in the fl ocs. Th e compo- ides (Al-mineral component). Th is component is named simply the fl oc component. nent seems to be associated with sediments Th e fl oc component was high in the sedi- originating from the catchment of Luku- ments of the Stream Merituulentie. In the puro, since particularly high scores of the main channel, thehighest accumulation of component were recorded in the sediments these fl ocs was recorded in the middle reach- of Lukupuro, as well as in the main channel es. Contrary to the other two components, of the Stream Gräsanoja below the confl u- the component of organic and metal oxide ence of the Stream Lukupuro. fl ocs obtained low scores in the lower reach- Th e fi rst three principal components es of the main channel. explained the majority of variation in the PCA3 is associated with Al and TOC sediment components, with the exception of bearing sediments which contain a low Mn-oxide which showed no marked trends amount of amorphous Fe-oxides. Although in the study area. Th e spatial variation of the interpretation of this component is not Mn-oxide content might be partly related to straightforward, it may be related to organic variations in the redox-condition. For exam-

30 Table 8. Results of the PCA analysis performed for the sediment composition of the Stream Gräsanoja and its tributaries. Clay particles: <2µm, coarse silt particles: 10–63µm. Bold text denotes absolute loadings greater than 0.3 (Legendre & Legendre 1998). PCA1 PCA2 PCA3 Communality Clay-% 0.88 -0.20 -0.36 0.95 Li 0.76 -0.47 -0.07 0.81 Al 0.65 -0.12 0.51 0.69 Fe-oxides 0.63 0.54 0.16 0.72 Mn-oxides 0.51 0.23 -0.06 0.32 TOC 0.42 0.45 0.53 0.66 Am. Fe-oxides 0.14 0.71 -0.54 0.81 Coarse silt-% -0.94 0.15 0.19 0.94 Eigenvalue 3.5 1.3 1.0 % of variation 44.1 16.8 12.8 ple, the zone of low Mn concentrations at 6 Th e results of the principal component km distance from the estuary (sites 25–28, analysis are strongly dependent on the vari- Fig. 10) is probably caused by occasional ables included in the analysis. Th erefore, the oxygen defi cits potentially resulting from strong eff ect of grain size indicators (clay-%, waste water overfl ows from a nearby pump- coarse silt-%, Al, Li) is partly a result of the ing station. Direct redox-measurements of variable selection process. Some direction the sediment were, however, not available. of variation that is only represented by one

Figure 11. Site scores of the ﬁ rst three principal components of the Gräsanoja dataset.

31 variable is probably overshadowed by direc- Th e variation related to this component was, tions represented by several variables. however, much weaker when compared to the soil aggregate component, and its eff ect 4.1.2. Areal dataset on the sediment composition may be easily overshadowed by the soil aggregate compo- In the principal component analysis of the nent. areal dataset two factors were extracted Th e intensity of urbanization appeared which together explained 65% of the vari- to be weakly related to the scores of the ation in the dataset. Th e first component fi rst principal component (Figure 12). Th e (PCA1) was positively related to clay-%, Al, densely built urban areas attained lower Li and Mn, and negatively related to TOC scores than the non-urbanized or low den- (Fig. 12). Usually clay aggregates are fl occu- sity residential catchment. Th e diff erence lated to form fl ocs composed of soil particles, between the two groups was statistically sig- organic matter and metal oxides. Th erefore, nifi cant (p<0.05), when determined using a positive relationship is generally observed the Mann Whitney U-test. between organic matter and decreasing par- According to correlation analyses (re- ticle size (Borovec 2000; Lin et al. 2003). sults not shown here) sediment components However, here the fi ne grained soil particles of the areal dataset were not related to the were deposited mainly as soil aggregates in- percentages of surfi cial sediment types or stead of fl ocs. Th us, this component refl ect- topographical variables. ed the occurrence of clay rich soil aggregates in the sediment (soil aggregate component). 5. Metal concentration results Th e observed behavior implies that, the accumulation of fi ne grained mineral material may be due to very strong supply of soil ma- 5.1. Metal concentrations terial through erosion: channel erosion, or enhanced surface erosion, as a result of for example construction activities. 5.1.1. Baseline concentrations Th e second component (PCA2) attained highest loading on Fe followed by TOC. Th e Th e baseline sites of both datasets were com- component was called the fl oc component, bined to present the baseline concentrations since it refl ected the accumulation of fl ocs of metals. Concentrations were slightly low- composed of Fe and organic matter. Th e er in the outskirts of the metropolitan region weak positive loadings of Al and Li on the but, overall, the concentrations exhibited component indicate the weak tendency of relatively low variation (Figure 13). Th e av- clay minerals to be incorporated in fl ocs. erage acid extractable metal concentrations

Figure 12. Principal component loadings (left) and site scores (right) of the sediment composition in the areal dataset.

32 of the nine background sites were for Zn 0.08–0.75 for Cd and 0.02–10.5 for Pb 108 mg/kg, for Cu 24.0 mg/kg, for Pb 12.1 (Fig. 26). mg/kg and for Cd 0.22 mg/kg. 5.2. Metal enrichment 5.1.2. Metals at the urban sites at the urban sites

Th e acid extractable metals in the silt and Th e standardized metal concentrations were clay fraction (<63 µm) displayed high vari- utilized to calculate the enrichment factors ability of concentrations among the urban over baseline (Fig. 15). Th e metals showed sites of both datasets (Fig. 14). Th e diﬀ er- mostly moderate contamination signals in ences between the two datasets were rela- both datasets. Th e sediments appeared to tively low, and the average concentrations of be most enriched with respect to Cu. More the datasets were close to one another. All than 30% of the sites showed signiﬁ cant or metals showed very high concentrations at a very strong pollution signal (EF>5). Th e several sites. Which sites were exceptionally sediments appeared to be the least contami- enriched varied, however, according to the nated with Cd and Pb. In the Gräsanoja metal in question. In the sand fraction, Cu dataset in particular over 40% of the sites and Zn concentrations were on the aver- showed no pollution signal with respect to age slightly lower than in the silt and clay Pb (EF<2). Th e median enrichment factors fraction, although the range of concentra- of the metals were 4.2 and 4.4 for Cu, 3.5 tions was very high. Th e concentrations of and 3.9 for Zn, 2.0 and 2.8 for Pb and 2.8 the metals in the easily reducible phase (in and 2.3 for Cd in the Gräsanoja dataset and mg/kg) of the silt and clay fraction ranged the areal dataset, respectively. between 9–218 for Zn, 0.01–7.6 for Cu, Th e enrichment factors for easily reduci-

Figure 13. Metal concentrations (in mg/kg) of the background sites.

33 Figure 14. Boxplot of the acid extractable metal concentrations of urban sites in the two datasets. Th e box represents the 25th percentile, median and the 75th percentile. Th e bars indicate the minimum and maximum values.

Figure 15. Ranking of the urban sites to diﬀ erent pollution categories according to enrichment factors of acid soluble metal concentrations.

34 Figure 16. Ranking of the sites to diff erent pollution categories according to enrichment factors of easily reducible metal concentrations in the Gräsanoja dataset. ble Cu and Pb were considerably higher than suggests that the Cu concentrations of the for acid extractable concentrations (Fig. 16). sediments are less than the threshold value at More than 60% of the sites for Cu and 30% more at 50% of the sites. However, the Cu of the sites for Pb appear to be signifi cantly, concentrations exceed the upper guideline very strongly or extremely polluted. For Zn level (proposed for industrial/commercial and Cd the enrichment was higher for acid and traffi c areas) at some sites. According to extractable concentrations. the soil screening values, the sediments are most polluted with Zn. More than 70% of 5.4. Comparison to soil the sites in both datasets show Zn concen- screening values and dredged trations that exceeded the lower guideline material quality guidelines value defi ned for residential and recreational sites. Th e comparison of Cu and Zn concen- In absence of appropriate sediment quality trations to the quality criteria for dredged guidelines, the sediment metal concentra- material further indicates that the sediment tions were compared to quality guidelines may be polluted at almost all sites. proposed for soil and dredged material (Fig. Th e sites of the Gräsanoja dataset were 17 and 18). Unstandardized metal concen- further ranked as hazard classes according to trations were used to compare the results the metal concentrations in relation to the with the soil screening values, while com- dredged material quality guidelines (Fig. parison with the dredged material quality 19). Th e sites of the hazard classes 1-3 can be guidelines was carried out using standard- denoted as sites of concern, since these sites ized metal concentrations. Th e results indi- are associated with a potential hazard due cate that the Pb concentrations of the sedi- to high metal concentrations. In this study, ments were mostly below the threshold level the metals that were responsible for the haz- of soils and the intervention level of dredged ard potential were Zn but particularly Cu. material. Cd concentrations are also mostly Th e sites of concern are located in the urban unpolluted according to soil screening val- headwater streams as well as in the lower ues, but 40 and 55% of the sites are possibly reaches of the main channel. Th e forested, polluted according to the quality criteria for agricultural and low density residential areas dredged material. Both quality guidelines show mostly only weak signs of potential indicate that the sediments are substantially hazard (class 0). Th e results further indicate, more polluted with respect to Cu and Zn. that high hazard potential is found at only Th e comparison with soil screening values three sites. At the site located in the estuary

35 Figure 17. Trace metal concentrations of the sampling sites in reference to soil screening values.

Figure 18. Trace metal concentrations of the sampling sites in reference to quality criteria of dredged material. the potentially high hazard is primarily due ban sites of the Gräsanoja dataset. Th e fine- to the exceptionally high binding capacity of scale spatial variations of acid extractable the organic rich and fi ne-grained sediment. trace metals (Fig. 20) were mostly low and nearly of the same order as the analytical 6. Results of the spatial and variation (Fig. 20). Th e fine-scale variations temporal variations were generally lower than the diff erences between the replicate sites, and between randomly selected sites in the Gräsanoja dataset 6.1. Variation across scales (Fig. 21). Th us, the fi ne-scale variations of acid extractable metals were unlikely to be Th e variation of trace metals at the fi ne spa- responsible for major spatial diff erences in tial and temporal scales were compared to the data. Th e fi ne scale variations of the met- the corresponding variations within all ur- als were somewhat higher in the easily re-

36 ducible fraction than in the acid extractable shows that the temporal variations are un- fraction (Fig. 22). However, the concentra- likely to mask major broad-scale spatial vari- tions showed very strong variations within ations. the urban sites, and the fi ne-scale spatial variations played no role in the ability to in- 6.2. Spatial autocorrelation terpret spatial trace metal patterns (Fig. 21). Th e temporal variation between two Trace metals received moderately high posi- base fl ow sampling campaigns was consid- tive values of Moran’s I in the shortest dis- erably higher than the fi ne-scale variation tance lags, which suggests moderate spatial for all trace metals (Fig. 20). Th is result sug- dependence (Figure 24). However, none of gests that small spatial diff erences in metal the autocorrelation values were statistically concentration between sampling sites do signifi cantly higher than the expected value not have a meaningful interpretation. Th e under the null hypothesis of no spatial au- temporal variations of the easily reducible tocorrelation. Th is may be related to the metals, particularly CuER and PbER, may low number of site pairs particularly in the be very high (Fig. 22). However, fi gure 23 shortest distance lag (20 pairs). Th e correlo-

Figure 19. Ranking of study sites to hazard classes in the Stream Gräsanoja. Class 0: no or weak indication of hazard, class 1: indication of potential hazard, class 2: higher certainty that hazard is present, class 3: indication of potentially high hazard.

37 Figure 20. Fine-scale spatial and temporal variation of trace metals (both analytical replicates shown). grams suggest that weak spatial dependence tion appeared to extend to 0.75 km for Cu, exists despite the lack of signiﬁ cance. For Cu Zn and Pb and 1km distance for Cd. and Pb the strongest spatial autocorrelation Th e residuals from the sediment compo- extends to 0.75 km, for Cd to 1km and for sition model were also tested for statistical Zn 1.25 km distance. autocorrelation in order to show how much Th e spatial autocorrelations of standard- of the local spatial pattern is attributable to ized metal concentrations were somewhat other factors than sediment composition weaker especially for Cu and Cd. No statis- (the residuals show that part of metal con- tically signiﬁ cant spatial autocorrelation was centration variations that is independent of recorded, although some weak autocorrela- sediment composition). Th e values of Mo-

Figure 21. Variation between the ﬁ ne-scale spatial replicates at seven sites (circles), and the distribution of the variation between two randomly selected urban sites in the Gräsanoja dataset. Th e box represents the 25th percentile, median and the 75th percentile. Th e bars indicate the minimum and maximum values.

38 Figure 22. Fine-scale spatial and temporal variation of easily reducible metal concentrations.

Figure 23. Variation between the temporal replicates at seven sites (circles), and the distribution of the variation between two randomly selected urban sites in the Gräsanoja dataset. Th e box represents the 25th percentile, median and the 75th percentile. Th e bars indicate the minimum and maximum values. ran’s I for the residuals show that the spatial autocorrelation is weak for Cu, Zn and Cd, whereas Pb shows spatial autocorrelation at the two shortest distance lags (Fig. 25). No statistically signiﬁ cant autocorrelation was measured for the residuals.

39 Figure 24. Spatial correlograms (Moran’s I) for metal concentrations and standardized metal concentrations (both log-transformed).

40 7. Results of the control of metals. Sediment components defi ned by sediment composition on metals this component were coarse silt-% (positive loading) and clay-%, Al, Fe-oxides and Mn- oxides (negative loading). When the fi ne 7.1. Control of sediment grained sediment is not associated with high composition on acid extractable organic matter content, the component refl ects the tendency of fi ne grained sediments metals in the Gräsanoja dataset to contain low metal concentrations,. Th e correlations between metals and 7.1.1. Regional variations sediment components support the graphical representation of the PCA analysis (Ap- Principal component analysis was used to pendix 2). However, nearly all of the cor- study and graphically represent the relation- relations were non signifi cant, due to the ships between acid extractable metals and small amount of independent observations sediment components (Fig. 26). Th e first estimated. Th e most important sediment two components, which explained 57% of parameter controlling the metal concentra- the variation, refl ected the association of tions was TOC for Cu, Zn and Cd. For Pb, metals with sediment components. Th e first the impact of sediment composition ap- component was strongly positively related to peared to be very complex. Clay-% did not all of the metals as well as most of the sedi- play an important role in defi ning the acid ment components. Only coarse silt-% was extractable metal concentrations. strongly negatively related to this component. Th e axis represents the primary asso- 7.1.2. Fine-scale spatial and ciation of metals with fi ne grained sediments temporal variation rich in organic matter and metal oxides. Th e Th e control of sediment composition on the second principal component (24% of varia- fi ne-scale spatial and temporal variation of tion) was also positively related to all of the metals was studied by calculating the partial

Figure 25. Spatial correlograms (Moran’s I) for the residuals of the sediment composition models of metals.

41 Figure 26. Results of the PCA analysis conducted for the Gräsanoja dataset. correlations between sediment components were extracted and, accounted for 63% of and metals, controlling for the eff ect of vari- the total variation. Th e fi rst component is ations between sampling sites. Th e results closely positively related with all metals and were more variable than at the regional scale. TOC, and strongly negatively related with Th e most consistent pattern is the negative Clay-%. Li, Al and Mn were also negatively correlation of coarse silt-% with all metals. associated with the fi rst component. Th e Th e correlation matrix demonstrates that component probably refl ects the impact of Cu and Zn concentrations were positively recent soil erosion on metal concentrations. related with sediments composed of organic Th ese fi ne grained sediments dilute both the matter and Fe-oxides (Table 9). Pb and Cd trace metal and organic matter concentra- did not demonstrate the positive depend- tions in the sediment. Consequently metal ence on TOC, making the results diff erent concentrations are closely associated with to those of Cu and Zn. organic matter and negatively related with Th e temporal variations of Cu, Zn and properties indicating fi ne grained sediment. Pb were also strongly related to the changes Th e second principal component was in the TOC content of the sediment (Table most strongly related to Li and Al which 9). In addition, Fe- and Mn-oxides were re- refl ect the abundance of clay minerals. Th e lated with the concentrations of Zn and Pb. component was also positively related to Clay-%, Mn and Fe as well as all metals. 7.2. Th e eff ect of sediment Highest loading was attained by Cd, lowest composition on trace metals by Cu. Th e component refl ects the associa- in the areal dataset tion of metals with fi ne grained rather than coarse grained sediments if the sediments Th e relationships between metals and sedi- contain an equal amount of organic matter. ment components were summarized and Th e correlations further revealed that the graphically represented using principal com- most important sediment parameter aff ect- ponent analysis (Fig. 27). Two components ing the metal concentrations was TOC that

42 Figure 27. PCA analysis of metals and sediment components in the areal dataset. showed signiﬁ cant positive correlation with with the sediment components. Th is is be- all metals. In addition, the metals correlated cause the metals shared a common spatial statistically signiﬁ cantly with Clay-% (nega- distribution between sampling sites, since tive relationship) (Appendix 3). the correlations between metals were mostly Th e results of both the principal compo- relatively high (r>0.48). Highest correlation nent and correlation analyses show that the was observed between Cu and Zn (r=0.87). metals had relatively similar relationships

Table 9. Partial correlations between sediment components and metals for the spatial and temporal replicates (<63µm fraction), controlling for the eﬀ ect of concentrations diﬀ erences between sampling sites (df=5). Fine-scale spatial variation Temporal variation

log10Cu log10Zn log10Pb log10Cd log10Cu log10Zn log10Pb log10Cd TOC 0.90 0.75 -0.40 -0.09 0.74 0.95 0.78 0.19 Am. Fe- 0.65 0.58 -0.39 0.05 0.03 0.51 0.65 0.69 oxides Fe-oxides 0.51 0.76 0.01 0.51 0.35 0.62 0.67 0.14 Mn-oxides 0.30 0.08 -0.37 -0.68 0.31 0.76 0.79 0.05 Clay-% 0.14 0.41 0.05 0.86 0.09 0.33 0.51 0.17 Coarse -0.10 -0.47 -0.49 -0.52 -0.32 -0.50 -0.61 0.09 silt-% Al -0.47 -0.42 0.64 -0.28 0.30 0.56 0.61 0.33 Li -0.72 -0.52 0.56 -0.01 0.25 0.11 0.05 -0.07

43 7.3. Metal partitioning in ferent phases varied somewhat among the the Gräsanoja dataset sites, the variations of Cu and Zn in most of the operational phases followed closely the acid extractable metal concentrations 7.3.1. Metal concentrations in closely. Hence, the concentrations of metals diﬀ erent binding phases in all phases increased with increasing acid extractable metal concentration and the pro- On the average, the easily reducible phase portions of Cu and Zn between the opera- constituted 50% (range 19.7–94.4%) of the tional phases were quite stable over the study acid extractable Cd, 17% (range 5.2–29.5%) area. In the headwater reaches of the main of Zn, 2.5% (range 0.2–19.6%) of Pb and channel Cu was, however, strongly bound 1.4% (range 0.0–8.3%) of Cu. Hence, Cd to the reducible phase and the proportion and Zn appeared to be distinctly more mo- bound to the organic phase was lower than bile compared to Cu and Pb. elsewhere. On the contrary, the proportion A large proportion of Cu and Zn ap- of Cu in the residual phase was low com- peared to be associated with the pseudor- pared to other sites in the coarse-grained esidual phase (on the average 29.7% of Zn sediments of sites 8 and 9 as well as at sites and 39.8% of Cu) (Figure 28). According 20–22. For Zn, the spatial variations of the to the results of the selective extraction, Zn relative importance of diﬀ erent binding was preferentially bound to the metal oxides phases were equally small. Th e proportion (on the average 47.0%), and it was found to of the residual phase can be assumed to be partition in the order residual = reducible > exceptionally low at sites 8 and 9, since the easily reducible > organic phase. Cu on the sum of Zn in the three most mobile phases other hand showed a strong tendency to be was even greater than the acid extractable Zn associated with organic compounds (on the concentration. average 39.9%), and it partitioned in the order residual = organic > reducible > easily reducible phase. Although the proportions bound to dif-

Table 10. Correlation coeffi cients between sediment components and arcsine-transformed metal proportions in diff erent binding phases. Degrees of freedom for the calculation of statistical signifi cance=22 (24-2). Bold text denotes signifi cant correlations.

TOC FeER FeR MnER Clay-% Coarse Li Al silt-% CdER% -0.51 0.24 -0.14 0.10 0.05 0.09 -0.22 -0.19 PbER% -0.58* -0.30 -0.59* -0.34 -0.38 0.48 -0.37 -0.26 ZnER% -0.23 0.22 -0.43 -0.21 -0.51 0.56* -0.60* -0.62* ZnR% 0.00 -0.25 -0.03 -0.09 -0.42 0.38 -0.26 -0.21 ZnOrg% 0.03 0.05 0.12 0.24 0.06 0.01 -0.24 0.32 ZnRes% 0.15 0.09 0.21 0.11 0.57* -0.59* 0.59* 0.40 CuER% -0.67* -0.14 -0.58* -0.27 -0.25 0.37 -0.32 -0.36 CuR% -0.25 -0.03 0.22 0.09 -0.05 0.12 -0.10 -0.05 CuOrg% 0.25 0.14 0.25 0.03 -0.23 0.20 -0.28 0.02 CuRes% 0.16 -0.01 -0.21 -0.04 0.28 -0.34 0.40 0.07

44 Figure 28. Partitioning of Cu and Zn between diﬀ erent binding phases.

7.3.2. Control of sediment composition the proportions of metals in the reducible, on metals in diff erent phases organic and residual phases, their corresponding concentrations were positively as- Th e correlations between sediment com- sociated with increasing TOC. ponents and metal proportions in diff erent binding phases showed that easily reducible 8. Results of land use impacts on metals decrease with increasing TOC (Table metals 10). Furthermore, CuER% and PbER% have a negative relationship with FeR concentration. Fine grained sediments were positively 8.1. Relationships between land associated with high proportions of Zn and use and sediment composition Cu in the residual phase, which refl ected the high metal concentrations of clay minerals. In the areal dataset TOC was positively asso- With decreasing grain-size the proportion ciated with urbanization, since the baseline of easily reducible Zn also decreased. While sites showed statistically signifi cantly lower TOC had relatively low concentrations with TOC concentrations when compared with

45 Figure 29. Relationships between sediment composition (TOC and clay-%) and land use of the catchment. Th e box represents the 25th percentile, median and the 75th percentile. Th e bars indicate the minimum and maximum values. the urban sites (Fig. 29). Th e baseline sites the urbanized areas, low density residential had higher proportions of the clay fraction. areas had a negative relationship with metal Within the urban sites, TOC was positively concentrations, which showed that low den- and clay-%, Li and Al negatively related to sity residential areas contribute less to the the percentage of high density residential ar- metal load of streams compared to more eas (correlations not shown here). Th e rest of densely built areas. the correlations between land use and sedi- Within the urban sites, the metals cor- ment composition were relatively low. related equally strongly with the land use metrics at the immediatre upstream catch- 8.2. Relationships between land ment scale when compared with the entire use and metal concentrations upstream catchment scale. Th e percentages of impervious areas of the riparian zone showed relatively high correlations when 8.2.1. Acid extractable metals compared with the correlations at the entire in the Gräsanoja dataset and immediate upstream catchment scales (Table 11). Th us, the local land use appears Based on fi gures 30 and 31 the association to be important in the urban environment. of trace metals with urbanization is evident. Th e spatial distribution of all metals was With the exception of a few sites, the sub- very similar, which suggests that these metals urban sites exhibited considerably higher have primarily common sources in the study metal concentrations when compared with area. However, the metals had slightly diff er- the non-urbanized Stream Lukupuro. ent correlation patterns with land use metrics. Within the urban sites, metal concen- Th e most distinct diff erences between the met- trations showed substantial variation un- als were the low standardized concentrations related to the degree of urbanization (Fig. of Pb in the stream Merituulenoja compared 31). Th us, the correlations between metals to the high concentrations of Cu, Zn and Cd. and urban land use were only moderate Th e spatial distribution of metal concentra- (Table 12). Th e percentages of dense urban tions (Fig. 30) further reveals that the Pb con- areas and impervious surfaces were among centrations are exceptionally high at sites 11, the most important correlates. In addition, 12, 14 and 15. Th is is related to the proximity commercial and traffi c areas were strongly of the sites to the heavily traffi cked Turunväylä related with Cu and Zn (Fig. 29). Within highway which is located nearby.

46 Figure 30. Spatial distribution of standardized acid extractable metal concentrations in the Stream Gräsanoja.

47 Table 11. Correlations between standardized acid extractable trace metals and diﬀ erent land use metrics for the urban sites of the Gräsanoja dataset (log-transformed metal concentrations and densities, arcsine-transformed land use percentages) (df=17).

Cust Znst Cdst Pbst Entire upstream catchment Urban (%) -0.01 0.15 0.10 0.10 Dense urban (%) 0.13 0.23 0.14 0.23 TIA (%) 0.14 0.22 0.09 0.30 EIA (%) 0.12 0.21 -0.00 0.21 Low density residential (%) -0.22 -0.16 -0.20 -0.40 High density residential (%) -0.03 0.16 0.12 -0.08 Institutional (%) 0.02 -0.00 0.07 0.22 Commercial & traffi c (%) 0.25 0.25 -0.05 -0.09 Industrial (%) -0.01 0.11 -0.25 -0.04 TIA_Roofs 0.04 0.17 0.00 0.12 TIA_Transport areas 0.20 0.25 0.13 0.36 Traffi c density 0.25 0.06 0.22 0.16 Population density -0.07 0.17 0.06 -0.48 Immediate upstream catchment (local) Urban (%) 0.16 0.22 0.22 0.01 Dense urban (%) 0.27 0.16 0.16 0.10 TIA (%) 0.21 0.14 0.22 0.04 Low density residential (%) -0.12 0.15 0.15 -0.12 High density residential (%) 0.10 0.02 -0.00 -0.01 Institutional (%) 0.03 0.06 0.20 0.12 Commercial & traffi c (%) 0.29 0.12 0.19 -0.11 Industrial (%) 0.15 0.09 -0.07 0.30 TIA_Roofs 0.20 0.34 0.25 -0.09 TIA_Transport a reas 0.19 0.08 0.19 0.07 Land use of the local riparian zone TIA 0.26 0.29 0.29 0.16

8.2.2. Easily reducible metals 32). High easily reducible metal concentrations in the Gräsanoja dataset were located close to densely built areas (high density residential, institutional, commercial and Th e easily redicuble concentrations of metals also industrial areas), although the Cu and Pb seem demonstrated the association of metals with ur- to diﬀ er from Zn and Cd in the spatial distribu- ban land use. Metal concentrations were gener- tion of the highest concentrations. Pb and Cu ally higher at the urban sites than the non-urban concentrations remained very high for a long dis- sites in the Stream Lukupuro, although concen- tance downstream of the industrial area of Kara- trations comparable to those of the Stream Luku- malmi in the upper reaches of the main channel puro were also recorded at several urban sites (Fig. even though the areas surrounding the stream

48 Figure 31. Relationships of standardized metal concentrations with TIA and the percentage of commercial and traﬃ c areas in the entire upstream catchment (land use percentages of the entire upstream catchment arcsine-transformed). Urban sites: open circles, agricultural and baseline sites: ﬁ lled circles.

49 Figure 32. Spatial distribution of easily reducible metals in the Stream Gräsanoja. were dominated by low density residential the general indices of urbanization within the housing. Zn and Cd also showed elevated urban sites (Table 12). In addition, particular- concentrations in the headwater sites close ly Cu and Zn correlate positively with the per- to the industrial area, but concentrations centages of commercial and traﬃ c areas, and decrease more rapidly in the downstream industrial areas. Th e correlations of the easily direction. reducible metals were considerably stronger Th e easily reducible concentrations of all when compared with the results of the acid metals were relatively strongly correlated with extractable metals.

50 Table 12. Correlations between easily reducible metals and land use metrics of the entire upstream catchment for the urban sites of the Gräsanoja dataset (log-transformed metal concentrations and densities, arcsine-transformed land use percentages) (df=17).

CuER ZnER CdER PbER Urban (%) 0.43 0.44 0.31 0.45 Dense urban (%) 0.27 0.43 0.36 0.29 TIA (%) 0.38 0.42 0.31 0.41 EIA (%) 0.38 0.41 0.23 0.37 Low density residential (%) 0.03 -0.19 -0.19 -0.17 High density residential (%) -0.06 0.27 0.13 0.04 Institutional (%) -0.10 0.04 0.17 -0.00 Commercial & traﬃ c (%) 0.37 0.39 0.19 0.08 Industrial (%) 0.45 0.19 -0.09 0.22 Traﬃ c density -0.22 -0.03 0.04 -0.25 Population density -0.18 0.27 0.03 -0.28

8.2.3. Areal dataset taset correlations between metals and urban land use metrics were moderate (Table 13), Th e most striking diff erence in metal con- and Cu, Pb and Zn concentrations increased centrations occured between the unbuilt with increasing density of urbanization. Th e and the urban catchments (Fig. 33 and 34). correlations with metals were lower for the While metal concentrations at the baseline individual land use types, but the negative sites were very low, all of the other land use correlation between low density residential groups, including the low density residen- areas and metals shows that the dense urban tial areas, showed statistically signifi cantly land use types are essential for the occur- higher metal concentrations (Fig. 33). Th e rence of high metal concentrations within diff erences between the urban land use the urban catchments (Fig. 32). groups were fairly small, and the only statis- Th e population density was weakly or tically signifi cant diff erences were recorded negatively related with metals (Table 13). for Pb and Cu concentrations between the Since industrial, commercial and institu- groups of low density residential areas and tional areas decrease the correspondence high density residential, institutional, com- between population density and metal con- mercial and traffi c areas. Industrial sites centrations, the correlations were calculated showed the highest median concentrations separately for the residential sites (where the of Zn, Cd and Pb. However, the industrial residential areas accounted for more than sites also showed highest variation of metal 50% of the built-up areas). Th e correlations concentrations, and the concentrations did were not, however, substantially stronger ex- not diff er statistically signifi cantly from cept for Pb (the correlation coeffi cients were other urban land use types. Th e land use cat- 0.06 for Cu, 0.21 for Zn, -0.06 for Cd and egories explained 56, 54, 45 and 39% of the 0.37 for Pb). standardized concentrations of Cu, Zn, Pb Th e correlations were higher for the and Cd, respectively. For the urban sites the land use of the entire upstream catchment, percentages were substantially lower, namely when compared to the immediate upstream 18% for Cu, Pb and Cd and 6% for Zn. catchment (Table 13). Th is highlights the Within the urban sites of the areal da- importance of the entire upstream catch-

51 Figure 33. Impact of the main land use type on the standardized metal concentrations in the areal dataset. Th e box represents the 25th percentile, median and the 75th percentile. Th e bars indicate the minimum and maximum values. ment in determining the quality of aquatic ban sites (TIA<15%) showed constantly low sediments. concentrations of all metals. With increasing TIA the metal concentrations become even 8.2.4. Th e relationship of TIA more variable, and both very low and high with metals for both datasets values are recorded. Th us, high urbanization does not necessarily cause high metal Th e graphical presentation of metal concen- concentrations, but, conversely, high con- trations against TIA of the entire upstream centrations that exceed the pollution level catchment demonstrates that the changes in for Zn and Cu and intervention level for average metal concentrations are steeper with Cd and Pb are related to high impervious- higher imperviousness (Fig. 35). Th e metal ness (TIA>30%). However, several high Pb concentrations increase only modestly until concentrations occured even with very low TIA reaches 3-40% level, but considerable levels of TIA, which indicates that factors variation is present in the concentrations. Cd causing Pb enrichment are not directly as- demonstrates very gently increasing trend sociated with the intensity of urbanization with increasing TIA. However, the non-ur- in the entire upstream catchment. Figure 35

52 Table 13. Correlations between acid extractable and standardized acid extractable trace metals and diﬀ erent land use and connectivity metrics in the areal dataset (log-transformed metal concentrations and densities, arcsine-transformed land use percentages) (degrees of freedom=65).

Cust Znst Cdst Pbst Entire upstream catchment Urban (%) -0.06 0.19 0.25 0.03 Dense urban (%) 0.36* 0.26 0.24 0.46** TIA (%) 0.36* 0.27 0.09 0.30 EIA (%) 0.38* 0.22 0.12 0.34** Low density residential (%) -0.36* -0.15 -0.15 -0.40* High density residential (%) 0.26 0.19 -0.10 0.25 Institutional (%) 0.20 0.09 -0.08 0.17 Commercial & traffi c (%) 0.29 0.19 0.20 0.21 Industrial (%) -0.13 0.10 0.37* 0.26 TIA_Roofs (%) 0.27 0.31 0.27 0.26 TIA_Transport areas (%) 0.35* 0.21 -0.01 0.28 Traffi c density 0.32 0.20 0.05 0.27 Population density 0.12 0.08 -024 0.05 Immediate upstream catchment (local) Urban (%) 0.13 0.16 -0.18 0.35* Dense urban (%) 0.27 0.12 -0.11 0.18 TIA (%) 0.06 0.25 -0.08 0.18 Low density residential (%) -0.20 -0.03 -0.09 0.04 High density residential (%) 0.33* 0.09 -0.05 0.15 Institutional (%) 0.23 0.05 -0.14 0.08 Commercial & traffi c (%) 0.05 0.11 -0.07 -0.12 Industrial (%) -0.06 0.06 0.04 0.15 TIA_Roofs (%) 0.00 0.23 0.04 0.22 TIA_Transport areas (%) 0.09 0.22 -0.14 0.14 further shows that the metal concentrations 9. Results of the modelling of are not elevated at all industrial sites, but a the trace metal concentrations in high proportion (over 50%) of the industrial urban catchments sites show metal concentrations above the pollution level (Cu and Zn) or intervention level (Cd and Pb). 9.1. Linear regression models

Th e metal concentrations were modelled using one combined dataset, which included independent urban sites (urban >30%) from both datasets. From the Gräsanoja dataset, only 24 sites, separated by more than 750

53 Figure 34. Relationships of standardized metal concentrations with TIA and the percentage of low density residential areas in the entire upstream catchment (arcsine-transformed land use percentages). Urban sites : open circles, baseline sites : ﬁ lled circles.

54 Figure 35. Relationship of standardized metal concentrations with imperviousness of the catchment (TIA). Industrial sites are indicated with an arrow. Th e dotted lines represent the intervention level (lower line) and pollution level (upper line) of the dredged material quality guidelines. metres, were included in the model to avoid liers. Most of these sites were, however, not the lack of independence of observations. infl uential, and were not discarded from the Firstly, linear regression models were analyses. Some of the outlier (negative resid- constructed using sediment composition ual) sites may represent exceptional environ- variables and land use metrics as explana- mental conditions, such as very coarse grain tory variables (Table 14). All of the models size that cannot be accounted for by the included TOC as explanatory variable. Th e model. Th e high positive residuals, on the rest of the variables varied between the met- other hand, are likely to refl ect other metal als. However, all of the models indicated sources in addition to the diff use metal pol- that metal concentrations increased with lution from urban areas, represented by the increasing intensity of land use. Th e models bulk of the samples. were relatively unbiased and did not show Th e models were evaluated using tenfold substantial heteroskedasticity (Figure 36). cross-validation. Th e predictive ability at sites However, several sites were identifi ed as out- not used for model construction appeared to

55 Table 14. Linear regression models. Explanatory variables selected from the set of sediment composition variables and land use metrics (see Table 4 for the land use abbreviations). Evalu- ation of the models: coeﬃ cient of determination (R2), Spearman’s rank correlation coeﬃ cients (rs) between observed and predicted values of the dependent variables and average absolute bias (AB). Land use percentages were arcsine-transformed and densities log10-transformed.

Model formula Final model Cross-validation R2 rs AB R2 rs AB Log10Cu=1.515+0.004·TOC+ 0.42 0.66*** 0.12 0.20 0.55*** 0.14 0.326·COMM_c+0.146·HDR_b +0.001·Traﬃ c density

Log10Zn=1.914+0.004·TOC+0.292· 0.46 0.67*** 0.10 0.27 0.58*** 0.11 COMM_c+0.233·TIA_b+0.006·Li

Log10Pb=1.229+0.004·TOC- 0.36 0.66*** 0.16 0.02 0.36*** 0.19 0.181·LDR_c+0.001·Traﬃ c density -0.226·COMM_b

Log10Cd=-0.884+0.005·TOC+ 0.35 0.57*** 0.13 0.02 0.47*** 0.15 0.431·IND_c+0.010·Li-0.180·IND_b be moderate (Table 14). Th e coeffi cient of using standardization instead of incorpora- determination decreased substantially com- tion in the model. However, the results show pared to the fi nal model, but the Spearman’s that within the urban zone, metal concentra- correlation coeffi cient between the observed tion diff erences are not strongly determined and predicted values was more stable. Cu by land use variations. and Zn models showed less deterioration For validation data, the predictive per- between the fi nal model and the cross-vali- formance of the models was even weaker dation compared to Pb and Cd. Th us, Cu (Table 15). Th e models failed to explain and Zn concentrations can be to some ex- even a small percentage of the variation. For tent predicted at new unvisited sites based Cd no statistically signifi cant explanatory on the sediment composition and metal variables were found for the calibration da- concentrations. Th is is also supported by the taset, and thus no statistics for the validation graphs representing the predicted median dataset can be presented. Although the ob- concentrations and observed concentrations served metal concentrations showed strong of metals (Figure 37). Th e median value of- deviation from the predicted concentrations fers some indication of the metal concentra- (Fig. 38), the Spearman’s correlation coef- tion level at the site. However, the 5% confi - fi cients indicated some correlation between dence bounds for individual concentrations the observed and predicted values. However, are very wide compared to the range of the the correlations were smaller than for the fi - predicted median concentrations. nal model. Next, linear regression models were Th e average bias of the land use-metal constructed for the standardized metal con- concentration models was slightly higher centrations using land use metrics as ex- than the corresponding values for the land planatory variables (Table 15). Th e models use-sediment composition-metal concentra- explained only a low proportion of the total tion models, which shows that the incor- variation, since the coeffi cient of determina- poration of the sediment composition in tion was less than 15% for Zn, Pb and Cd. the models does provide better predictive Th e lower explanation power of the current performance compared to standardization models compared to the models described of metal concentration according to the in- above is partly attributable to the fact that structions given for dredged material. the sediment composition is accounted for

56 Figure 36. Studentized residuals of the models linking metal concentrations to sediment composition and land use. Outliers : fi lled circles (G=Gräsanoja dataset). 9.2. Logistic regression models and logistic regression models were created using land use metrics as explanatory vari- Th e occurrence of concentrations higher ables (Table 16). Using the complete data- than the dredged material quality guidelines set, fi nal models were obtained for Cu, Zn was modelled using logistic regression. Th us, and Pb. For Cd, no statistically signifi cant the occurrences of metal concentration above variables were identifi ed. the pollution (Cu, Zn) or intervention level Th e predictive ability of the fi nal model (Pb, Cd) were used as the dependent vari- was good for Zn, while the models of Cu ables (CuHigh, ZnHigh, PbHigh, CdHigh), and Pb were poor according to Cohen’s Ka- Table 15. Regression models of standardized metal concentrations (see Table 4 for the land use abbreviations). Evaluation of the models: coeffi cient of determination (R2), Spearman’s rank correlation coeffi cients (rs) between observed and predicted values of the dependent variables and average absolute bias (AB). Land use percentages were arcsine-transformed and densities log10- transformed. Final model Cross-validation Model formula R2 rs AB R2 rs AB Log10Cust=1.885-0.114·LDR_ 0.24 0.53*** 0.13 0.02 0.31** 0.15 c+0.163·HDR_b+0.001·Traffi c density

Log10Znst=2.266+0.493·TIA_c 0.12 0.40*** 0.12 -0.18 0.16 0.14 Log10Pbst=1.635-0.261·LDR_c 0.13 0.37** 0.19 0.01 0.28* 0.20 Log10Cdst =-0.313+0.276·IND_c 0.10 0.16 0.14 -- -

57 Figure 37. Observed metal concentrations and the median predicted concentration from the 10- fold cross-validation (explanatory variable groups were sediment composition and land use). ppa or moderate according to the coeffi cient logistic regression model of Zn performed of AUC (Table 16). Th e values of positive almost equally as well. Th e predictive capac- prediction power show that the proportion ity of the Zn-models remained moderate- of present sites (those, where observed con- good even when applied to data not used centration exceeds the guideline) among the in the calibration of the models. Both the sites that were predicted to be present sites threshold independent AUC and kappa that is considerable higher than among the com- used the prevalence cutoff indicated poor plete dataset. However, only 64–67% of all performance for Cu and Pb. However, the the sites were correctly predicted. more detailed investigation of the statistics Th e models were thenevaluated by cal- reveals that the Cu model performs slightly culating the predicted probabilities of guide- better than the very poor model of Pb. Th e line exeedance using tenfold cross-valida- predicted presences or absences of Pb guide- tion. Th e models, particularly that for Pb, line exceedance seem to be no better than showed some deterioration compared to those obtained by chance. Th e proportion of the fi nal model (Table 17). However, the actually present sites in the group predicted

58 Figure 38. Observed metal concentrations and the median predicted concentration from the 10- fold cross-validation (explanatory variable group: land use). to be present is nearly the same as in the 10. Results of the diff erences complete dataset. Th us, the model does not between mud and sand size appear to provide any potential for screening fraction of the data for the occurrence of guideline exceedance. Cu model, on the other hand, Th e sand fraction (63-200µm) left on the does not show any better overall perform- sieve was analysed for some sediment pa- ance, but the grouping of sites according to rameters in order to estimate the impact of the predicted presence or absence does pro- sieving on the reliability of the results. Th e duce some concentration of actually present concentrations of trace metals were meas- sites to the group predicted to be present. ured from the sand fraction, and the concentrations were on the average between 70 and 77% of the concentrations in the mud fraction (Figure 14). Th us, the somewhat lower trace metal concentrations refl ect the diluting eff ect of the sand fraction, i.e. the lower trace metal concentrations and bind-

59 Table 16. Logistic regression models (ﬁ nal models) and their performances (see Table 4 for the land use abbreviations). Area under the curve (AUC) is a threshold independent measure, while the rest of the evaluation statistics were calculated using the cutoﬀ threshold which corresponded to the prevalence in the data. LP=linear predictor of the logistic regression model formula.

Dependent variable Cust > pollution level Znst > pollution level Pbst > intervention level Model formula LP=-1.897 LP=-21.854 LP=-0.756 +2.101·HDR_b +11.746·TIA_b -2.978·LDR_c +0.01·Traffi c density +6.186·LDR_c +1.677·LDR_b +8.713·HDR_c +2.599·IND_b +15.433·COMM_c +5.885·IND_b AUC 0.755** (moderate) 0.917*** (very good) 0.752** (moderate) Cutoff threshold 0.58 0.15 0.26 Kappa 0.277** (poor) 0.426*** (good) 0.278** (poor) Sensitivity 0.57 0.83 0.67 Specifi city 0.74 0.78 0.67 Positive predic- 0.75 0.40 0.41 tion power Accuracy 0.64 0.79 0.67

ing capacity of the coarse grained sediment decreasing grain size (Al, Li) and increasing particles. Fe and Mn concentrations, which were not Sediment composition had a stronger in- observed in the fi ner fraction. fl uence on trace metal concentration in the Th e control of sediment composition on sand fraction compared to the mud fraction the fi ne scale variation was also stronger for (Table 18). Metal concentrations were high- the sand fraction (Table 18), since most of est in sediments containing abundant organ- the correlations were statistically signifi cant. ic matter, corresponding with the results of In the sand fraction, Cu and Zn correlated the silt and clay fraction. Furthermore, met- strongly with Fe, Mn and Al, which refl ects al concentrations appear to increase within the importance of sediment grain-size, and

Table 17. Predictive performance of the logistic regression models in the 10-fold cross-validation (the evaluation data was not used for model construction). AUC=area under the curve.

Cust > pol- Znst > pollution level Pbst > interven- lution level tion level AUC 0.577 (poor) 0.856*** (moderate) 0.544 (poor) Cutoﬀ threshold 0.58 0.15 0.26 Kappa 0.144 (poor) 0.452*** (good) 0.043 (poor) Sensitivity 0.57 0.92 0.38 Speciﬁ city 0.62 0.77 0.67 Positive predic- 0.68 0.42 0.28 tion power Accuracy 0.59 0.79 0.59

60 the occurrence of clay minerals and Fe-ox- 11. Water analysis results ides. Organic matter was, however, not as strong a control on metals as in the mud fraction. 11.1. Water chemistry and Th e fi ne-scale spatial variation of the sand trace metal concentrations fraction appeared to be slightly greater than that of the mud fraction (<63µm). However, Th e waters of the sampling sites in the areal due to the high variations between sites in dataset were generally of near neutral pH, the sand fraction, the fi ne-scale variations well oxygenated and had moderate con- did not strongly interfere with the interpre- ductivity (Table 20). During the snow-melt tation of spatial patterns in metal concen- period, however, several sites, both baseline tration data. Th e analytical variation is also and urban sites, were acid with pH of less higher in the sand fraction compared to the than fi ve. During the low-fl ow period only more homogeneous mud fraction (Fig. 39), one baseline sites had pH lower than fi ve. but these variations are also overshadowed Th e snow-melt period also demonstrated by the strong regional variations of metals in lower electrical conductivity and higher the sand fraction. SPM concentration and oxygen content Th e correlations of trace metals with the than the summer low-fl ow period. Th e base- land use variables were of the same magni- line sites showed lower pH-values (<7.1 dur- tude when compared to the mud fraction, al- ing the low-fl ow period and <5.6 during the though there were some diff erences between snow-melt period) and considerably lower the two size fractions (Table 19). Particularly electrical conductivity when compared to the general indicators of urbanization (TIA the urban sites. and percentages of urban and dense urban All of the metal concentrations, except land use) were more weakly related with dissolved Pb and Cd, were higher in the metals in the sand fraction. high-fl ow than low-fl ow samples. Th e trace metal concentrations of the suspended particulate matter associated with snow-melt period were higher than the bed sediment metal concentrations, while the low-fl ow concentrations were mostly comparable to the sediment concentrations (Table 21). Cu, Cd and Pb showed higher association with the particulate phase during the snow-melt

Table 18. Correlations of Cu and Zn with sediment components in the sand fraction (entire data, degrees of freedom=18) and partial correlations between sediment components and Cu and Zn for the spatial replicates (sand fraction), controlling for the eﬀ ect of concentrations dif- ferences between sampling sites (df=5).

Entire data Spatial replicates

Log10Cu Log10Zn Log10Pb Log10Cd Log10Cu Log10Zn Organic matter 0.73** 0.75** 0.58* 0.75** 0.71 0.76 Fe 0.61* 0.71** 0.38 0.72** 0.87* 0.84* Mn 0.43 0.49 0.22 0.55* 0.76 0.88* Al 0.37 0.37 0.10 0.27 0.81 0.86* Li 0.54* 0.57* 0.36 0.37

61 Figure 39. Analytical and ﬁ ne-scale spatial variation of acid extractable metals in the sand fraction. Table 19. Correlations between land use variables (entire upstream catchment) and metal concentrations in the sand (63µm-2mm) fraction of the urban sites in the Gräsanoja dataset. Land use percentages are arcsine-transformed and traﬃ c density log10-transformed.

Log10Cusand Log10Znsand Log10Cdsand Log10Pbsand Urban (%) 0.01 -0.25 -0.11 0.05 Dense urban (%) 0.19 -0.15 -0.02 0.19 TIA (%) 0.17 -0.06 -0.01 0.28 Low density residential (%) -0.34 0.05 -0.06 -0.24 High density residential (%) 0.08 -0.03 -0.11 -0.05 Institutional (%) 0.06 -0.08 -0.01 0.22 Commercial & traffi c (%) -0.05 0.39 0.25 0.27 Industrial (%) -0.03 0.11 -0.07 0.08 Traffi c density 0.04 0.28 0.23 0.28 period, while there was no diff erence in the during the low-fl ow period. Cu was the ele- partitioning of Zn between the two sam- ment showing the weakest association with pling campaigns. the particulates. Pb showed very strong affi nity to the sol- Th e sediment metal concentrations ap- id phase, and during the snow-melt period peared to be fairly strongly associated with it was almost completely associated with the the high-fl ow metal concentrations of SPM particulates (Table 21). Cd showed similar and water phase (Table 22). Th e relationship tendency to occur in the particulate phase was relatively strong for SPM metal concen- during the high-fl ow period, although the trations even during the low-fl ow condi- proportion of dissolved Cd was considerable tions.

62 Table 20. Median and range (in parenthesis) of water quality parameters of the entire areal dataset Parameter Low-ﬂ ow period Snow-melt period (high ﬂ ow) pH 7.09 (4.87–8.45) 6.94 (4.17–7.97) Electrical conduc- 424 (52–1460) 331 (55–2151) tivity (µS/cm)

Diss O2 (%) 74 (34–140) 92 (61–120) SPM (mg/l) 16.8 (3.1–74.1) 51.3 (0.6–399)

11.2. Controls on metal commercial areas. concentrations in the Th e total aqueous metal concentrations water phase and SPM were lower in the baseline sites when compared with the urban sites for Cu and Zn Th e diff erences in SPM metal concentrations during high-fl ow conditions (Fig. 41). In between catchments dominated by diff erent the low-fl ow samples no statistically signifi - land use types showed that the major diff er- cant diff erences between the land use cat- ences in metal concentrations were caused egories were observed. High-fl ow Cu and by distinctions between the baseline and ur- Zn also showed the most distinct diff erences ban sites (Fig. 40). With the exception of Pb between diff erent urban land use types. For during the low-fl ow period, the SPM metal these metals, industrial areas showed signifi - concentrations were statistically signifi cantly cantly higher concentrations than the low lower at the baseline sites when compared density residential areas. For Cu, there was with the urban sites. In the high-fl ow sam- also a diff erence between the low density ples there were also signifi cant diff erences residential areas and the high density resi- between the urban land use categories for all dential, institutional and commercial areas. metals with industrial sites showing higher Within the urban sites, the metal concen- metal concentrations than the other two trations of SPM showed substantially higher urban types. Furthermore, Cu was signifi - correlations with the percentage of impervi- cantly lower in the low density compared ous surfaces during the high-fl ow conditions to high density residential, institutional and when compared with low-fl ow conditions Table 21. Median and range (in parenthesis) of acid extractable metal concentrations in water (total concentration in water = particulate + dissolved) and suspended particulates (SPM), in addition to the particulate and dissolved metal concentrations and the proportion of particulate metals in the areal dataset. SPM (µg/g) Total conc. in Particulate Dissolved Particlate water (µg/l) (µg/l) (µg/l) (%) Cu Low-fl ow 106 (7.1–594) 9.1 (1.9– <16.9) 1.5 (0.2–5.9) 7.3 (1.8– >11) <18 (3–43) High-fl ow 237 (21.0–1780) 18.9 (2.3–61.7) 12.4 (0.1–47.0) 9.0 (1.3–27.3) 56 (1–86) Zn Low-fl ow 476 (46.5–4194) 13.6 (3.3–70.9) 7.7 (0.9–61.5) 4.5 (<1.5– >110) 57(7– c.90) High-fl ow 829 (81.5-3141) 69.0 (7.5–479.5) 46.5 (0.4–159) 26.0 (1.9– 320) 58 (2–99) Pb Low-fl ow 24.9 (0.07–213) 0.56 (0.02–2.26) 0.48 (0.002–2.22) 0.06 (<0.02– >1.1) 86 (8–98) High-fl ow 45.6 (14.2–191) 2.3 (0.04–17.1) 2.8 (0.02–17.1) 0.02 (<0.004–0.45) 99 (41–100) Cd Low-fl ow 1.1 (0.1–9.1) 0.04 (0.01–0.19) 0.02 (0.003–0.13) 0.02 (<0.01– >0.22) 45 (<6–71) High-fl ow 0.8 (0.3–4.9) 0.08 (0.02–0.35) 0.04 (0.0004–0.15) 0.03 (0.01–0.26) 98 (37–100)

63 Table 22. Correlations of sedimentary trace metal concentrations with the corresponding metal concentrations in water and SPM (log-transformed concentrations). Correlation: Sedi- Correlation: Sediment–total ment–SPM concentration in water Low-fl ow High-fl ow Low-fl ow High-fl ow Cu 0.62*** 0.71*** - 0.69*** Zn 0.67*** 0.73*** 0.29* 0.63*** Pb 0.18 0.59*** -0.04 0.25 Cd 0.49*** 0.42** 0.22 0.37** (Fig. 42). Th e association of SPM metal water had mostly low correlations with TIA concentrations with TIA was even stronger in the urban samples during both sampling than that for the bed sediment. However, periods (Fig. 42, Appendixes 5 and 6). How- the metal concentrations of the bed sedi- ever, total and dissolved Cu concentrations ment refl ected the land use of the catchment were closely related to the degree of urbani- better than low-fl ow SPM. Th e particulate, zation during the high-fl ow period, and the dissolved and total metal concentrations in correlations were only slightly lower than

Figure 40. Th e inﬂ uence of the dominant land use type of the catchment on SPM metal concentrations in the areal dataset during low- and high-ﬂ ow (snow-melt) periods. Th e box represents the 25th percentile, median and the 75th percentile. Th e bars indicate the minimum and maximum values.

64 Figure 41. Th e infl uence of the dominant land use type of the catchment on total metal concentrations in water in the areal dataset during low- and high-fl ow (snow-melt) periods. Th e box represents the 25th percentile, median and the 75th percentile. Th e bars indicate the minimum and maximum values. those for SPM and bed sediment. Contrary ductivity and SPM metal concentrations. to the other metal concentrations, the dis- During the high-fl ow sampling cam- solved Pb had a negative association with paign the discharge conditions and the areas urbanization in the high-fl ow samples. contributing discharge changed somewhat, In the basefl ow samples SPM metal although the snow melted almost gradu- concentrations decreased with increasing ally during the sampling period. Th e sus- suspended sediment concentrations (Table pended sediment concentrations increased 23). Th is was probably a result of metal di- at the urban sites towards the end of the lution via erosion. Th e metal concentrations period with corresponding increases in pH. in water were, on the other hand, positively Th e total metal concentrations of the water related to SPM refl ecting increasing particu- phase were strongly related to the SPM con- late metal concentration. centration, which shows the importance of During the high-fl ow period SPM metal particulate metals in controlling the varia- concentrations were related to the electrical tions of total metal concentrations. Hence, conductivity of water, which is predomi- increasing suspended matter concentration nantly a refl ection of the diff erences between tends to increase the total amount of metals baseline and urban samples, since baseline in the water. Th e metal concentrations were sites are associated with low electrical con- also strongly related to pH, which is prob-

65 Figure 42. Correlations of trace metals in sediment, SPM and water with TIA and traﬃ c density of the contributing catchment within the urban sites. ably a result of the similar temporal varia- 12. Discussion tions of SPM and pH during the sampling campaign. 12.1. Sediment composition

12.1.1. Th e inﬂ uence of surﬁ cial deposits

Th e stream bottom sediment is a composite of materials derived from a variety of sources and from diff erent parts of the catchment. Th e characteristics of the sediment material of each source and the relative contribution of diff erent sources infl uence the eventual characteristics of sediments deposited on the

Table 23. Correlations of metal concentrations in water (total concentration) and SPM with water quality parameters in low-fl ow and high-fl ow samples. Metal concentrations, SPM and electrical conductivity log-transformed. Low-fl ow period High-fl ow period pH Electrical SPM pH Electrical SPM conductivity conductivity Cu SPM 0.23 0.17 -0.39** 0.06 0.54*** -0.21 Water (µg/l) 0.41** 0.46** 0.38** Zn SPM 0.26 0.34* -0.40** 0.13 0.61*** -0.15 Water (µg/l) -0.12 0.03 0.28 0.54*** 0.52*** 0.55*** Pb SPM -0.03 -0.17 -0.22 0.14 0.43** -0.07 Water (µg/l) -0.13 -0.26 0.36* 0.63*** 0.22 0.93*** Cd SPM 0.07 0.06 -0.48*** 0.02 0.22 -0.25 Water (µg/l) -0.34* -0.21 0.14 0.58*** 0.27 0.66***

66 channel bed. Th ese original sediment com- types of the catchment have a relatively low ponents have undergone various physico- infl uence on the variations of bed sediment chemical and biological processes both in the characteristics when compared with other water phase as well as within the sediment, catchment characteristics such as land use. which complicates the connections between A few patterns of sediment character- sediment and the contributing catchment istics in the Gräsanoja dataset could, how- (Fig 1.). ever, be connected to the surfi cial sediments Th e main sources of the inorganic sedi- of the catchment. In the upper parts of the ment in lowland catchments include erosion catchment the stream fl ows through a small of surface soils by water, channel erosion, coarse silt deposit. Th is is apparently asso- resuspension of channel bed sediments, con- ciated with the strong supply of relatively struction sites, the road network and mate- coarse inorganic material to sediment with rial from point sources (Carter et al. 2003; simultaneous decreases in the proportions Taylor et al. 2008). In most cases erosion of of clay particles and organic matter. In ad- topsoil or channel banks is the dominating dition, the stream has a steep gradient in source of inorganic sediment (Carter et al. the area, which is likely to further weaken 2003; Taylor et al. 2008). Th us, the geology the deposition of fl ocs and aggregates of fi ne and geomorphology of the catchment have mineral and organic sediment. Th e peat de- an impact on the characteristics of the sedi- posits located in the middle reaches of the ment transported (Walling & Moorehead main channel impact upon the organic com- 1987). Th e sediments collected from the ponent of the sediments, possibly in con- Stream Gräsanoja and its tributaries as well junction with the old waste landfi ll of the as the small streams of the areal dataset were area. Water draining from these areas may be relatively fi ne grained. Th e clay fraction ac- rich in dissolved/colloidal organic matter, Fe counted for approximately 20-25% of the and Mn, which enhances the formation and mud fraction (clay and silt). A great major- settling of organo-metallic fl ocs (PCA2) in ity of the bulk sediment belonged to clay, the main channel. silt and sand size fractions (<2 mm), since In the sediments of Lukupuro stream the amount left on the 2 mm sieve was al- clay-% is high and principal components most negligible. Th e fi ne sediment grain size of fl ocs and aggregates (PCA1) as well as is a refl ection of the role of clays as one of Al-minerals (PCA3) also show high load- the dominant surface sediment types in the ings. Th e high amount of flocculated fi ne Helsinki area. In addition, the sediments grained, organic and metal oxide rich mate- were collected from depositional areas of rial is related to the agricultural land use or the streams, rather than transport or erosion gyttja-dominated Quaternary sediments of sites where the sediment is composed prima- the cachment. Erosion of organic rich fi ne rily of sand and gravel were avoided. grained soil material is likely to be eroded Statistical relationships between surfi cial and transported to streams since extensive deposit type and stream sediment compo- fi elds surround the main channel of Luku- nents could not be confi rmed in the areal puro. Furthermore, the input of fi ne grained dataset. In the Gräsanoja dataset variations sediment from the the Stream Lukupuro of proportions of diff erent surfi cial deposit increased the proportion of fi ne sediment types were low, since bedrock and clay de- and other fl occulated material in the lower posits dominate the entire catchment. reaches of the main channel. Th e sediments Consequently, the variations of grain-size of Lukupuro also showed high loadings of distribution were also weak. Th e results of Al-mineral component and the Al-concen- this study indicate that in a geologically rela- trations of the sediments were very high tively homogeneous region, such as the Hel- when compared to Al-concentrations re- sinki Metropolitan region, the rock and soil ported for clay soils of the area (Tarvainen &

67 Teräsvuori 2006). Th is indicates very strong nel (see Nicholas & Walling 1996; Droppo leaching of Al from the sulphide containing et al. 2005) and was infl uenced by in-stream soils of the central parts of Lukupuro catch- processes. In the higher order reaches of the ment (see Ojala & Palmu 2007). Stream Gräsanoja the majority of the sedi- Th e infl uence of surfi cial deposits was ment has thus been transported a long dis- more easily discernible in the Gräsanoja da- tance and altered by fl occulation processes. taset when compared to the areal dataset. Th e infl uence of local input of soil aggre- It may be related to the fairly dense spatial gates was weaker in proportion to the total sampling which enables the detection of sediment volume. Th e sediment may also gradual but relatively small changes in sedi- have been involved in various sedimenta- ment composition. In the areal dataset with tion/resuspension cycles. only one sample per catchment, the relationship between distinct surfi cial deposit types 12.1.3. Sediment transport and sediment composition was more diffi - and deposition cult to detect due to various disturbing factors, such as diff erences in abiotic and biotic Processes of sediment transport and deposi- conditions and anthropogenic eff ects. tion are traditionally regarded as being primarily dependent upon the hydraulic con- 12.1.2. Flocculation and soil aggregates ditions of the stream and the properties of the sediment, such as settling velocity, which Th e sediments showed evidence of fl occula- is dependent on the grain-size, shape and tion of organic matter, metal oxides and fi ne density of the particles (Lick 1986; Bridge grained inorganic particles. In the Gräsanoja 2003). On the scale of an entire drainage dataset the intensity of in-stream fl occula- network, there is a usually a continuous tion appeared to be the dominating control downstream decrease of gradient, and a cor- on sediment composition. In the areal data- responding decrease of bed material grain set it had a substantially lower importance, size (Knighton 1980; Th oms 1987; Webster since the variations of sediment composition 1995; Church 2002). Th ere are no direct appeared to be dominated by the accumula- measurements on the fl ow conditions of the tion of soil aggregates. Th e organic matter Stream Gräsanoja. However, the gradient poor fi ne-grained sediments were indicative decreases downstream accompanied by in- of freshly eroded soil from channel or sur- creasing channel width, which is likely to be face erosion, whereas high signals of fl occu- associated with downstream decreasing fl ow lation suggest that the sediment has strongly velocities despite increasing discharge. Th e altered from the original source sediments spatial distribution of sediment components due to in-stream processes, such as fl occula- in the drainage network suggests that there tion (c.f. Droppo 2001; Taylor et al. 2008). may be some dependence on the longitudi- Th e diff erence between the datasets may nal changes in energy of the fl ow, since the be primarily related to the characteristics grain size distribution demonstrates a slight and sizes of the catchments. Th e strong in- shift towards fi ner grain sizes in the down- fl uence of soil aggregates in the areal dataset stream direction. Th is trend is considered to indicates that, the sediments represent mate- result from progressive downstream sorting rial which has recently eroded from primary and abrasion of particles (Knighton 1980; sources and subsequently deposited. Th e Brigde 2003). When being transported by a location of sampling sites in the headwater fl ow of decreasing velocity, largest and dens- reaches may explain the low fl occulation de- est grains tend to be deposited fi rst owing to cree of the inorganic matter. Th us, the dense their highest settling velocity (Bridge 2003). soil aggregates were deposited on the stream Th erefore, sediment sorting takes place and bed rapidly after reaching the stream chan- downstream reaches with low fl ow velocities

68 are associated with deposition of fi ne grained reaches, below the confl uence of Lukupuro. and organic rich material. Th e increased fl occulation in the lower In addition to downstream changes, hy- reaches of the main channel may be related draulic conditions and consequently grain to the increased transport of organic matter size properties of the sediments show typi- and fi ne grained particles, since the concen- cally strong local variations according to the tration of suspended solids infl uences the local morphology (Rhoads & Cahill 1999). rate of fl occulation/defl occulation (Droppo Depositional zones with slowly fl owing or et al. 1997, 1998). Th e low gradient and quiescent water and fi ne grained sediments the wide and uniform channel morphol- are encountered for example in shallow ogy in the lower reaches of the main chan- water areas near the shores, on pointbars, nel may increase the deposition of fl ocs due downstream from obstacles or in stagnation to decreasing fl ow velocities (c.f. Droppo et zones at confl uences (Shelton & Capel 1994; al. 1997, 1998). In addition, the shores of Wood & Armitage 1997, 1999; Rhoads & the lowermost reaches are mostly vegetated, Cahill 1999). Th e sediment samples of this which enhances sedimentation as the sus- study were mainly collected from such depo- pended material may be eff ectively trapped sitional areas. Th us, local grain size variations by the vegetation (c.f. Ciszewski 1998). are not expected to be considerable, but local fl ow velocities may have some infl uence 12.1.4. Land use impacts on sediment composition. Although the hydraulic environment is Th e results of the areal dataset suggest that theoretically the determining factor behind increasing urban land use increases the or- the settling of sediment, the traditional ap- ganic matter content and decreases the clay proach involves some problems. Since ma- percentage of the depositional sediments. jority of the fi nest sediment particles does High scores of soil aggregate component not settle as discrete particles but as com- were also found to dominate in the forest- posite particles, the fi ne sediment content is ed and low density residential catchments not dependent on the settling velocity of the when compared with more dense urbanized primary particles (Raudkivi 1990; Bridge catchments. In other studies, very controver- 2003). Aggregation increases the actual size sial results have been found concerning the and settling velocity of the particles thus en- grain size distributions of urban stream beds. abling them to be deposited in the sediment Schoonover et al. (2005, see also McBride & (Raudkivi 1990; Droppo & Stone 1994b; Booth 2005; Finkenbine et al. 2000) have Nicholas & Walling 1996; Droppo et al. obtained results similar to those reported 1997). Th us, the formation and deposition here with relatively coarse sediments oc- of fl ocs may cause an increase of fi ne sedi- curring in urbanized areas. Booth and Jack- ment particles and organic matter without son (1997), on the other hand, claim that a decrease of fl ow competence (Nicholas & increased surface runoff increases the ac- Walling 1996; Droppo et al. 1997). Droppo cumulation of fi ne sediment, which is also and Stone (1994b) further state that in ad- supported by similar results from May et al. dition to the energy gradient, chemical and (1997) and Th oms (1987). biological gradients are important, as they Th ree main reasons for the observed land may control the formational processes that use-sediment composition correlation pat- aff ect the nature of fi ne-grained material. terns can be distinguished: 1) the impacts Th us, the grain size variations observed here of soil sealing, 2) the sources of anthropo- are related to the formation and deposition genic sediment material and 3) the changes of fl ocs and aggregates as well as the veloc- in hydraulic processes due to urbanization. ity of the fl ow. Th e component of fl ocs and Soil sealing leads to low availability of fi ne aggregates showed an increase in the lower grained soil material for erosion (see Wol-

69 man 1967), which decreases the proportion 12.2. Connections between of fi ne particles in sediment. In addition, sediment components and metals when open channels are replaced with storm drains the supply of fi ne grained inorganic material by means of channel erosion de- 12.2.1. Relationships in the creases. Th e weakness of erosion is likely sediment of the Stream Gräsanoja to increase the proportion of organic matter, since abundant organic material such as Th e PCA-biplot and correlation matrix of leaf litter is washed down from urban areas metals and sediment composition demon- (Allison et al. 1998; Ruth 2004; Miller & strated the association of metals with dif- Boulton 2005). ferent sediment components. Th e PCA plot Particles washing down from the street showed that high concentrations of Cu, Zn, surfaces include abrasion products of pave- Cd and Pb were recorded in similar sediment and traction sand as well as particles ments. Th e metals appeared to be strongly emitted from tyres, brakes and corrosion of associated with sediments which showed vehicles (Neller 1993; Kupiainen et al. 2003; strong signals of fl occulation or coating of Robertson et al. 2003). Th e road deposited mineral grains: fi ne grained sediments which particles have been shown to be fairly coarse also contained abundant organic matter and grained (Chad et al. 2002; Robertson et al. metal oxides. Th e high specifi c surface area 2003; Lau & Stenstrom 2005; Adachi & and high trace element binding capacity of Tainosho 2005), although hydraulic sorting clay particles, Fe- and Mn-oxides and organ- results in a shift towards fi ner grain sizes dur- ic matter is already widely known (Horowitz ing sediment transport (Stone & Marsalek & Elrick 1987). Th e additional impact of 1996; Droppo et al. 1998; Vaze & Chiew fl occulation on metal adsorption has rarely 2002, 2003; Sutherland 2003). been documented, although Droppo et al. In the highly urbanized catchments wa- (1998) claim that fl occulation alters the ter is conveyed through storm water pipes, chemical and biological behaviour of sedi- where deposition of the larger grains is hin- ments in terms of how it interacts with con- dered. Th us, all sediment grain size fractions taminants. Flocs tend to have a high specifi c are primarily transported to receiving wa- surface area (Chu & Lee 2004; George et al. ter bodies (Graf 1975; Walling & Moore- 2007), but the control of fl ocs on contami- head 1987; Nelson & Booth 2002). When nant adsorption/desorption involves all fl oc stormwater is discharged to open channels constituents (living and non-living organic the coarsest grains are also deposited to the matter, inorganic compounds and water) stream bed (Neller 1993) forming a fairly and may be much more complicated than heterogeneous sediment. pure physical attraction. Th ese constituents High organic matter content of sediments may infl uence interactions with contami- in dense urban catchments is in agreement nants through their functional processes, with high organic matter concentrations de- such as microbial activity, formation of fi - tected in suspended sediments of stormwa- brillar extracellular polymeric material and ter in Finland (Kivikangas 2002; Tarvainen anaerobic/aerobic processes (Droppo et al. et al. 2005). However, the exact reasons for 1997). the coarse grain-size and high organic matter Th e second principal component dem- content are not clear. Th e observed pattern onstrated the negative eff ect of organic poor is a result of many factors, and the sediment fi ne grained sediments on metal concentra- properties are likely to vary between urban tions. Similarly, the basin-scale diff erences of areas depending on local conditions such as metals show almost no relation to the grain- soil type, structure of stormwater drainage size parameters, Al and metal oxides. Th e system and runoff processes. low or negative relationships between metals

70 and clay-% were surprising, since they are metrics. Th e aim of the normalization was contrary to the general geochemical behav- to highlight the anthropogenic signals by iour of trace metals (Förstner & Wittman reducing the variations in grain-size and 1981; Horowitz & Elrick 1987; Borovec mineralogy caused by diff ering depositional 2000). Th e input of clay particles should in- conditions (energy of the environment). crease the potential of the sediment to bind Th e natural metal concentrations increase trace metals, since clay fraction has been re- with the clay content, since the fi nest par- garded as a strong scavenger of trace metals ticles are composed of metal-bearing miner- (Wilber & Hunter 1979; Dong et al. 1984; als, and they have a high capacity to bind Horowitz & Elrick 1987; Jain & Ram 1997; trace metals as coatings (Kersten & Smedes Förstner 2004). A number of authors have 2002). In this study, low and even nega- also claimed amorphous Fe- and Mn-oxides tive correlations between clay-% and metals to be the most important scavengers of trace were observed, which contradicts the natu- metals in sediment (Jenne 1968; Horowitz ral metal-grain-size associations. However, & Elrick 1987). the observed relationships were not merely a Th e weak relationships between trace refl ection of the energy of the environment, metals and grain-size properties were partly but rather the observed relationships were related to the relatively low grain-size vari- dominated by the mixing of sediments of ations of the data. Since only fi ne-grained diff erent origins and grain-size distributions: depositional sites were sampled and the uncontaminated fi ne-grained soil and mate- sediments were sieved before any chemi- rial that originates from urban surfaces. Th e cal analyses were carried out, the grain-size results cannot be interpreted to infer that the variations have an unusually low impact most coarse-grained particles would have on trace metals. In addition, it appears that the highest metal binding capacity. Instead, sediments representing freshly eroded and it can be assumed that the established rela- deposited soil aggregates (indicated by low tionships between metals and grain-size are scores of the second principal component) valid also for the two sediment components did not show high metal concentrations. that dominate the sediments of this study. Hence, a strong supply of clean sediment Th erefore, the normalization method pro- from soil and channel banks dilutes the posed for marine sediments in Finland was concentrations of trace metals transported adopted here. Th e clay-% and organic mat- through storm water. ter content were, however, relatively close to Th e correlations suggest that organic those of the standard sediment, and the nor- matter is the major control on the binding malization did not strongly alter the original of Cu, Zn and Cd (for further discussion metal concentrations. on the role of organic matter, see chapter On the small spatial and temporal scales, 9.2.4.). Pb is less strongly related to TOC the relationships between metals and sedi- compared to other metals. Pb has probably ment components were stronger but less accumulated in the surface soil during the consistent. Th e organic matter appeared to years when leaded gasoline was used. Th ere- be the most important sediment parameter fore the erosion of Pb-rich surface soils at having mostly a positive relationship with parts of the catchment strengthens the rela- the metals. However, the relationship of tionship between Pb and soil aggregates, and TOC with Pb for instance was positive at at the same time weakens the relationship the temporal scale but negative at the spa- between Pb and TOC. tial scale. Th erefore, it seems that the cor- In this study, normalization of metal relations are somewhat arbitrary, and may be concentrations with respect to clay-% and strongly aff ected by variations at a single site. organic matter was conducted prior to Th e only consistent relationship recorded analysing the relationships with land use was the negative relationship between met-

71 als and coarse silt. Hence, the proportion of the same as in the Gräsanoja dataset in that, coarse particles tends to decrease the metal the organic matter is be the single most im- concentrations at the small spatial and tem- portant sediment parameter aff ecting the poral scales. Overall, the results were quite metal concentrations. Th e fi rst principal diffi cult to interpret, and due to the small component showed that the metals were number of replicate sites, the correlations are positively related with organic matter and not very robust. Th erefore, further analysis negatively to grain-size parameters and metal using more sites is needed for more profound oxides. Th is component is comparable to the analysis of the eff ects of sediment composi- second principal component of the Gräsa- tion on the small-scale spatial and temporal noja dataset, which refl ects the low metal variations of metal concentrations. scavenging ability of soil aggregates. Th us, Th e partitioning of metals between dif- due to the dominating control of the soil ferent operational phases gave an indication aggregate component on the sediment com- of the functional relationships between met- position, the metals are negatively related to als and sediment components. Although the the sediment fi nes. correlations of metals with sediment com- Th e second principal component refl ects ponents were relatively similar, they dem- the impact of sediment grain size on metal onstrated very diff erent binding behaviour accumulation. If the organic matter content for operational phases. Th e easily reducible of sediments is equal, fi ne grained sediments phase that represents metals bound to Mn- tend to be stronger metal scavengers com- oxides and reactive Fe-oxides was important pared to coarse grained sediments due to for the binding of Zn and particularly Cd, higher specifi c surface area. In addition, the although the statistical relationships with metal oxide content is usually higher in fi ne metal oxides were low. Furthermore, Zn grained sediments. Th is is also indicated by showed strong affi nity to the reducible phase the positive loadings of Fe and Mn in the (Fe-oxides) and Cu to the organic phase. component. Although the partitioning results mostly do Th e observed correlations in both the not correspond with the statistical relation- Gräsanoja and the areal dataset indicate ships observed, the fi ndings are in accord- that the availability of fi ne material is not ance with most studies on metal specia- the limiting factor for the accumulation of tion in stream and pond sediments (Lee et trace metals in the stream bed sediments. As al. 1997; Liebens 2001; Johannesson et al. concluded by Nicholas and Walling (1996), 2003; Marsalek et al. 2006). However, very the diff erent origins of composite particles contradictory results have also been present- in streams control the eff ective size, shape ed (Charlesworth & Lees 1999a; Sutherland and density of the sediment. Th e results pre- 2000); Zn in particular has been reported to sented here further indicate that the origin be more strongly associated with the organic of the composite particles, particularly the phase than the results of this study would eff ect of in-stream processes including fl oc- suggest (Johannesson et al. 2003; Marsalek culation, has a strong infl uence on the ac- et al. 2006; Charlesworth & Lees 1999a). cumulation of trace metals. Other reasons Accordingly, the binding behaviour of met- for the lack of generally observed relation- als may be slightly site or area specifi c, as sug- ships between grain size properties and met- gested by Charlesworth and Lees (1999a). als may be related to the granulometric ho- mogeneity of the sediments and the sieving 12.2.2. Statistical relationships procedure undertaken in the pre-treatment in the areal dataset of the samples.

In the areal dataset, the factors aﬀ ecting the sediment-metal relationships were roughly

72 12.2.3. Th e role of TOC in determining and alternatively weak bonds between met- the metal concentrations als and organic matter have been suggested as reasons for similar disparities between the TOC has a strong impact on the large scale correlations and binding phases (Flores-Ro- distribution of metals for both datasets as dríguez et al. 1994; Turer et al. 2001). In well as on the fi ne scale spatial and temporal the present study, it is possible that a large variations of metal concentrations. For Cu, proportion of the metals is bound to very the strong correlation with TOC concorded stable organic compounds, such as bitumen well with the high proportion of Cu bound or tire tread, that may not dissolve in the ex- to the organic phase in the Gräsanoja data- tractant of the organic phase. However, it is set. Th erefore it seems likely that the organic not likely that these compounds would exert matter is a strong causal and direct control on such a strong infl uence, since their concen- the Cu distribution in the sediments. Th is is trations in urban air and soil are relatively in agreement with the typical occurrence of low (Fauser et al. 2002). Cu in organic complexes (Jaïry et al. 1999; Organic matter can have indirect posi- Åström & Corin 2000). Th e low percentage tive impacts on the trace metals by enhanc- of Zn bound to the organic phase is, howev- ing their adsorption on other phases (Jenne er very controversial with the observed high 1968; Davis & Leckie 1978; Warren & correlation between Zn and TOC. Similarly, Zimmerman 1994; Nelson et al. 1995), but the strong affi nity of Zn to the reducible the exact causes of the co-eff ects of organic phase does not support the low correlation matter and other phases are not understood. between Zn and the reducible Fe. According to Jenne (1968), this could result Th e correlations between Cu, Zn, Pb and from the control of organic matter on the Cd in the areal dataset as well as their similar reduction and oxidation of Fe-oxides. Th e spatial distributions in the Stream Gräsa- organic matter would in this manner main- noja sediments indicate that the sources and tain the oxides in an amorphous high surface controlling factors are largely the same for area state (Jenne 1968), which favours trace all metals, although the speciation between metal adsorption. According to Warren and diff erent binding phases is very diff erent. It Zimmerman (1994) the indirect impact of appears that, despite their operational sig- organic matter could be related to ‘a change nifi cance, the diff erences in the strengths from repulsive to attractive electrostatic in- of various binding phases do not strongly teraction by the sorbed organic matter or determine the spatial distribution of metals. the formation of oxide-Cu-organic matter Since there are strong statistical relationships bridges’. Th e complexity of the relationships between metals and TOC in both datasets, between the sediment components and met- organic matter appears to refl ect the control- als may be further enhanced by other bio- ling factors behind the spatial distributions logical and physical processes (Jenne 1968; of metals. Organic matter content is prob- Davis & Leckie 1978; Horowitz & Elrick ably a good indicator of properties which 1987; Nelson et al. 1995, 1999; Stecko & aff ect the ability of the sediment to accu- Bendell-Young 2000). mulate metals, such as fl occulation degree of Th e dominant role of TOC regulating sediment. However, based on the speciation the concentrations of Cu, Zn, Pb and Cd study, TOC is not the most important causal might also be due to the same sources of determinant at least for Zn and Cd. metals and organic matter in the study area Possible reasons for the contradiction (see Flores-Rodríguez et al. 1994; Turner between the correlations and Zn specia- 2000; Filgueiras et al. 2004; Fan et al. 2002; tion may be related to the selective extrac- Tsai et al. 2003). Th e association of organic tion scheme used. Incomplete dissolution of matter with the urban pollution has been organic compounds from urban sediments, widely reported, but mostly in relation to

73 sewage and waste inputs (Lin & Chen 1998; 12.2.4. Control of sediment composition Matthai et al. 1998; González et al. 2000; on the partitioning of metals Orescanin et al. 2004; Yau & Gray 2005). However, this study catchment is drained In addition to the organic phase TOC ap- by a separate sewer system so one can expect peared to also increase the Cu and Zn con- the input from sewage sources to be fairly centrations in the pseudoresidual and reduc- small. Nevertheless, diff use runoff from ur- ible phases. Th us, organic matter increased ban areas, especially in districts with high the binding capacity of Fe-oxides oxides as traffi c densities, may also be an important suggested by Jenne (1968) and Warren and source of organic matter (Williamson 1985; Zimmerman (1994). Surprisingly, there was Flores-Rodríguez et al. 1994; Svensson & only a weak correlation between the reduc- Malmqvist 1995; Gromaire-Mertz et al. ible metals and the corresponding matrix 1999; Kivikangas 2002; Ruth 2004). In the component (FeR). Th e pseudoresidual Cu räsanoja dataset TOC appeared to be inde- and Zn were, however, correlated with clay- pendent of land use, but in the areal dataset a %, Li and Al. Th is implies that Zn and Cu linkage between sediment composition and concentration in the mineral lattices are land use was clearly observed. Organic mat- high in clay rich sediments, which is in good ter content increased and clay-% decreased agreement with the higher trace metal con- with increasing intensity of urbanization. tent of the micaceous minerals of the clay Hence, the control of organic matter con- fraction compared to feldspars and quartz tent on metals may be partly explained by that dominate the coarser fractions (Salmin- their common sources. en & Tarvainen 1997). Th e correlations between metals and TOC had an opposite impact on the eas- sediment components, especially organic ily reducible metals compared to the metals matter content, have often been used to as- in the other phases. Organic matter content sess the relative binding affi nities of various and reducible Fe had negative relationships sediment phases for metals (Bubb & Lest- with easily reducible Cu and Pb concentra- er 1994; Tsai et al. 2003; Orescanin et al. tions. Th e negative associations were even 2004). Care should, however, be taken when stronger when the proportions of the easily using the correlation approach to make de- reducible metals were considered. Appar- ductions on the binding modes of metals. ently, Cu and Pb have so high affi nities to Th e issue has been discussed previously by organic matter and Fe-oxides that by com- claiming that strong correlations between peting for the Cu and Pb in solution, or- various sediment parameters make it dif- ganic matter and Fe-oxides diminish the ad- fi cult to distinguish between the eff ects of sorption of these metals to the most mobile organic matter and other sediment compo- phase. And conversely, the affi nity of Cu and nents (Horowitz & Elrick 1987; Horowitz Pb to the easily reducible phase is very weak. et al. 1989; Combest 1991; Flores-Rodrígu- Th erefore, they bind to the easily reducible ez et al. 1994). Th e results of the present phase in signifi cant amounts only in condi- study confi rm that even strong correlations tions where the sediment contains minimal between metals and a matrix component of amounts of competing phases, including the sediment do not provide a reliable esti- organic matter or reducible iron oxyhy- mate of the dominant binding phases of the droxides. Th e role of organic matter (Wei metals, or the potential mobility and bio- & Morrison 1993; Coquery & Welbourn availability of metals. 1995; Morillo et al. 2002) and Fe and Mn oxides (Adriano 2001) in decreasing the mobility and bioavailability of metals has also been reported. Compared to Pb and Cu, Zn and Cd have stronger affi nity to the amor-

74 phous Fe-oxides and Mn-oxides. Th erefore, play negligible enrichment in relation to the the easily reducible Zn and Cd seemed to geochmemical background which represents decrease less with increasing concentrations conditions unimpacted by anthropogenic of the matrix components of the competing eff ects. Th is is slightly surprising, since the phases. baseline sites are located around the sub- It seems that the total amount of MnER, urban zone of the Helsinki Metropolitan FeER and FeR were not the limiting factors region. Even sites surrounded by built-up for the accumulation of metals in the easily areas do not show metal concentrations sub- reducible and reducible phases of the sedi- stantially above the geochemical background ment. In addition to the above mentioned values, which suggests that regional and long reasons, the lack of correlation might be distance transport of airborne pollutants do related to variations in the crystallinity and not markedly increase the natural metal age of the precipitates as well as to the vari- concentrations of the stream sediments. Th e ous types of occurrence of these oxides, for small diff erence may, however, be partially example as coatings on mineral grains or as an artefact caused by diff erences in sediment discrete particles (Jenne 1968; Lee 1975). composition (grain size, organic matter content). In addition, the chemistry of stream 12.3. Sediment quality assessment sediments is a result of complex processes involving both release and retaining of metals in the drainage basin (e.g. Starr et al. 2003), 12.3.1. Metal concentrations which may interfere with the accumulation at the baseline sites of depositional metals in sediments. Pb and Cd concentrations were lower Th e baseline sites of this study represent than the corresponding baseline concentra- catchments that receive only limited direct tions in humus samples. Th ese metals are contaminant inputs from anthropogenic preferentially bound to the humus layer of sources. Th e baseline concentrations of met- soils (Tarvainen et al. 2006) and are not als in the stream sediments were a result of easily carried to waterbodies. Th is may ex- weathering of minerals in the surfi cial sedi- plain the low baseline concentrations in the ments and bedrock, metal release from lit- stream sediments. Th e strong role of sedi- terfall and atmospheric deposition (e.g. Starr ment composition and catchment processes et al. 2003). Here, direct inputs from waste- on the baseline metal concentrations is fur- water, stormwater or other human sources ther supported by considerably higher metal can be assumed to be small at the baseline concentrations reported for organic rich sed- sites. Some of the catchments have been iments in small ponds with non-urbanized previously cultivated, and therefore, minor catchments in Helsinki (Marttila 2007). amounts of metals from fertilizer use can be It is acknowledged that the choice of washed down to the streams in association baseline concentrations is critical concerning with soil particles. Th e concentrations of the results of sediment enrichment (Rubio et metals are, however, even slightly lower than al. 2000). Based on the low metal concentra- the regional average of stream sediments in tions of the baseline sites in this study, the Southern Finland (Table 24). baseline sediments are likely to refl ect the Th e metal concentrations of the base- natural metal concentrations with minor line sites of this study compare well with the additions from atmospheric pollution. Bias metal concentrations of subsoil and pre-in- should not result from diff ering geologies dustrial sediments in the Baltic Sea and lakes either, since the baseline sites are located located in southern Finland (Table 24). Th e within the same geological area with the close correspondence shows that the stream suburban sites. Th us, the sediments fulfi ll sediments of undisturbed catchments dis- many of the criteria proposed for obtaining

75 Table 24. Background concentrations of metals in soils and aquatic environments, and median metal concentrations of various urban soils and sediments (in µg/g). Reference Size Dissolution Zn Cu Pb Cd fraction method Baseline stream sediments <63µm Partial 113 25 13 0.2 of this study (median) dissolution Urban stream sediments of the <63µm Partial 360 108 31 0.6 Gräsanoja dataset (median) dissolution Urban stream sediments of <63µm Partial 356 95 31 0.5 the areal dataset (median) dissolution Pre-industrial sediments Pre-industrial marine sediment Bulk Partial 100- 20-30 (Laajalahti Bay, Gulf of Finland) 2) dissolution 125 Pre-industrial marine sedi- <2mm Partial 86.7 22.2 14.9 0.14 ment (Gulf of Finland) 3) dissolution Pre-industrial lake sediment (Valkea- Bulk Partial c. 100 c. 21 c.10 c. 0.6 Kotinen, Southern Finland) 4) dissolution Pre-industrial lake sediment (Hir- Bulk Partial c. 90 c. 15 c. 10 c. 0.25 vilampi, Southeastern Finland) 4) dissolution Recent background sediments Regional average of or- <2mm Partial c. 130 c. 30 c. 17 0.35- ganic stream sediments1) dissolution 0.4 Median of clay soils (subsoil) in <2mm Partial 108 42 17 <0.1 the areas surrounding the met- dissolution ropolitan region of Helsinki 5) Urban soils and sediments (medians) Urban baseline concentrations of <2mm Partial 63 9 49 0.4 humus (in the area surrounding the dissolution metropolitan area of Helsinki) 5) Concentrations of organic top- <1mm Partial 82 44 51 0.3 soil (parks in Helsinki) 6) dissolution Urban baseline concentrations of <1mm Partial 48 16 59 0.2 humus (natural soils in Helsinki) 6) dissolution Street dust (Turku, Finland) 7) <43µm Total dis- 222 101 38 0.5 solution 43–100 Total dis- 172 67 25 0.3 µm solution 1) Lahermo et al. 1996, 2) Vaalgamaa 2004, 3) Vallius 1999, 4) Mannio 2001, 5) Tarvainen & Teräsvuori 2006, 6) Helsingin ympäristötilasto 2009, 7) Peltola & Wikström 2006 representative background values (Förstner 12.3.2. Metals at the urban sites & Müller 1981). However, a more comprehensive picture of the natural variation of Zn and Cu concentrations in both datasets baseline concentrations would be obtained compare well with concentrations reported with a higher number of baseline sites. for urban sites in the United States (Rice 1999). However, metal concentrations of urban stream sediments exhibit very high variations between studies (Rice 1999;

76 Sutherland 2000; Webster et al. 2000; Mel- during the 1980’s until the input from gaso- lor 2001; Andrews & Sutherland 2004; line sources practically reached zero in 1994, De Carlo et al. 2005), probably depending when no leaded gasoline was for sale in Fin- upon the size of the drainage area, geology of land (Pietarila et al. 2001; Environmental the area, the intensity of urban land use and statistics 2005). However, the Pb storage of properties of the collected sediments. Th e the surfi cial layers of soils responds slowly to Zn and Cu concentrations of the Helsinki the reduction of emissions (Van Metre et al. region seem to be lower than those reported 1998). Th e high concentrations reported in for streams of similar size in O’ahu, Hawai’i other studies refl ect the long period in the (De Carlo et al. 2005). However, larger 20th century when leaded gasoline was a ma- streams with forested upstream catchments jor source of Pb. Th e eff ects are still widely show lower Cu and Zn concentrations in evident in stream sediments and road depos- their urban lower courses compared to the ited particles through the erosion of road- concentrations obtained here (Webster et al. side soils (Sutherland 2000, 2003; DeCarlo 2000; Andrews & Sutherland 2004). & Anthony 2002; Brown et al. 2008). Th e Enrichment factors off er a possibility to high Pb concentrations observed at indi- compare metal contamination with other ar- vidual sites in this study are also a result of eas that may be dominated by diff erent basic the mobilization of previously contaminated geology. Zn enrichment of stream sediments roadside soils. Th e catchments analysed in in the Helsinki region is comparable to other this study were dominated by single-family urban areas, where the median enrichment houses until 1960’s, when the construction factors range between 1.5 and 5 (Sutherland of suburbs started outside the city centre 2000; Andrews & Sutherland 2004; De (Schulman 1990).Th e relatively low age of Carlo et al. 2005). Meanwhile, Cu enrich- the suburban districts and the short dura- ment is relatively high in the Helsinki region tion of Pb accumulation are the most prob- when compared with the two catchments in able reason for the generally low Pb concen- Hawaii, where low enrichment factors of Cu trations in Helsinki. (1.5-2) have been reported for urban areas. Th e Pb concentrations have almost In contrast to Cu and Zn, Pb concentra- reached the background levels at most of tions from the studied streams in Helsinki the urban sites. Results on urban marine region were low. Almost 75% of the sites in sediments in Helsinki have also shown that both datasets are below the minimum val- Pb concentrations almost reached the back- ues reported for many urban and suburban ground level in the 1990’s (Tikkanen et al. catchments (Wei & Morrison 1993; Suther- 1997). However, the concentrations of Pb land 2000; Webster et al. 2000; De Carlo & in organic stream bed sediments have, in Anthony 2002; De Carlo et al. 2005). Me- fact, increased from the year 1990 to 2000 dian values of Pb are usually at least twice in Southern Finland (Tenhola & Tarvainen as high as reported in this study. Th e diff er- 2008). Th erefore, it must be acknowledged ences cannot be accounted for by diff erent that other Pb sources exist in the urban envi- backround concentrations, since the enrich- ronment, including atmospheric deposition, ment factors obtained in this study were also car tires and paints (Good 1993; Davis et al. very low. As much as 10 times higher en- 2001; Hjortenkrans et al. 2007). richment has been detected in other studies Cd concentrations of the small streams (Sutherland 2000; Andrews & Sutherland in Helsinki region were well below those re- 2004; De Carlo et al. 2005). ported in previous studies (Rice 1999; Suth- Th e low concentrations of Pb in the Hel- erland 2000; De Carlo et al. 2005). In most sinki region demonstrate the positive eff ect studies Cd concentrations of urban stream of the use of unleaded gasoline and paint. sediments are around 0.9 mg/kg (Rice 1999; Th e atmospheric Pb input declined rapidly Sutherland 2000; De Carlo et al. 2005). As a

77 comparison, the median Cd concentrations sediment quality guidelines have been pro- obtained here were 0.6 in the Gräsanoja da- posed by the Finnish authorities or the EU. taset and 0.5 in the areal dataset. However, Th erefore, sediment metal concentrations based on the enrichment factors, the anthro- were compared with the Finnish soil screen- pogenic input of Cd to the aquatic environ- ing values and the quality criteria proposed ment is almost as high as the input of Zn. for dredged materials in Finland. Th ese Th e results demonstrate that the an- guidelines are not intended for contamina- thropogenic pollution exerts a widespread tion assessment of stream sediments (Degree impact on the Zn and Cu concentrations 214/2007), and therefore they can only be of sediment in the suburban stream reaches. used as a preliminary indication of potential Th e sediments are mainly moderately pol- risks associated with the sediments. luted with respect to Cu and Zn (EF 2–5) Cd and Pb can be considered as the most (Fig. 15), but some sites show signifi cant or dangerous metals of those determined in this even very strong pollution particularly with study. Th ey have been included in the list of respect to Cu. In contrasts, Pb shows very priority substances in EU (Water framework low pollution signals. In the urban baseline directive, EC 2000). Th eir concentrations study of Tarvainen (2006) Pb and Cd were were, however, low when compared with the found to refl ect the atmospheric deposi- soil screening values and dredged sediment tion and other anthropogenic input better quality guidelines, which indicates a low risk than Zn and Cu. Th is shows that in urban of ecological eff ects. Cu and Zn concentra- areas Pb and Cd were strongly aff ected by tions in the studied sediments were consid- atmospheric transport and deposition, while erably higher compared to Cd and Pb. Th e Cu and Zn are transported to the aquatic concentrations were widely above the inter- system directly from the contamination vention criteria for dredged material, and sources (traffi c areas, buildings) by storm- the pollution level was exceeded at numer- water. Comparison to street sediment metal ous sites. Th e soil screening values suggest, concentrations further reveals that, traffi c is however, that the sediments are much less not the only substantial source of Zn in the contaminated with Cu than Zn. Zn con- urban environment. In addition to the re- centrations were above the guideline value gional atmospheric deposition, Cd appears at more than 75% of the sites. Th e results to have direct sources in the urban environ- suggest that ecological eff ects are probable to ment, since the Cd concentrations are higher occur due to high concentrations of Zn and in urban stream sediments when compared possibly also Cu. Th e metal concentrations with the baseline humus samples. in this study were measured for the mud Sediment deposits provide habitat for fraction (<63µm), while the soil screening many benthic organisms, which are thus values and dredged material quality guide- in direct contact with the interstitial and lines are reported for the fraction <2mm. sediment-bound metals. Many organisms Th erefore, the proportion of sites where can accumulate metals either by adsorption metal concentrations exceed the criteria lev- from the interstitial or overlying water to the els may be somewhat overestimated. body wall or respiratory surfaces, or from Although sediment quality guidelines the solid phase through ingestion of the (SQGs) have a central role in many sediment sediment (Tessier & Cambell 1987; Förstner investigations, their use is dispute througout 1989; Wang et al. 1999). Sediment quality the scientifi c community (Apitz & Power guidelines have been developed to be able to 2002). Th ey have been criticized of lack of identify sites where adverse eff ects on ben- predictive ability concerning the biological thic organisms may occur based on sediment eff ects, particularly of long-term exposure metal concentrations (Canadian Council of to the contaminant (Chapman & Mann Ministers of the Environment 1999). No 1999; Chapman et al. 1999; O’Connor &

78 Paul 2000; Mowat & Bundy 2001; Burton In this study the most mobile and bio- 2002). However, despite the weaknesses re- available phase was represented by the easily lated to the use of SQG, they off er a prelim- reducible metal concentrations, which have inary indication of where biological eff ects been found to be the most important factor might be expected (Roach 2005). Th e results concerning the bioaccumulation of metals in demonstrated that the anthropogenic pollu- the bivalve mollusc Macoma balthica (com- tion exerted a widespread impact on the Zn mon name the Baltic macoma) (Th omas & and Cu concentrations of sediment in the Bendell-Young 1998). Th e results demon- suburban stream reaches. Th e concentra- strated that a considerable proportion of the tions were so high that they may even have total Zn and particularly Cd may possibly toxicological eff ects on stream biota. Mean- be taken up by benthic organisms. However, while, despite the dangerous nature of Pb due to low acid extractable concentration of and Cd, their concentrations were not high Cd the concentration of bioavailable Cd was enough to cause adverse biological impacts also relatively low. Pb and Cu were highly on the streams of the Helsinki region. unavailable to biota, since the proportion of easily reducible phase constituted a low 12.3.3. Possible bioavailability percentage of the acid extractable concentra- and mobilization of metals tions. Th e enrichment factors and comparison to quality guidelines proposed for soil Sediment is considered polluted, when the and dredged material suggest that Cu and metal concentration is high enough to cause Zn may pose the highest risk for environ- harmful eff ects on biota (Burton 2002). Th e ment. When considering the possible bio- concentrations obtained here by partial dis- availablity of these metals, it is clear that Zn solution do not, however, adequately refl ect may be more available to biota, and can thus the potential impacts of the metals to the be of higher concern from the ecological biota, since part of the metals are bound to point of view. relatively stable fractions of the sediment Th e sediment bound metals may further (Mowat & Bundy 2001). Only a small part cause adverse eff ects through mobilization of the total metal concentrations, the bio- of metals. Th e increase of metal concentra- available fraction, is taken up by sediment- tions in the interstitial water can pose im- dwelling organisms (Mayer et al. 1996; pacts on the overlying water quality and the Birch et al. 2001). According to Mayer et al. sediment-dwelling biota (Stumm & Morgan (1996) only 1-10% of the total Cu in sedi- 1996). Ecological impacts may also extend ments was bioavailable. Although a thor- to lower reaches as a consequence of down- ough assessment of biological eff ects should stream movement of contaminated water. include methods that rely on direct toxic- Major controls on the mobilization of ity measurements, in situ studies of metal metals to the interstitial water in (sub)oxic concentrations in biota or changes in com- sediments are changes in the metal binding munity structure or a combination of these phases (Whiteley & Pearce 2003; Ouddane methods (Ankley et al. 1994; Chapman et et al. 2004; Tao et al. 2005). Here, Zn and al. 1999; Ingersoll et al. 2001), an estimate Cd were most strongly bound to the Fe- of the biological reactivity of the sediment and Mn-oxides that are particularly sensi- can be obtained using sediment chemistry tive to changes in redox conditions (Jenne (Tessier et al. 1979; Luoma 1989; Bryan & 1968; Salomons & Förstner 1984; Stumm Langston 1992). Particularly metals bound & Morgan 1996). Small changes in pH and to the most labile phases of sediment corre- redox-conditions can lead to reduction and spond with the metals in the biota (Tessier & dissolution of the Mn-oxides and reactive Cambell 1987; Luoma 1989; Pempkowiak Fe. As a consequence, metals bound to the et al. 1999). easily reducible phase, particularly a high

79 proportion of Cd, could be released to in- are probably relatively harmless to the biota. terstitial water and migrate to the overlying Th is would suggest that the anthropogenic water. Th e reducible phase constitutes com- inputs of metals are more of a concern in the pounds which require lower redox-potential smaller headwater streams when compared and pH for reductive dissolution. Th erefore, with larger streams, where the abundance fairly strong changes in the environmental of fl occulated fi ne grained and organic rich conditions are needed in order to mobilize sediment immobilizes a large fraction of the signifi cant amounts of Zn from the solid metals. Previous studies have also demon- phase. Th e strong association of Cd and Zn strated the occurrence of phase changes dur- with the metal oxides suggests that the stable ing transport from source to deposit in the redox conditions of the streams are essential urban environment (Flores-Rodriguez et al. to prevent the mobilization of these metals. 1994; Charlesworth & Lees 1999a). Flores- According to Charlesworth and Lees (1999a) Rodriguez et al. (1994) reported a similar environmental conditions are continuously downstream shift towards more stable forms changing in urban waterbodies, which in- as observed in this study. creases the potential for metal mobilization. However, fairly high (6.4-7.9) and constant 12.3.4. Hazard assessment values of pH have been recorded in urban streams in Helsinki (Ruth 2004). Regard- Th e assessment of sediment quality is often less of the chemistry of the overlying water, the basis for further management strategies changes of redox conditions can also result or actions. Th is sediment quality evaluation from early diagenesis of sediments (Salo- does often take the form of a risk assessment, mons & Förstner 1984). which aims to defi ne the probability of vari- Organic matter had the strongest im- ous impacts of sediment contamination pact on the speciation of Cu. Th us, the slow (Apitz & Power 2002). Th e fi rst step of qual- microbial decomposition of organic matter ity or risk assessment is usually to compare may also result in the release of considerable the sediment chemistry with SQGs (Chap- Cu storage from sediment to the intersti- man et al. 1999; Apitz & Power 2002). De- tial water (Ouddane et al. 2004; Audry et spite the drawbacks of the use of SQGs, the al. 2006). Dissolved organic matter is also information they provide can be used as a produced in association with organic matter screening tool to prioritize the sites that most destruction, which may lead to complexa- urgently need further analysis (Chapman et tion of the released metals and thereby aff ect al. 1999; Canadian Council of Ministers their partitioning between the solid and dis- of the Environment 1999; Apitz & Power solved phases (Förstner 1989; Ouddane et 2002; Förstner & Heise 2006). In this study, al. 2004; Audry et al. 2006). the dredged material quality guidelines were Th e spatial distribution of easily reduc- the basis for identifying the sites that may ible metals suggests that metals are more be of concern. In the Gräsanoja dataset, the mobile and available to the biota in the possibility of a hazard in the fi ne grained headwater reaches. In particular, Cu and Pb sediments is fairly common in the suburban showed high concentrations in the smaller areas, especially in the high density residen- tributaries and in the upper reaches of the tial, commercial and industrial areas. In the main channel, while spatial variations of Zn agricultural, forested or low density residen- ad Cd were considerably weaker. Th e ob- tial areas the hazard is classifi ed as non-exist- served pattern strongly refl ects the immobi- ent or weak. Th us, the potential for ecologi- lizing eff ect of organic matter and iron oxides cal eff ects at the low density residential areas on Cu and Pb. As a consequence, the fairly is probably low, even though the sediments high acid extractable metal concentrations cannot be regarded to represent natural con- in the lower reaches of the main channel ditions based on the mainly moderate en-

80 richment over baseline. his widely cited article “Th e importance of Th ese results may act as an initial esti- imperviousness” that urban water pollu- mate of the locations most vulnerable to tion is directly related to the percentage of contamination induced biological eff ects. impervious areas in the contributing catch- However, it is acknowledged that the sediment. May et al. (1997) also belong to the ments represent only the depositional sites few who have found a relationship between that constitute a small proportion of the metals and increasing urban land use and total sediment storage of studied streams. imperviousness. Th e results of the areal data- Th us, no actual estimate of the stream-scale set further support the view that metal con- biological eff ects can be made. Sediment centrations, especially Cu, Zn and Pb, are samples representative of the diff erent sedi- dependent upon the proportions of dense ment types need to be collected in order to urban land use and impervious surfaces. make an accurate assessment of sediment However, while the metal concentrations quality (Ladd et al. 1998). In addition, the of the non-urban and weakly urban sites framework for assessing sediment quality is were consistently low, the more urbanized usually a tiered approach, where by diff er- sites were associated with a high variability ent chemical and biological analyses of sedi- of metal concentrations. In fact, the increas- ment, interstitial water and biota are com- ing variation of metal concentrations with bined to evaluate the potential for adverse increasing TIA was the most striking feature biological eff ects (Mowat & Bundy 2001; of the metal–TIA relationships. Hence, the Apitz & Power 2002; Crane 2003; Förstner results demonstrate that within the urban & Heise 2006). sites high metal concentrations do not result directly from a high degree of urbanization. 12.4. Land use impacts Variations between sites may be strongly af- fected by point sources and diff erences in the spatial proximity of the stream to contami- 12.4.1. Intensity of urbanization nant sources. Th e weakness of correlation between the population density and metal Th e association of metals with urban pol- concentrations in the residential catchments lution is already well known. Increasing further shows that contamination is not di- sediment metal concentrations have been rectly dependent on the degree of urbaniza- reported for streams fl owing through urban tion of the catchment. areas (Dong et al. 1984; Sutherland 2000; Th e usefulness of the observed metal– Andrews & Sutherland 2004). Furthermore, land use relationships for applications, such smaller urban catchments tend to be associ- as land use planning or environmental man- ated with higher sediment metal concentra- agement, are dependent on the selection of tions when compared with agricultural and proper land use metrics. Th e land use indi- forested catchments (Rice 1999; De Carlo et cator should refl ect the occurrence of vari- al. 2005). Th e results of this study are paral- ous pollution sources, while also being easily lel to those reported before. Metal concen- measurable and related to diff erent land use trations in the urban catchments of the areal policies. Th e results of this study confi rm the dataset were statistically higher than at the widely accepted view that imperviousness is unbuilt catchments. Moreover, metal con- a very useful indicator of land use impact on centrations were lower at the forested and aquatic systems (Schueler 1994; May et al. agricultural sites when compared with most 1997; Wang et al. 2001). TIA and the per- of the urban sites in the Gräsanoja dataset. centage of dense urban areas correlated with However, the impact of increasing ur- the metals most consistently out of all the banization has rarely been shown statisti- land use metrics within the urban sites. TIA cally, although Schueler (1994) claimed in is, however, likely to refl ect the variations of

81 metal concentrations relatively well also in but biological eff ects due to high concentra- weakly urbanized catchments (see Fig. 43). tions of Cu and Zn are possible at nearly all Impervious surfaces are feasible predictors sites. When TIA exceeds 25–30%, the range of aquatic pollution loads, since they accu- of concentrations is very high and biologi- mulate pollutants from diff erent sources and cal eff ects are probable at numerous sites due eff ectively deliver these pollutants to water- to very high concentrations of Cu and Zn. sheds during storm events (Schueler 1994). Th erefore, the TIA thresholds of c. 10 and Th e impervious surfaces are not necessarily 25% can be used as general guidelines for the original sources of pollutants, but TIA the estimation of the urbanization impacts is an index that integrates a variety of hy- upon stream sediments, although the metal drological changes induced by land devel- concentrations do not appear to increase in opment (Schueler 1994; Booth & Jackson a stepwise manner. 1997; Zandbergen 1998). Although the use of TIA is tempting, the calculation may be 12.4.2. Transport areas and rooftops expensive (May et al. 1997). However, the recent development of GIS based techniques Impervious surfaces composed of transport for automatic classifi cation of remote sens- areas and rooftops were both related to the ing data enables fast determination of the metal concentrations in the areal dataset. percentage impervious surfaces from over However, Zn and Cd showed considerably extensive areas (Carlson & Arthur 2000; higher correlations with the percentage of Elgy 2001). rooftops. In the Gräsanoja dataset correla- Walsh (2000) claims that TIA alone is not tions were weak for both components. Since an appropriate indicator of aquatic impacts, the diff erences between the two impervi- since the variations in hydrological connec- ous surface types were relatively small, both tion have to be taken into account. Connec- rooftops and transport areas appear to be tivity is possibly even the dominant factor substantial metal sources. controlling water pollutants in urban areas Results of most stormwater studies and (Hatt et al. 2004). EIA is a measure which loading calculations infer that transport ar- accounts for the variations in the connectiv- eas are the leading source of at least Pb and ity of the impervious surfaces to watersheds. Cu, although buildings may also contribute However, the estimated percentages of EIA substantially to Cu loads (Bannerman et al. did not appear to be more closely related to 1993; Bergbäck et al. 2001; Davis et al. 2001; metal concentrations in this study. Gray & Becker 2002; Sörme & Lagerqvist According to a classifi cation of headwater 2002; Van Metre & Mahler 2003). Despite urban streams (see Schueler 1994), streams the reduction of Pb loads due to unleaded with TIA below 10% show no degradation, gasoline, traffi c is still the most important Pb and water quality tends to be good. When source in the urban environment according TIA is between 10 and 25%, some degrada- to Pakkanen et al. (2001). Pb is contained in tion is usually apparent, and water quality brake linings and tires, although their con- tends to be fair. When TIA exceeds 25%, centrations have decreased substantially over streams are in poor condition with fair-poor the last years (Hjortenkrans et al. 2007). In water quality, low biodiversity and highly addition, Pb may be derived from histori- unstable channels. Th ese thresholds cor- cal emissions through erosion of previously respond well with the results of this study contaminated surface soil close to transport concerning the metal concentrations of sedi- areas (c.f., Bergbäck et al. 2001; Sutherland ments. Zn, Cu and Cd concentrations were & Tolosa 2001; Jonsson et al. 2002; De Car- very low at the baseline sites with TIA<8%. lo et al. 2004). Th e statistical relationships When TIA is between 8 and 25–30%, metal obtained in this study for stream sediments concentrations remain mostly relatively low, do not support the commonly reported

82 dominating role of transport areas as sources Th e analyses based on correlations with of Cu and Pb. However, the transport areas metal concentrations do not provide direct showed stronger relationships with Cu and estimates of the actual contributions of Pb than with Zn and Cd, which is in accord- transport areas and rooftops to metal load- ance with previous studies. ings. In addition, the metal concentrations Cd and Zn are, according to most load- are not solely determined by the areal covering studies, derived from both transport age of the two impervious surface types or areas and buildings. Th e results of this the traffi c amounts. Th e control of rooftops study suggest, however, that the role of the on metals is complicated by the variety of rooftops is more important than that of the diff erent materials in use. Th e fact that age transport areas. It is worth noting that the of the buildings varies further enhances the results on the relative contribution of trans- diversity of roof materials and possible metal port areas and buildings in stormwater metal loads. In many studies the metal sources are loads show considerable variation depending simply separated into transport areas and on the type of land use and building mate- rooftops, although siding materials have also rials (Bannerman et al. 1993; Bergbäck et been shown to be potential metal sources al. 2001; Davis et al. 2001; Gray & Becker (Davis et al. 2001). Moreover, the metal 2002; Sörme & Lagerqvist 2002; Van Metre loads of traffi c are additionally determined & Mahler 2003). by the driving habits, distribution of traffi c In stream sediment studies traffi c sources lights and the road conditions (Brinkmann have often been considered the most im- 1985; Hjortenkrans 2008). portant control on trace metal concentrations (Dong et al. 1984; Callender & Rice 12.4.3. Impacts of diff erent 2000; Sutherland 2000). Th e assumed im- urban and use types portance of transport areas is supported by the fact that roads, yards and parking lots Metal concentrations increased with increas- account for the majority (50-80%) of the ing proportions of dense urban land. In- total impervious surfaces in the study area. creasing proportion of low density residen- Moreover, transport areas are usually better tial areas on the other hand resulted in lower connected to the storm drain system when metal concentrations relative to dense urban compared with rooftops particularly in resi- land use. However, the individual dense ur- dential areas (Schueler 1994). However, the ban land use types (high density residential, results of this study strongly indicate that institutional, commercial and industrial) both traffi c and buildings are major sources showed weaker relationships with the met- of metals. Th is suggests that loading calcula- als than the combined group of dense urban tions might overestimate the importance of land, and no consistent diff erences could transport areas. be established between these land use types Th e relationship between metals and with regard to correlations with metals. Th e traffi c density of the catchment was also diff erence between the land use types was studied here, because metal loads associated also refl ected in the median metal concen- with transport areas have been claimed to be trations between diff erent catchment cat- largely controlled by the amount of traffi c egories, since catchments dominated by (Rose et al. 2001; Robertson et al. 2003). low density residential areas exhibited lower Th e correlation of traffi c density with trace metal concentrations when compared with metals was, however, equal to that of the other urban catchments. However, statisti- impervious areas composed of transport ar- cally signifi cant diff erences were recorded eas. Th us, the information concerning the only for Cu and Pb between low density amount of traffi c did not increase the corre- residential catchments and the group of high spondende between metals and traffi c areas. density residential, commercial and institu-

83 tional catchments. impact of dense urban land uses can over- Th e fairly high metal concentrations of shadow the independent impact of low den- dense urban areas observed in this study are sity residential areas. Th e spatial distribution related to higher traffi c volumes and densities of metal concentrations in the Gräsanoja of buildings and roads when compared with dataset, and the diff erences between catch- low density residential areas (c.f. Karouna- ments dominated by unbuilt areas and low Renier & Sparling 2001). Th e dominating density residential areas in the areal dataset building materials may also vary between (Fig. 33) reveal the independent positive im- diff erent land use types. For example, steel pact that low density residential areas have roofs are the dominant roof type in Finland, on metal concentrations. but brick roofs are most common in residen- Th e results of the present study suggest tial buildings (Kytölä 2008). However, the that high density urban areas exert the high- metal loads related to diff erent construction est contamination eff ect on the sediments. materials have not been determined unam- Although this fi nding is in line with the biguously, and further research on this sub- general understanding of increasing metal ject is needed. concentrations with increasing urbaniza- Catchments dominated by industrial tion intensity, it has rarely been statistically land use showed the highest median metal shown for stream sediments. Quantitative concentrations, but the variation within studies on the infl uence of diff erent urban the group was very strong, and no statisti- land use types on trace metal contamination cally signifi cant diff erence with other land of stream or stormwater pond sediments are use groups was detected. However, many of rare (although see Parker 2000; Karouna- the highest metal concentrations occurred Renier & Sparling 2001; Liebens 2001; at industrial catchments. Th is corresponds Andrews & Sutherland 2004). Th ese studies well with studies that report highest metal show that the evidence on the diff erences be- concentrations in stream sediments or soils tween diff erent urban land use types has not in areas that are presently or have been for- been very strong and consistent. Although merly industrialized (Mellor & Bevan 1999; highest metal concentrations have been re- Mellor 2001). In addition to high diff use ported for commercial areas in stormwater pollution levels, the elevated metal concen- pond and stream sediments (Liebens 2001; trations may be due to point sources of met- Andrews & Sutherland 2004), other studies als from industrial activity, outside storage have failed to show such diff erences (Parker or traffi c. Th e strongly varying metal con- et al. 2000; Karouna-Renier & Sparling centrations in industrial areas emphasize the 2001). Although the dense urban land use need for site-specifi c investigations as the types diff ered from the low density residen- foundations for decisions concerning storm- tial areas in this study, the lack of clear dif- water management and treatment. ferences between the dense urban land use Within the urban sites low density resi- types further demonstrates the diffi culty of dential areas were negatively related to metal discriminating the impacts of diff erent land concentrations. A similar result has been use types. Th e diversity of diff erent results presented by Wilber and Hunter (1977), shows that point sources, local circumstanc- who found a decrease of stormwater metal es and diff erences in human activity (such as concentrations with increasing residential street cleansing operations) greatly aff ect the land use. Th ese results are explained by the relationships between sediment metal con- fact that the land use percentages are not centrations and land use. Correspondingly, independent variables. Th e increase of one Baker (2003) has stated that also the corre- land use type percentage leads to a decrease lations between land use and water chemis- in the percentages of one or more other land try are often site-specifi c. Th e prediction of use classes (King et al. 2005). Th erefore, the highest metal concentrations based on land

84 use information is therefore diffi cult among bypasses the buff erzone. Th e positive corre- relatively densely built catchments. lations between metals and TIA of the riparian zone were not statistically signifi cant. 12.4.4. Spatial arrangement Th erefore, the results did not confi rm the of urban areas importance of the riparian zone, but the results do suggest that the riparian areas may Th e land use of the entire upstream catch- impact upon the transport of metals. Th ere- ment appeared to be related to the sediment fore, a more detailed study on the linkage metal concentrations in the areal dataset. between the land use of the buff er zone and On the other hand, land use of the immedi- the metal contamination should be con- ate upstream catchment correlated relatively ducted in future. weakly with sediment metal concentrations Th e impact of the spatial distribution in both datasets, which shows that the po- of land use on stream sediment chemistry tential metal loads entering the stream close has not been studied previously. However, to the sampling site were not particularly Comeleo et al. (1996) found that the met- well refl ected in the sediment chemistry. al levels of estuarine sediments were best Th ese results show that metal input from predicted by local land use (less than 10 diff erent land use classes has impacts on the km from the sampling site). Also for water stream sediment metal concentrations rela- quality there exist several scale studies but tively far downstream. Th is results from the it remains unclear whether the land use of long range transport of dissolved and par- the riparian or local zone exerts a greater im- ticulate bound metals. Th e result does not pact on water quality than land use of the indicate exactly, how far the most obvious entire catchment (Johnson et al. 1997; Sliva impact is detectable. Th e most distant areas & Williams 2001; Sponseller et al. 2001; are, however, likely to exert a lower infl uence Baker 2003). In this study the local upon the sediment when compared with areas stream catchment was delineated using the located closer to the sampling site, since part hydrological pathways of sediment and pol- of the metals are stored in the stream bed as lutants, which is likely to increase the corre- sediment is transported downstream (Miller spondence between local land use and met- 1997). als compared to previous studies. However, Th e local riparian zone has an equally the results suggest that the entire upstream strong relationship with the metal concen- catchment is more important than the lo- trations compared to the immediate up- cal zone. In current literature catchment stream catchment in the Gräsanoja dataset. scale approach to sediment management Th e riparian zone fi lters sediment and associ- is also strongly emphasized (Owens 2008; ated metals when a shallow water layer fl ows White & Apitz 2008). Th e stream network slowly through the vegetated buff er zone and adjacent hillslopes function as an open (Barling & Moore 1994; Schueler 2000). system, where major changes in one part Th erefore, the high percentage of impervi- impacts upon other parts (Owens 2008). ous surfaces in the riparian zone decreases Th e present results confi rm the importance the capability of the buff er zone to remove of the entire upstream catchment when the metals and also increases direct metal loads catchment size is up to a few square kilome- discharging to the stream. Th e effi ciency of tres. Th roughout the land use metrics corre- riparian zone in decreasing metal loads from lations with land use were higher in the areal stormwater has been questioned by Schueler dataset when compared with the Gräsanoja (2000). He claims that in urban areas 90% dataset, which shows that, the fairly homo- of the surface runoff is converted to chan- geneous land use of the small catchments nels before it reaches the buff er zone. Con- controls metal concentrations more strongly sequently, a high proportion of the runoff than the more heterogeneous larger catch-

85 ment. With increasing catchment size the treatment plant the adverse eff ects of an old variety of diff erent land use types increases, landfi ll can be eff ectively eliminated and el- and it becomes more diffi cult to distinguish evated metal concentrations do not occur in between the infl uences of urban and non-ur- bed sediments. Th e results of the sediment ban land use or individual land use types. assessment do not, however, rule out the possibility of momentary peaks of dissolved 12.4.5. Point sources trace metals. Th e elevated Cu and Zn concentrations Concentrations of individual metals were el- at the estuary (site 39) are probably due to evated at some sites, which may indicate the the higher pH of the water at the estuary infl uence of point sources or some other site- (7.9) when compared with freshwater sites specifi c factors. Such sites were at least those (pH 7.3–7.7). According to Förstner and classifi ed as outliers based on high positive Wittman (1981) high pH of the environ- residuals (when the metals were modelled ment increases the adsorption of metals. Th e using land use and sediment composition as increased content of organic matter, silt and explanatory variables). Th ese sites appeared clay also suggest increased fl occulation and to be impacted by other sources than merely settling in the saline environment (Webster the diff use urban pollution, which is sup- 1995; Birch et al. 2000; Johannesson et al. posed to determine the metal concentrations 2003). High ionic strength and Cl-concen- of the bulk of the study sites. At least one tration of the water inhibit the Cd adsorp- site stood out as an outlier for each metal. tion (Palheiros et al. 1989). Th is apparently Th e reasons for the elevated concentrations overcomes the eff ect of higher pH in the es- are diffi cult to interpret case by case, but tuary, and consequently Cd concentrations many of the sites received discharges from are lower in the estuarine sediments when industrial areas (sites 37, 33B, G1, 900, 94). compared with fl uvial environments. Another reason may be the proximity of a Horowitz et al. (2008) reported that con- major highway which was not included in tribution of nonpoint-sources to sediment the contributing catchment (site 21). bound trace metals of streamwater in Atlanta In the Gräsanoja dataset, examination of were more important than point-sources. In the spatial distribution of metal concentra- the present study, metal concentrations were tions revealed evidence of point sources at moderately determined by diff use pollution. some additional sites. Th e acid extractable However, metal concentrations showed Pb concentrations were enriched at several considerable residual variation particularly sites in small tributaries along the Turun- among the urban sites, when the land use väylä highway (sites 11, 12, 14 and 15). Ero- was accounted for. Th e variation may be re- sion of soils previously contaminated by Pb lated to point sources or some unmeasured probably caused substantial Pb enrichment environmental factor (such as some element in these sediments. Th e pollution impact of sediment composition). was, however, mostly local, since the high concentrations were not strongly refl ected in 12.4.6. Easily reducible the sediments of the main channel. For Cd, metal and land use one site (45) showed highly elevated concentration compared to other sites in the Gräsa- Th e easily reducible metals appeared to re- noja dataset. Th is Cd “hot spot” is most fl ect the land use of the catchment more probably related to some point source of Cd. strongly than the acid extractable metals in However, low metal concentrations around the Gräsanoja dataset. Diff erent from the the old solid waste landfi ll in the Stream acid extractable metals, the correlations be- Gräsanoja catchment shows that, when the tween general urbanization indicators (ur- leachate waters are conveyed to wastewater ban%, dense urban%, TIA) and metals were

86 moderately high within the urban sites. Th e dictions of metal concentrations a larger cal- correlations with land use type percentages ibration data is needed, in addition to some were slightly weaker, but the commercial estimate of the possible of point sources. and traffi c areas appeared to have the strong- Th e logistic regression models showed est positive relationship with metal concen- that the occurrence of very high metal con- trations. centrations can be predicted to some degree. As describer earlier, the easily reducible Th e models performed moderately for the metal concentrations, particularly of Cu complete dataset, but the predictive statistics and Pb, were substantially controlled by the for the cross-validation were mostly poor sediment composition and the competition (Zn performed moderately-well). However, between diff erent binding forms of metals. the logistic regression models for Cu and Zn Even so, the easily reducible metals appear appeared to make some distinction between to be sensitive indicators of metal loading the present and absent sites even during the from urban settlement. cross-validation stage. Th e proportion of sites where the sediment quality guidelines 12.4.7. Predicting metal concentrations were exceeded could be increased by collecting samples only from the sites predicted to Th e predictive regression models showed be present (i.e. containing a high concentra- that land use explained only 10-24% of the tion of metals). Th us, if only the presence standardized metal concentration data of ur- of high concentrations in an area is to be ban sites used to construct the models. For confi rmed, screening of the sites according the validation data coeffi cients of determina- to land use reduces the costs of the study. tion were even lower. Th e strong variation of observed metal concentrations around the 12.4.8. Th e role of land use predicted concentration (Fig. 37) showed that, the models cannot be used to estimate It appears that the impact of urbanization the concentrations of individual sites. How- on trace metals was clearly evident when the ever, some broad spatial or temporal trends baseline sites were compared with the urban may be estimated using the information of sites in both datasets. Th us, the trace metal the catchment urbanization. Better models concentrations (particularly Cu, Zn and Pb) can be obtained, if the sediment composi- showed considerably higher concentrations tion is incorporated in the model instead of in all of the urban land use types than at the using general standardization methods, as baseline sites (in the areal dataset). Th e shift the average bias of the land use-sediment- from fairly sparse residential areas dominat- metal models were slightly lower than that ed by single-family houses to dense urban of the land use-metal models. However, this areas with apartment buildings and com- method necessitates the collection of repre- mercial or institutional districts increased sentative sediment chemistry data from the the average metal concentrations consider- study area. ably. However, diff erences between diff erent Th e predictive ability of the regression land use types in dense urban areas cannot models decreased considerably when cross- be easily distinguished. Th e variations be- validation was performed. Th us, the models tween rather similar catchments can be al- were unstable, and the removal of one tenth most as much as the variation in the median of the data lead to diff erences in the vari- concentrations between diff erent ends of the ables selected to the models. Th e instability urban continuum. Th us, dominating land is related to the small size of the data, as well use poorly predicts the concentrations at in- as to the heterogeneity of the data (very high dividual sites, and the land use of the catch- concentrations at some sites compared to the ment cannot be used as an indication of the rest of the samples). For more accurate pre- potential need for stormwater treatment or

87 stream restoration. Th e dense urban areas substantially stronger relationship with trace and industrial areas appear to have the most metals in the small catchments of the ar- potential for high metal concentrations, but eal dataset when compared with the larger enriched sites were also observed in low den- catchment of the Stream Gräsanoja. sity residential catchments. Th e idea of linkages between land use Th e correlations between land use met- and metals is based on the assumption that rics and trace metal concentrations con- most of the metals are derived from diff use fi rmed the relationship between contamina- pollution arising from various urban sources. tion and the degree of urbanization within Consequently, abundant point sources may the urban sites of the areal dataset. However, reduce this correspondence. In this study, the majority of the correlations between the similarity of the spatial variations of the land use variables and trace metal concen- studied metals indicates that similar diff use trations were non-signifi cant or showed only sources were dominantly responsible for relatively low signifi cance levels. Th is was metal concentration variations. However, partly due to the corrections performed to the highest concentrations of metals did not decrease the errors caused by multiple sta- occur at the same locations, and hence point tistical tests. If the correlations were tested sources were likely to occur in the urbanized only for the most commonly used indicators catchments. Accordingly, this decreased the of urbanization (TIA, EIA), then consider- correspondence between metals and land ably higher signifi cance levels would have use metrics. More detailed studies, especially been obtained. Th e use of the corrected sig- with a smaller spatial resolution, are needed nifi cance levels means, however, that only a to confi rm the sources of the highest metal very small proportion of the signifi cant cor- concentrations. relations between the metals and the indica- Moreover, complexity of sediment and tors of urbanization were caused by change. metal transport, and the spatial diff erences in Th e purpose of this study was to explore the erosion may complicate the spatial patterns nature of the relationships between land use of metal concentrations (Karouna-Renier & and metal contamination. Th erefore the re- Sparling 2001). Th e infl uence of sediment sults were not interpreted on a pass fail/basis characteristics was taken into account in this according to the signifi cance levels. Instead, study, but other processes such as catchment the sizes of the correlation coeffi cients were hydrology and fl ow conditions, the design also considered (see Cabin & Mitchell 2000; of stormwater sewerage system or biogeo- Moran 2003). chemical changes may mask land use eff ects. Despite the signifi cance of the results, Charlesworth and Lees (2001) went so far the correlation coeffi cients between the land as to claim that the sediment may fi nally use metrics and trace metal concentrations refl ect the processes which formed it rather were at most moderate. Th is refl ects the im- than the original sediment material, because portance of other factors, such as sediment the sediment goes through alterations on composition (which could not be totally the way downstream from urban surfaces to accounted for by normalization) and point stream sediments. sources. In the Gräsanoja dataset, the p-val- Th e results emphasize the importance of ues were low due to the low number of in- the entire upstream catchment to the met- dependent observations. Th is did not have al concentrations in the stream sediment. a strong impact on the interpretation of the Th us, urban development at the watershed results, because most of the correlation co- areas could have an impact on the chemi- effi cients between land use and metals were cal quality of the sediments and water of the too low to indicate any meaningful relation- downstream stream reaches. Th e results of ships. Th us, the proportions of diff erent land this study concerning the land use impact use types and other land use metrics had a on trace metals might be at least partly trans-

88 ferable to other small urban catchments with concentrations (Birch et al. 2001). However, a fairly similar, mainly residential history of the ability to distinguish the diff erences of urban development. Th e extrapolation of metal concentrations between sampling sites results obtained from small areas to larger also depends upon the contaminant gradi- catchments is, however, more problematic ent (Birch et al. 2001). In the lower reaches due to the increase of the possibility of dis- of the main channel metal gradients were turbing factors (increasing noise) (Ongley low, and the diff erences between sites were 1987). Th e results could serve as an informa- aff ected by fi ne-scale variation. However, re- tion source for land use planners and envi- gional comparisons of metal concentrations ronmental administrators in minimizing the have primarily a meaningful interpretation detrimental eff ects of urban development in the Gräsanoja dataset. In addition to the on nature. Ongley (1987) claims that me- acid extractable metals, the easily reducible dium sized study catchments (tens to several metal patterns also seemed to be unaff ected hundred km2) are needed in order to make by the fi ne-scale spatial variations. interventions involving general questions of Th e fi ne-scale spatial variation of repli- land use and to off er information for the de- cate samples includes variation of metal con- velopment of land use policies. In Finland, centrations at the sampling site and variance however, urban areas are relatively small in associated with sample pre-treatment and extent, and the impact of urbanization is analytical process itself (Birch et al. 2001). likely to be most pronounced in small water Th e results show that the sampling site selec- bodies. Th e contamination of most urban tion within an area of a few square meters, water bodies may, thus, be best controlled by and the pre-treatment methods (sieving, set- regulating and steering the local land use. tling and freezing) were not involved with substantial variation between the samples 12.5. Spatial and temporal variation (c.f. Barbanti & Bothner 1993; Miseroc- chi et al. 2000). Th e fine scale-variations were only slightly higher than the analytical 12.5.1. Analytical and fi ne- variance, which demonstrates that the pre- scale spatial variation treatment methods and concentration determination provided reproducible results. Th e fi ne-scale variations of metal concentra- However, both analytical and fi ne-scale spa- tions within a site were small when com- tial variances were high (more than 20%) at pared to other investigations (Birch et al some sites particularly for Pb, which shows 2000, 2001; Walling et al. 2003). Further- that moderately low concentrations diff er- more, the small-scale variations of sediment ences between single sites may be aff ected properties were on average under 25%, a by errors related to the selection of the sam- limit considered to indicate signifi cant dif- pling site or the analytical process. ferences by Fisher et al. (2004). However, In previous studies, high fi ne-scale vari- the magnitude of small-scale variation in ability even between sites separated by a few relation to regional and local concentration metres have been observed (van der Perk & diff erences is more important than the abso- van Gaans 1997), which is in contrast to the lute value of small-scale variation. Th us, the present results. Typical reasons for the fi ne- fi ne-scale variations of trace metals within scale variability include locally varying sedi- the replicate sites were very low compared mentational conditions and sediment com- to variations between the replicate sites, and position, particularly organic matter content between all sites of the Gräsanoja dataset. and the relative variations of fi ne particles Th is suggests that fi ne-scale variations do and coarse diluting material (Wilber & not have a considerable impact on the in- Hunter 1979; Miller 1997; van der Perk & terpretation of spatial variations of metal van Gaans 1997; Ciszewski 1998; Ladd et

89 al. 1998; Rhoads and Cahill 1999; Birch et 2003). Th e metal concentrations between al. 2000, 2001; Walling et al. 2003). In this the two sampling occasions did not increase study the control of sediment composition or decrease consistently, although Cu and on trace metals was also relatively strong. Zn concentrations were mostly higher in the However, due to the small number of repli- samples taken in October than September. cate sites the results are not conclusive. Since Th e variations of metals were controlled by the replicates represented similar morpho- the sediment composition, TOC in particu- logical settings (low energy environments), lar. In addition, the variations were likely to the fi ne-scale variations of sediment compo- be related to local factors, such as changes sition and metals were low. Higher fi ne-scale in the sediment sources through time, and variability of both sediment composition consequently variations of the infl ux of and metals could be expected between dif- contaminants or diluting sediment material ferent geomorphological units or diff erent (Wei & Morrison 1993; Foster & Charles- parts of the cross-section. worth 1996; Rhoads & Cahill 1999; Wall- ing et al. 2003). 12.5.2. Temporal variation Temporal variations of metals were substantially higher than the fi ne-scale varia- Th e variations of metal concentrations be- tions. Th e results indicated that moderate tween two low fl ow periods were studied spatial diff erences between sites can be a in order to assess the infl uence of temporal consequence of temporal variations in metal variation over the period of a sampling cam- concentrations. Th us, relatively low varia- paign on the results of the study. Sediment tions between neighbouring sites should not dynamics is one of the most important fac- be interpreted from sediment data due to tors aff ecting temporal variations of metals. the possibility of analytical, fi ne-scale spatial It is strongly dependent upon the hydrologi- or especially temporal variations. However, cal conditions which infl uence runoff char- concentration trends or broad-scale patterns acteristics and erosion intensity in the catch- are not aff ected by sources of variation. Th e ment. During high fl ow, the fi ne grained temporal variation tends to decrease the sta- settled sediments are resuspended and the tistical relationships between regional pat- bottom sediment is fairly coarse (Jain et al. terns of metals and environmental factors, 2005; Taylor et al. 2008). During dry pe- but does not induce bias. In addition, the riods and slow fl ow velocities fi ne grained identifi cation of sites showing very high sediments and associated trace metals ac- concentrations, i.e. “hotspots”, were not cumulate on the bottoms of the streams. impeded by the temporal variation between Th e samples were collected during low-fl ow low-fl ow periods. periods, but temporal variations of metal Th e temporal variability can occur at dif- concentrations were more than 15% for all ferent scales from hours, to months (season- other metals than Cd. Th e observed varia- al) and years (climatic change) as a response tions were, however, comparable to seasonal to the scale of major fl ow variations (Birch et variation between low-fl ow periods in con- al. 2001; Jain et al. 2005). High fl ows (fl ood secutive years reported elsewhere (Axtmann peaks) are generally recorded several times a & Luoma 1991; Birch et al. 2000; Walling year in southern Finland (see Kotola & Nur- et al. 2003). Even higher average variation minen 2003; Ruth 2004), and the surfi cial (10-50%) tends to be associated with more sediments in the sampling sites of this study diff ering hydrological conditions (Birch et have rarely time to sustain for months or al. 2001). years. Th erefore, the sediment samples are Th e reasons for temporal variation are likely to refl ect the properties of suspended often diffi cult to identify (Rhoads & Cahill sediment transported by relatively recent 1999; Birch et al. 2000, 2001; Walling et al. storm events.

90 12.5.3. Spatial autocorrelation of metals ent on each other in urbanized areas, where metal discharges are not dominated by one Metal concentrations showed moderate but several smaller sources (e.g. stormwa- spatial dependency, which implies that the ter outlets). Sampling intervals of 200 me- metal concentrations of closely located sites ters turned out to be too high to record the are similar. Th e residuals of the sediment strength and extent of local spatial autocor- composition models show no substantial au- relation in the Stream Gräsanoja using spa- tocorrelation which indicates that sediment tial statistics. Th e concentrations are, thus, composition is primarily responsible for the likely to decrease rapidly due to dilution by observed spatial autocorrelation pattern. bank and surface erosion. Van der Perk and Consequently, true local autocorrelation, van Gaans (1997) also observed no spatial related to the dispersion of metals independ- dependency of metals at fi ner spatial scales. ently of the environmental factors appears to Ladd et al. (1998) on the other hand found be fairly low. spatial autocorrelation between sites separat- In other studies high metal concentra- ed by 50 meters or less. Consequently, based tions have been encountered close to metal on the results of this study and reading of sources such as mining activity or large ur- literature, the spatial autocorrelation of met- ban centres, with a pattern of downstream als in fl uvial system receiving metals from decreasing concentrations (Axtmann & several moderate sources does not extend to Luoma 1991; Heiny & Tate 1997; Cal- hundreds of meters distance in fl at terrain. lender & Rice 2000). Possible reasons for A smaller sampling interval would allow the this spatial autocorrelation pattern include determination of fi ner scale spatial patterns. hydraulic sorting, dilution by uncontami- Th e small extent of local spatial auto- nated sediment and losses along the chan- correlation suggests that there is no local nel as metals are deposited (Axtmann & dependency between adjacent sites and that Luoma 1991; Miller 1997; van der Perk & the sediment samples collected using 200 van Gaans 1997). In fl uvial environment, metre sampling intervals would be spatially water transport further tends to lengthen independent of each other. However, the the extent of spatial autocorrelation (Dent transport of metals appears to play a major & Grimm 1999). role in controlling the metal concentrations Unfortunately, few quantitative studies at broader scale. Th is was demonstrated by exist on the spatial autocorrelation of met- the importance of the entire upstream catch- als in stream sediments (for exceptions, see ment in determining the metal concentra- van der Perk & van Gaans 1997; Ladd et al. tions. Consequently, each observation does 1998), and none for urban settings. One can not represent an independent sample and assume that spatial autocorrelation occurs bring a full degree of freedom in statistical downstream of metal sources, which include analysis (Lichstein et al. 2002; Wagner & stormwater outlets or industrial discharges. Fortin 2005). As a result, correlations ap- Th is kind of behavior has been observed pear to be more signifi cant than they actu- qualitatively by several authors (e.g. Wei & ally are (Lennon 2000). Th erefore, the ex- Morrison 1993; Bubb & Lester 1994; van tent of spatial autocorrelation was estimated der Perk & van Gaans 1997; Rhoads & from the spatial correlograms of the metal Cahill 1999), but only downstream of indi- concentrations, so that the number of inde- vidual metal sources. In this study, the trans- pendent observations could be determined. port and dilution pattern of metals in stream Finally, the correlations between metals and sediments were approached using spatial sediment composition or land use metrics statistics. Th e purpose of the approach was were calculated using a reduced number of to determine, wheather the metal concen- degrees of freedom, as suggested by Diniz- trations in adjacent locations are depend- Filho et al. (2003) and Wagner and Fortin

91 (2005). fi ciently on the suspended matter. Determination of spatial patterns is de- Most of the SPM metal concentrations pendent on the distance and connectivity showed a statistically signifi cant diff erence measures used (Ganio et al. 2005; Peterson between the baseline sites and urban sites et al. 2006; Ver Hoef et al. 2006). Th e flu- during both sampling periods, whereas the vial systems pose challenges when determin- total metal concentrations in water did not ing the spatial autocorrelation. Hydrological show any diff erence between land use cat- distances, which means the downward dis- egories during the low-fl ow period. In ad- tance between two sites which are function- dition, all of the SPM metal concentrations ally connected, i.e. water must fl ow from were strongly related with the impervious- one site to another, were used in this study. ness of the catchment during the high-fl ow However, the connection between two sites period. Hence, the suspended particles seem is not necessarily solely determined by the to refl ect the land use of the catchment con- absolute distance along the network. Sites lo- siderably better than the total metal concen- cated on a small tributary may have a weaker trations in water. Th e correlations of metals impact on the downstream sites when com- with land use percentages were low during pared with sites located on larger tributaries the low-fl ow stage. Th erefore, the low-fl ow (see Peterson et al. 2006). Models that take SPM metal concentrations are likely to re- water fl ow into account have already been fl ect the diff erences between urban and non- developed, but further research in this fi eld urban land use, but do not indicate the de- is still required (Peterson et al. 2006; Ver gree of urbanization more specifi cally. Hoef et al. 2006). In future, more appropri- Th e temporal trends of SPM metal con- ate hydrological geostatistical models may centrations are very diverse (Williamson provide more functional spatial patterns in 1985; Carter et al. 2003, 2006; DeCarlo fl uvial systems. et al. 2004; Bibby & Webster-Brown 2005; Horowitz et al. 2008), and the various pat- 12.6. Trace metals in the terns may refl ect diff erences in storm events, water phase and SPM land use and other catchments characteristics. Often, the metal concentrations of hy- Th e partitioning between dissolved and par- drological events are lower than base-fl ow ticulate phases showed the typical sequence concentrations due to the dilution eff ect, of Pb being the most attached to the par- and higher grain-size of the sediment being ticulates (see Salomons & Förstner 1984). transported (Carter et al. 2006; Horowitz Th e particulate proportions of Zn, Cd and et al. 2008). However, in urban catchments Cu were relatively low during the low-fl ow the dilution eff ect is weaker, since there is period, but increased during the high-fl ow less potential for erosion of uncontaminated period (c.f. Blake et al. 2003). During the areas, and the metal concentrations of SPM snow-melt period Cd was also nearly com- may remain relatively constant throughout pletely bound to the particulate phase. Th e the storm (Carter et al. 2006). Th e results of relatively low pH-values of the fi rst stages this study also suggest that the SPM metal of the snow-melt period did not evidently concentrations may be higher during snow- cause a decrease in the solid partitioning of melt induced high-fl ow periods in urban metals. Th e high particulate proportions catchments when compared with basefl ow suggest that a major fraction of the metals conditions. Th e SPM metal concentrations transported by the streams during high-fl ow were not related with the amount of sus- events could theoretically be removed from pended sediment during high fl ow, which the water utilizing stormwater treatment sys- indicates that no substantial dilution by tems. However, these systems should be able eroded surface soils occurred during the to retain even the fi nest particles to work ef- sampling period.

92 Controversial opinions of the role of bed conditions. It is likely that accumulation of sediments as indicators of suspended sedi- the sampled bed sediments has taken place ment metal concentrations of diff erent fl ow over a relatively long time period, although regimes have been presented. Horowitz et al. the results suggested a close correspondence (1999, 2008) have considered bed sediment between the bed sediments and storm-fl ow to correspond with the base-fl ow suspended SPM. sediment, whereas other researchers have Th e metal concentrations of the water found a correspondence between metals phase were only weakly associated with the in bed sediments and SPM collected dur- degree of urbanization. Th e low-fl ow con- ing storm events (Rhoads & Cahill 1999; centrations had no relationship with land use Owens et al. 2001). In this study, metal con- for any of the aqueous metal concentrations. centrations of SPM appeared to be related to During the high-fl ow stage there was a dis- the bed sediment metal concentrations dur- tinction between the total metal concentra- ing both hydrological conditions. However, tions of the urban and non-urban sites, but the high-fl ow concentrations had a closer no linkages between metal concentrations association with the sedimentary concentra- and land use could be distinguished within tions. Catchments were poorly hydraulically the urban sites. Th e total metal concentra- connected with the stream in dry periods, tions were dependent upon the concentra- and it is unlikely that considerable amounts tion of suspended sediment, which indicates of particulate metals were transported to the that the use of aqueous metal concentrations stream from urban areas at the time of the in contamination monitoring may suff er sampling. Th e interaction between the bed- from the problems associated with tempo- sediments and low-fl ow SPM may be related ral variation of suspended matter during a to the remobilization of bed sediments (see storm event. Correspondingly, temporal Salant et al. 2007). metal concentration variations of an order Here the bed sediment samples were of magnitude have been recently observed in collected in summer base-fl ow conditions, stromwater during a storm event in Helsinki when the amount of fi ne sediments accu- (Karvinen 2009). Th e samples of this study mulated on the stream bed is usually con- were collected when the snow cover was sidered to be highest (Walling et al. 1998; gradually thawing. Th erefore, the suspended Collins & Walling 2007). Th e upper fluff y sediment concentrations and consequently layers (SFGL) of the sediment have prob- also the aqueous metal concentrations, were ably accumulated during base-fl ow mainly relatively stable over time. When sampling is via deposition of aqueous fl ocs. Previously, carried out during individual storm events, SFGLs have been observed to reach thick- it may be diffi cult to obtain as stable metal nesses of 5-20 mm (Carling & Reader 1982; concentrations for extensive spatial com- Droppo & Stone 1994a). At the majority of parisons the sites, the sediment beneath the SFGL Th e dissolved Cu and Zn concentra- layer presumably accumulated during nor- tions of high-fl ow samples appeared to be mal conditions and the falling limb of the related to the degree of urbanization. Th is previous storm event. Wood and Armitage indicates that the higher input of metals (1999) described the succession of fi ne sedi- from urban surfaces is also refl ected as high ment deposition on the bed of a small stream dissolved concentrations. During low-fl ow in the UK. Th ey observed that the marginal conditions, when practically no water is dis- deposits were the fi rst to accumulate on the charger to streams from urban surfaces, the declining limb of the hydrograph, while the dissolved metals did not refl ect the land use other deposits (depressions, macrophyte of the catchment. deposits, lee-side deposits and SFGL) were Dissolved Pb displayed a negative rela- created later during normal and low fl ow tionship with urbanization indicators during

93 the snow-melt period, which was very sur- Sampling of exceptionally coarse sediment prising, since it indicates that dissolved Pb is due to lack of fi ne-grained deposits may lead dominantly derived from non-urban areas. to major underestimation of anthropogenic Th is behavior may be related, in addition to metal contamination. the infl uence of pH, to water characteristics, Sampling of SPM should preferentially particularly the abundance of complexing take place during high-fl ow periods. It is, agents. However, the reason for the negative however, essential to confi rm the benefi ts correlation between Pb and urbanization re- of bed sediment and SPM, by perform- mained unclear. Due to the low percentage ing more detailed studies on the temporal of dissolved Pb, this unexpected pattern does variations of metal concentrations in these not play a dominant role in determining the matrixes. For example, the impact of dilu- behavior of total Pb concentrations. tion on SPM metal concentrations over the Th e particulate metal concentrations course of a short rain event might be crucial were not closely correlated with urbaniza- in determining the suitability of SPM for tion of the catchment, and are consequently source detection studies. not particularly suitable for contamination monitoring. Dissolved Cu showed a closer 12.7. Implications for water relationship with land use during the high- pollution studies fl ow period. However, the concentrations of some metals (e.g. Pb) were often very low in the dissolved phase, which may increase 12.7.1. Objectives of sediment assessment the impact of analytical errors on the results. Th us, the concentrations of diff erent sites Sediment assessment may serve a number would not be entirely comparable to each of diff erent objectives from specifi c problem other. oriented investigations to national or inter- In fl uvial environments metal contami- national water quality measurement pro- nation assessment studies are often per- grammes. Th e usefulness and the require- formed by collecting water samples, and ments concerning the sampling, analytical consequently analyzing dissolved or total techniques and data analysis of sediment metal concentrations (Nurmi 1998; Gro- assessment depend fundamentally on the maire-Mertz et al. 1999; Davis et al. 2001; aims of the study (see Förstner 2004). Th e Ruth 2004). Th e sediment concentrations following classifi cation of the objectives of of bed sediments and SPM are rarely con- water quality measurements that also ap- sidered. Th e results of this study show, how- ply to sediments has been presented: survey, ever, that metals in bed sediment and sus- surveillance and monitoring (Water quality pended particles are better correlated with surveys 1978). A fourth category, spatial the intensity of urbanization than metals and temporal prognosis, was later attached in the aqueous phase. Consequently, by us- to the classifi cation by Förstner (2004) and ing these matrixes for metal contamination Förstner and Heise (2006). Surveying is an assessment, the ability to detect the most intensive investigation which aims to gather- important contaminant sources could be ing information of a specifi c sediment/wa- improved at least in urban environments, ter quality problem. Surveillance also serves where metals are predominantly transported a specifi c purpose related to the control or in the particulate phase. Bed sediment sam- management but is continuous in nature. ples ought to be collected during low-fl ow In addition, surveillance studies often focus periods, when the temporal variations are on detecting sources and tracing pathways relatively low. Th e use of bed sediments is, of contaminants (Förstner & Heise 2006). however, constrained by the availability of Monitoring is ‘a continuous, standardized suitable sediment at the site of investigation. measurement and observation of the envi-

94 ronment’ (Water quality surveys 1978) with 12.7.2. Accounting for variations specifi c emphasis on anthropogenic changes in sediment composition and the suitability of water quality for future use (Förstner 2004). Monitoring often Sediment composition is one of the most involves the determination of bioavailability often cited factors that obscure the metal of contaminants and the ecological status patterns at the regional, local and temporal of the watershed utilizing a tiered approach scales (Arakel 1995; Ladd et al. 1998; Ker- (Förstner 2004). Spatial and temporal prog- sten & Smedes 2002; Walling et al. 2003). nosis aims to assess the risks related to soil or Th e infl uence of sediment properties and sediment erosion or chemical mobilization components that most strongly aff ect the currently or in the future (Förstner & Heise trace metal binding capacity of the sediment 2006). (surface area, metal oxides, organic matter, Th e sediment assessments usually in- clay mineralogy) increases with decreasing volve diff erent chemical, biological, physical grain size (Horowitz & Elrick 1988). In this analyses and/or modelling. Th e determina- study, it was also assumed, that grain-size tion of sediment metal concentrations often variations arising from diff erent hydraulic serves as a fi rst stage of the risk assessment conditions (both regionally and locally) es- associated with sediment contaminants sentially control the metal concentrations (Chapman & Mann 1999; Förstner 2004). of fl uvial sediments. Th us, the sediment However, metal concentrations and the samples were collected from depositional identifi cation of metal sources may even be areas where the sediments are primarily the central issue of many survey and surfi ne-grained. Th e depositional zones usually veillance type sediment investigations. Th e show highest metal concentrations, and low main focus of the following discussion is on natural variability (Ciszewski 1998; Ladd et the application of sediment chemistry analy- al. 1998; Rhoads & Cahill 1999). Th ese are sis to detection of metal sources in the fl uvial of high importance especially in detecting environment. anomalies, as high natural variability of sedi- Diff erent environmental factors can in- ment quality prevents the meaningful inter- crease the uncertainties related to assessment pretation of regional diff erences (Birch et al. of sediment metal concentrations. Th ese un- 2000). Th e fi ne-grained sediment is also im- certainties can be partly reduced by careful portant for the biota, since they off er most selection of proper methods, but the chain of the food (organic matter) for sediment of diff erent procedures involved in measur- dwelling organisms (Förstner 2004). ing sediment metal concentrations depends According to Ladd et al. (1998) the ap- on the purpose of the study. Th erefore, dif- proach, where sediments of only one type, ferent requirements and recommendations for example depositional zone, are sampled, may be proposed concerning the sampling is appropriate when the aim is to detect the (sampling site selection, sampling interval maximum concentrations of metals. When etc.), pre-treatment (sieving, drying), ana- the goal is to describe the overall quality of lytical techniques (including digestion), data the sediment or bioaccumulation of metals, presentation or interpretation of the results. or estimate transport and storage of metals, When the purpose of the study is to detect more complex sampling strategies should be metal sources, interference may be caused by implemented which aim to produce samples sediment composition, temporal variations that represent the variety of sediment types of metal loading and transport of metals. in the stream (Ladd et al. 1998; Walling et Minimizing the impact of these factors on al. 2003; Förstner & Heise 2006). the results reduces the uncertainties related Secondly, the remaining grain-size vari- with the interpretation of metal sources. ations were reduced by sieving of the sediment to separate the clay and silt (mud) frac-

95 tions from the sand fraction. Th e purpose of ity to detect anomalies is the choice of the this granulometric normalization approach proper extraction method. In this study, is to distinguish between natural variabil- partial dissolution (nitric acid extraction as- ity and human induced pollution (Kersten sisted by microwave) was used as the stand- & Smedes 2002). Th e usefulness of sieving ard approach, but the usefulness of the easily procedure in tracing pollution sources is reducible phase for metal source detection slightly controversial, because the fi nest sedi- was also examined. Th e partial dissolution is ment particles are most strongly aff ected by often considered more sensitive to anthro- erosion and transport processes (Wilber & pogenic eff ects compared to total digestion Hunter 1979). In spite of that, sieving has (Arakel 1995; Vallius & Lehto 1998), al- been successfully applied to reduce metal though Sutherland et al. (2001) found only variation (e.g. Birch et al. 2001), and it has limited diff erences between the total leach been suggested as a standard approach for and the nitrogen acid digestion. Th e acid ex- sediment quality assessments (Horowitz & tractable metal concentrations do not refl ect Elrick 1988; Arakel 1995). the eff ects of the minerals as strongly as the Th e results of the present study show that total leach, as most of the silicate minerals the grain-size separation succeeded well in and associated metals remain undissolved decreasing the fi ne-scale variation of metals. (Vallius & Lehto 1998), although the par- Th e resulting variation of the mud fraction tial dissolution used dissolves most of biotite was very low, and did not appear to mask and clay minerals and some of plagioclase in any metal concentration diff erences between addition to the organic matter and oxides sites. In the sand fraction, both analytical (Hämäläinen et al. 1997, Lahermo et al. and fi ne-scale spatial variations were greater, 1996). Th e results of the present study show which suggests that more pronounced un- that, for the purpose of source identifi cation, certainty is associated with the results, al- partial dissolution appears to be appropriate though the errors did not appear to disguise since the metal concentrations obtained re- the occurrence of metal “hotspots”. In ad- fl ect relatively well the land use of the ar- dition, the dependence of metal concentra- eas surrounding the streams, and moderate tions on sediment composition appeared to correlations were also found between metals be stronger in the sand fraction. Th us, the and land use metrics in the areal dataset. sieving procedure decreased the control of Th e higher enrichment factors of the sediment composition on metals, although easily reducible metals compared to acid the correlations between metals and land extractable concentrations suggested that use did not show considerable diff erences metals of the most mobile phase are more between the size-fractions. Th e most serious strongly aff ected by human actions com- drawback of granulometric normalization pared to the more strongly bound metals. is that the method is very time-consuming Th is conclusion is based on the assumption and laborious. Th erefore, despite the evident that the standardized concentrations above benefi ts of the grain-size separation, it re- the reference values are of anthropogenic mains unclear, wheather the bulk sediment origin (Irabien & Velasco 1999). A major could also be used for cost-effi cient tracing of part of the variations above the reference metal sources. Hence, future investigations concentrations is likely to be attributable to would benefi t from a pilot study undertaken land use diff erences, since the metal concen- to assess the extent of fi ne-scale variation of trations correlated relatively strongly with metals and the infl uence of sediment com- the land use metrics. Th e correlations were position on metals in the bulk sediment. even somewhat higher compared to the acid Next, in the series of diff erent procedures extractable metals. Th e results thus indicated aimed at reducing the infl uence of sediment that the metals in the most mobile phase are composition and enhancing the possibil- particularly sensitive indicators of anthropo-

96 genic metal contamination. Th is parallels the results obtained in the present study indi- fi ndings of Förstner and Müller (1981), who cate, that in sieved sediments collected from reported that, the metals released to aquatic depositional zones the eff ect of organic mat- systems by human actions are associated with ter may exceed that of pure granulometric relatively labile phases (cation exchangeable variations. positions and easily reducible compounds). Th e importance of the eff ects of sedi- However, the extent of fi ne-scale spatial ment characteristics on metal variations in and particularly temporal variations showed this study supports the conclusion presented that the uncertainties related to individual by Kersten & Smedes (2002), that a two- measurements were higher when compared tiered normalization, using both sieving with the acid extractable concentrations. and geochemical normalization is necessary. Nitric acid digestion assisted by microwave Th ere exist several alternative methods for is a standardized, well controlled procedure geochemical normalization (Fe, Co, organic (Vallius & Lehto 1998), and the obtained matter, clay content) (Loring 1991; Schiff & concentrations can be compared to reference Weisberg 1999; Matthai & Birch 2001; Ker- values and concentrations of other studies, sten & Smedes 2002), most commonly the which further support the applicability of normalization is achieved using conserva- the digestion. tive elements such as Al or Li that represent Th e various extraction methods evidently proxies for clay mineral content (Schropp et have their own benefi ts and drawbacks. Tack al. 1990; Rubio et al. 2000; Miserocchi et al. and Verloo (1995) recommended non-se- 2000; Aloupi & Angelidis 2001; Kersten & lective leaches that are easier to apply when Smedes 2002). Th e use of organic matter as compared with (multiple) selective extrac- a normalizer has been critisized for two rea- tions. Furthermore, Sutherland et al. (2001) sons. Firstly, it is not conservative but takes considered weak non-selective extractants to part in diagenetic processes in sediments be the best indicators of anthropogenic con- (Kersten & Smedes 2002). Secondly, it has tamination. However, the future availability various sources and can originate from pol- of reference materials for diff erent digestions lution sources, such as wastewater discharges should further guide the selection of meth- or black carbon particles in urban areas (Lei- ods to be applied. Based on this study the vuori 1998; Lin & Chen 1998; Matthai et acid extraction off ers a relatively useful, reli- al. 1998; Kersten & Smedes 2002). In this able and rapid method for surveillance in- study, however, organic matter appeared vestigations focused on anthropogenic metal to be the most signifi cant control on met- pollution (c.f. Wilson et al. 2006). als, and the impact of common sources be- Despite the granulometric normaliza- tween metals and organic matter were con- tion applied, the acid extractable metal sidered as being less important. Th erefore, concentrations of the mud fraction showed the sediments were normalized to organic fairly strong dependence on sediment com- matter and clay content, which succeeded position. Since the sieving approach mainly in removing most of the eff ects of sediment reduces the grain-size eff ects, variations composition. However, the regression mod- of organic matter content and mineral- els between metal and land use showed that, ogy remain after sieving (Kersten & Smedes the variations in metal concentrations could 2002; Walling et al. 2003). So far, the ef- be accounted for better when the sediment fect of diff erent grain-size properties driven composition was included in the models, by diff erent hydraulic conditions has been and the parameters were estimated specifi - considered as the major confounding factor cally for this data. However, the infl uence of for the interpretation of sediment chemistry sediment composition varied slightly even data (Combest 1991; Bubb & Lester 1994; between the two datasets of this study. Th is Kersten & Smedes 2002). In contrast, the corresponds with the view that the eff ect of

97 sediment composition is somewhat area- al. (2001). specifi c (Horowitz & Elrick 1988). Th e regional variations of sediment properties appear to be relatively unaff ected by 12.7.3 Th e infl uence of metal transport temporal variations between the two periods and temporal variations on sediment which represent base fl ow conditions. Low sampling and interpretation of the results fl ow conditions are likely to refl ect the maximum metal concentrations (see Birch et al. Th e results obtained here suggest that metal 2001) which are appropriate for investiga- concentrations are rapidly diluted in the tions aiming at tracing metal sources. High- downstream direction in urban fl uvial sys- er temporal variability would be expected tems. As a result of the small extent of lo- between variable hydrological conditions cal spatial autocorrelation, some moderate and diff erent seasons (Förstner & Wittman point source inputs may be missed if the 1981; Walling et al. 2003). Th ese variations sediment samples are collected a few hun- need to be determined in order to obtain a dreds of metres apart. Th erefore, for a de- comprehensive view of the comparability of tailed survey of the metal sources, the use of sediment samples collected at diff erent times a smaller sampling interval is recommended. (Walling et al. 2003). Strong fi ne scale variations of metals are Th ere are no clear standards of the ap- usually overcome by collecting composite propriate sampling interval but usually wa- samples (Ladd et al. 1998; Salminen et al. ter samples are collected more frequently 1998). Th us, a site may comprise a stretch than sediment samples (Förstner 1989). of 100-500 metres of the stream. Multiple Stronkhorst et al. (2004) suggested that once subsamples are collected from several depo- every 1 to 3 years is enough for monitoring sitional zones along the stream within the purposes under the demands of the Euro- site, and the subsamples are composited to pean Water Framework Directive. However, decrease the natural variability of metal con- Rhoads and Cahill (1999) and Birch et al. centrations. Th e results of the present study (2001) concluded that due to high temporal show, however, that for the identifi cation of variability in the urban fl uvial environment, anomalous concentrations in small urban assessment of metal contamination may re- streams, sediment sampling sites should be quire temporal sediment monitoring over limited in size. Due to the spatial pattern of several years. Th us, site-specifi c risk assess- metal transport (autocorrelation), the col- ment and decisions on sediment manage- lection of composite samples may eff ectively ment options in urban fl uvial environments impede the detection of metal sources (see should probably be conducted based upon also Karouna-Renier & Sparling 2001). Th e information of temporal (at least seasonal reliability of the results is better increased and short term) variations. However, one by collecting replicate samples at very fi ne sampling campaign may serve the needs of scale and by analyzing the spatial autocor- preliminary catchment-scale site prioritiza- relation structure from distances of metres tion. to several hundred meters. Compositing is usually selected based on the lower costs 12.7.4. Applicability of sediment associated with the approach. However, re- for aquatic pollution monitoring sults of the fi ne scale variation and spatial autocorrelation structure analyzed in a pilot In this study, metal concentrations of diff er- study can be used to direct the main sam- ent matrixes were correlated with the land pling campaignmore cost-eff ectively (Arakel use metrics in the following order: high fl ow 1995; Birch et al. 2001). Alternatively the particulate << high fl ow dissolved < low fl ow fi ne scale variation may be studied at the end bed sediments < high-fl ow SPM. Th us, the of the investigation as suggested by Birch et suspended sediments may, at best, provide

98 better indication of contamination when sediment quality, but monitoring is not de- compared to bed sediments, but the eff ects manded from the member states. Compli- of varying fl ow conditions may weaken their ance monitoring to check if the preset qual- advantage in contamination assessment. Th e ity standards are met is not yet appropriate results suggest, however, that sediments are because no environmental quality standards appropriate for pollution monitoring espe- have been set for sediment at the EU scale cially with a focus on detecting metal sourc- (Stronkhorst et al. 2004). Th e implementa- es in fl uvial environments, when sediment tion and setting of European EQS for sedi- compositional eff ects are taken into account. ment are obstructed by the additional costs Enrichment factors or easily reducible metal associated with sediment monitoring, and concentrations off er additional insights into to the lack of correspondence between ob- the degree of contamination due to anthro- served toxicity and EQSs (Stronkhorst et pogenic eff ects. al. 2004; Karvonen 2006). Recognizing the Th e benefi t of sediment assessment is misfi t between toxicity and sediment chem- that ‘the sampling does not need to be re- istry, proposals have been made to use the stricted to a limited number of infrequent sediment EQS as a screening tool in the fi rst high fl ow events’ (Owens et al. 2001). tier of a risk assessment rather than as a le- However, sediment quality assessment is not gally enforceable pass/fail limit in EU (Apitz as straightforward as that for water, since & Power 2002; Crane 2003). many site-specifi c parameters, such as sedi- Th e use of sediment seems to be a fairly ment composition, infl uence the pollutant simple, reliable and a relatively cost-eff ec- concentrations and their bioavailability (see tive way to gather information for spatial Förstner 2004). and trend monitoring (c.f. Metals in lakes Th e ecological consequences of con- and watercourses 2000; Brils 2004). Sedi- tamination have recently been a focal aspect ment assessment appears to be particularly of sediment assessment (Apitz 2006). Th is valuable for surveillance investigations or trend will continue particularly due to the synoptic surveys of metal pollution in urban ecological goals included in the European areas, when a large or dense spatial coverage WFD (Apitz 2006). However, neither the is essential. Th us, a general view of the met- metal concentrations of the partial leach or al concentrations and their variation may enrichment factors over baseline provide an be obtained. In addition, the sites may be indication of where adverse eff ects on biota screened for more detailed analyses (e.g. ex- or human health could occur (Th omson et tent of contamination or biological status). al. 1984; Burton 2002). Following the em- Th e bed sediments might off er a more sta- phasis on the ecological quality of waterbod- ble and time-integrated estimate of the ac- ies, sediment does not play a key role in the tual contaminant concentrations when com- new European water framework directive de- pared with water samples. Stronkhorst et al. spite the calls for including the role of sedi- (2004) and Casper (2008) even recommend ment and sustainable sediment management that analysis of sediment contaminant con- as an integrated part of WFD and river ba- centrations could serve the purposes of the sin management (Crane 2003; Brils 2004). WFD at relevant locations by aiding in the Sediment should not be treated in isola- monitoring of the progressive reduction of tion, but rather as a factor that impacts the contamination. Standardization of the sam- ecological quality of a waterbody and may pling and pre-treatment procedures in addi- be an obstacle to receiving good ecological tion to the development of some sediment status (Stronkhorst et al. 2004). WFD with quality guidelines would further increase the its daughter directive on the environmental usefulness of the sediment approach. quality standards in the fi eld of water policy (EC 2008) off er a mechanism to monitor

99 13. Conclusions an immediate threat to the biota. At some sites, however, high metal Trace metal contamination of urban streams concentrations showed potential in the Helsinki Metropolitan region, Fin- for harmful eff ects. At these sites land, was examined in this study utilizing a risk assessment involving the the information provided by bed sediment whole sediment mass and biological chemistry. In addition, the eff ects of the methods should be carried out to most important controls, namely sediment verify the pollution status of the composition and land use, were analysed sediments. In the catchment of statistically (Fig. 43). Th e variations of metal the Stream Gräsanoja the most concentrations at diff erent scales were also contaminated sites were located in quantifi ed in order to increase the under- the headwater reaches. Hence, it standing of the infl uence of natural factors would be relatively easy to control and processes on metal concentrations. Th e the highest metal discharges using results were fi nally evaluated to determine local stormwater treatment facilities. the applicability of sediment assessment in Furthermore, owing to their location aquatic contamination monitoring. Based in the small headwater reaches, the on the fi ndings, fi ve main conclusions can concentration “hotspots” are unlikely be drawn: to initiate large scale pollution. 1. Streams draining small suburban 2. Th e infl uence of sediment catchments in the Helsinki composition on metal concentration Metropolitan region are typically variations is comparable to that of moderately contaminated based land use, and it can interfere with on sediment chemistry assessment. the detection of anthropogenic At the majority of the sites metal contamination patterns. Organic concentrations were clearly above the matter was the dominant sedimentary baseline but did not appear to pose control on metals, although metals

Figure 43. Factors controlling the variations of trace metal concentrations (outside the outer circle). Th e nested circles describe the contribution of diﬀ erent scale variation components on the regional variation of metals: the ﬁ ne-scale spatial variation, temporal variations within the sampling period and the between site variations.

100 were not operationally bound to sediments studies in particular is the organic compounds. Scavenging of high number of catchments that can metals to the sediment appeared to be be included. In future, fi ngerprinting strongly dependent upon the amount studies involving stable isotope of fl ocs which consisted of organic measurements may off er a novel matter and mineral components. method to assess the contributions of Signals of abundant soil aggregates diff erent sources in stream sediments. were observed in sediments with low Th e information of the general metal concentrations. Consequently, increase of metal concentrations organic matter appears to have a with increasing urbanization can be strong indirect infl uence on the utilized in land use planning or large metal binding capacity of the scale environmental management sediment. Hence, despite the critical projects. However, the results views presented previously, it appears show that questions concerning that sediment metal concentrations more detailed issues, for example should be normalized to organic in stormwater treatment or stream matter content, that is if substantial restoration decisions, require contributions from sewage can be site-specifi c metal concentration ruled out. analyses. 3. While diff use sources from urban 4. In the fi ne-grained depositional land use lead to moderate increases of sediments metal concentrations are metal concentrations with increasing dominantly determined by regional urbanization, point sources or some patterns, in that the fi ne-scale spatial other site-specifi c factors appear or temporal (between two low-fl ow to be responsible for the highest periods) variations do not appear to metal concentrations. Especially hamper the detection of signifi cant the concentrations that indicate a metal concentration patterns. Th e probable threat to the biota could problems caused by natural spatial not be explained by land use. From and temporal processes can evidently the environmental perspective, future be reduced by the sampling and studies should focus on assessing the sediment analysis methods used reasons for these high concentrations. here, and the sediment approach Th e results further indicated that is according to the present results, the highest metal concentrations applicable to metal contamination were not linked with any particular studies. However, further studies dense urban land use type, although should be aimed at capturing the many of the industrial sites showed temporal and spatial variations of very high metal concentrations. Th e metal concentrations. Th e temporal impacts of buildings and transport variations of metal concentrations areas appeared to be equally strong. in depositional surface sediments Th ese fi ndings contradict many of the are still poorly understood, and it loading estimates and water quality is vital to investigate the variations studies carried out with relatively of sediment composition and metal few study catchments. It seems concentrations a) between diff erent evident that diff erent approaches seasons b) between separate low- to estimating metal sources can fl ow periods and c) in the short improve the current knowledge of term, focusing on the building up the factors responsible for urban of metal concentrations following a contamination. Th e advantage of storm-event. Studies involved with

101 the spatial variations of sedimentary metal loads from a limited number of study metals would be most benefi cial at sites and catchments. Th e spatial coverage of scales ranging from tens of metres water quality studies is restricted by the high to a few hundred meters. Th e results amount of data needed to obtain reliable re- of this study suggest that the major sults. In spite of the relatively high number increase in spatial variation occurs of studies, the general understanding of the within this range for depositional relationships is rather vague (e.g. Baker 2003; sediments. Tiefenthaler et al. 2008). In particular, the 5. Th e stream bed sediments and variation of metal contamination within cer- suspended particles show potential tain land use types or within equally urban- for wider use in spatial and temporal ized catchments cannot be determined if the contamination monitoring in number of studied catchments is low. Th is is urbanized areas. When single certainly the case in Finland, where studies samples are collected, these matrixes on aquatic metal contamination in urban- appear to refl ect land use better ized areas are scarce. Th erefore, although than the commonly used total some of the present results support the gen- metal concentrations of the water eral perceptions, the quantitative results of phase. Sediment and suspended a large number of independent catchments sediment chemistry analysis obtained here essentially contribute to the provide methods for cost-eff ective knowledge of the patterns and variations and rather reliable contamination of stream sediment metal concentrations in assessment when continuous water urbanized areas. Th is information may be quality measurements are not highly valuable in future, since stormwater available. Such general estimates and the development of stormwater man- of metal contamination may be agement strategies are increasingly impor- needed, for example, as a basis for tant issues for municipalities. stormwater management strategies Finally, the application of sediment or as screening level information for analyses does not mean that all water qual- further contamination assessment in ity analyses could be replaced. Actual load- urban areas. Th e sediment approach ing estimates and information of short term might also improve the quality of variations provided by continuous meas- temporal environmental monitoring urements have their own advantages. In data when only coarse temporal addition, aquatic pollutants may in some resolution is needed. However, the situations remain in dissolved form, which lack of sediment quality guidelines necessitates the measurement of dissolved hampers the use of sediment metal concentrations. However, the poten- assessment in offi cial monitoring tial benefi ts of the sediment approach are tasks. Th us, development of sediment substantial. Stream bed sediments appear quality guidelines would increase the to be suitable for contamination monitor- applicability of sediment chemistry ing, particularly for the detection of spatial data in the future by enabling patterns and sources of trace metals. Large compliance monitoring. spatial coverage would be useful in many Th e statistical approach adopted here in environmental monitoring tasks, but the analysing the metal-land use relationships availability of resources limits the number has provided new insights into the nature of of catchments that can be studied by water the relationships between diff erent land use quality analyses. In these situations sediment parameters and trace metal concentrations. analyses would be particularly valuable, and So far, the urban metal pollution investiga- could be applied more frequently. Th e re- tions have mostly yielded information of the sources allocated to more detailed research

102 on the sediment approach in contamina- Th is work was primarily fi nanced by the tion monitoring may lead to reduced costs Finnish Geography Graduate School. I also of environmental monitoring in the future, received support from the Nordenskiöld- if water quality analyses can be replaced by samfundet and the University of Helsinki. sediment analyses. Maija Heikkilä off ered me encouragement (and understanding) throughout this Acknowledgements project. Th e long conversations we had during Nordic walking exercises and trips to the Th is thesis would not have been accom- ski tunnel in Paimio were very therapeutic! I plished without the support and advice also want to thank Maria, Taru and Venla for from many people. Firstly, I would like to giving me something else to think about. express my warmest gratitude to my super- Most of all, I want to thank my fam- visors Prof. Matti Tikkanen and laboratory ily. My mum, Tatta, is always encouraging engineer Dr Juhani Virkanen for their in- and ready to listen to my worries and com- terest, encouragement and guidance. Th is plaints. My dad, Seppo, who passed away a study was carried out at the Department few years ago, was interested in everything, of Geography, University of Helsinki. I was including my PhD thesis project. He off ered lucky to be able to use the laboratory and me mental support and assisted in the fi eld GIS facilities at the department. I want to work. I am grateful to my sister Elina and thank laboratory assistant Hanna Reijola, her husband Andi for their warm support. who gave me company and numerous valu- With all my heart, I want to thank Jan, able instructions in the laboratory. She also my colleague and husband, who has been conducted most of the water analyses. I also my “third supervisor”. He has always had acknowledge the advice and support that I faith in me, and given me love and sup- received from Prof. John Westerholm, Prof. port when I most needed it. Finally, I wish Matti Seppälä and Prof. Markku Löytönen. to thank Nella, our cheerful daughter, for I had many fruitful conversations with my teaching me how to fi nd joy in small things colleagues at the department. Here I want in life! Nella, when this thesis is fi nished, the to off er my sincere thanks to Dr. Olli Ruth, computer is vacant more often and you can Jari-Pekka Mäkiaho, Dr. Janne Heiskanen, play jigsaw puzzles on the computer every Dr. Sanna Vaalgamaa, Inka Kaakinen, and now and then. Hilkka Ailio who also prepared the layout of this thesis. I am grateful to the pre-examiners Prof. Miska Luoto and Dr Timo Tarvainen for their valuable and constructive comments. I want to thank Dr. Gareth Rice for proof- reading the manuscript for English and Barnaby Clark for proof-reading article manuscripts based on this thesis. I received storm sewer maps from the study region, and I warmly thank all persons who assisted in providing these maps. Spe- cial thanks go to Heidi Kauppinen (Helsinki Water), Juha Hermunen and Juhani Moil- anen (City of Espoo), Eero Heiskanen (City of Kauniainen) and Ulla-Maija Rimpiläinen (City of Vantaa).

103 References

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124 Appendix 1. Locations and elevations of the sampling sites in addition to the dominating land use and surficial sediment types of the catchments (areal dataset) (Map of quaternary deposits 1:20 000, sheets 203211, 203212, 203403, 203406, 203409, 204110, 204301, 204304 and 204307). HDR=high density residential, LDR= low density residential. No Sampling site Location Elevation (m asl) Catchment Surficial sediment Land use area (km2) 3 Kivenlahti 24°38'54"E 60°8'48"N 13 0.36 Till HDR 5 Kattilalaakso 24°39'54"E 60°10'5"N 12 0.52 Thin sediment cover LDR 8 Soukka 24°39'59"E 60°8'16"N 10 0.21 Thin sediment cover HDR 11 Matinkylä 24°44'45"E 60°9'2"N 1 0.42 Till HDR 13 Olari 24°44'11"E 60°9'36"N 5 2.01 Thin sediment cover HDR 14 Puolarmaari 24°42’43”E 60°10’44”N 10 1.41 Thin sediment cover Unbuilt 15 Latokaski 24°39'51"E 60°10'35"N 12 0.59 Thin sediment cover LDR 16 Furubacka 24°39'34"E 60°11'34"N 19 2.15 Thin sediment cover Unbuilt 17 Tuomarila 24°40'47"E 60°11'47"N 13 4.49 Thin sediment cover LDR

125 18 Kauniainen 24°44'20"E 60°12'41"N 23 0.28 Thin sediment cover LDR 19 Haukilahti 24°46'45"E 60°9'40"N 2 0.37 Clay and silt HDR 21 Tapiola 24°48'40"E 60°11'10"N 2 0.68 Till HDR 22 Rastasmäki 24°47'18"E 60°13'56"N 24 0.30 Clay and silt LDR 23 Otaniemi 24°50'12"E 60°11'14"N 6 0.02 Clay and silt HDR 24 Perkkaa (North) 24°49'29"E 60°13'2"N 3 0.05 Clay and silt HDR 25 Perkkaa (South) 24°49'47"E 60°12'45"N 1 0.09 Clay and silt HDR 26 Viherlaakso 24°44'25"E 60°13'30"N 29 0.65 Thin sediment cover HDR 27 Laaksolahti 24°45'47"E 60°14'46"N 34 0.34 Thin sediment cover LDR 28 Jupperi 24°46'40"E 60°14'23"N 22 0.50 Till LDR 29 Lintuvaara 24°49'20"E 60°14'14"N 30 1.08 Till LDR 30 Leppävaara 24°48'38"E 60°13'30"N 12 0.40 Thin sediment cover HDR 33 Hämevaara 24°48'7"E 60°15'17"N 31 0.16 Clay and silt HDR 35 Vapaala 24°49'17"E 60°15'52"N 29 0.19 Till LDR 37 Martinlaakso 24°50'18"E 60°16'49"N 33 0.47 Till HDR 38 Myyrmäki (West) 24°50'35"E 60°15'59"N 32 0.13 Till HDR 39 Myyrmäki (North) 24°50'41"E 60°16'20"N 29 0.55 Till HDR 41 Myyrmäki (South) 24°51'51"E 60°15'34"N 19 0.46 Clay and silt HDR 42 Malminkartano (East) 24°51'56"E 60°15'13"N 18 0.20 Clay and silt HDR 43 Malminkartano (West) 24°51'22"E 60°14'39"N 19 0.40 Clay and silt HDR 47 Kannelmäki 24°53'9"E 60°14'47"N 20 0.65 Till LDR 48 Hakuninmaa 24°54'30"E 60°14'47"N 24 0.11 Till LDR 49 Lassila 24°52'46"E 60°13'51"N 16 1.18 Till HDR 54 Paloheinä (South) 24°56'24"E 60°15'15"N 18 0.59 Till LDR 56 Karhusuo 24°37'33"E 60°13'40"N 19 2.70 Thin sediment cover Unbuilt 57 Pirkkola 24°55'28"E 60°14'0"N 20 2.50 Till LDR 61 Pihlajamäki 25°0'54"E 60°14'27"N 13 0.29 Thin sediment cover HDR 62 Ylä-Malmi 25°0'1"E 60°14'56"N 14 0.35 Sand and gravel HDR 63 Ala-Malmi 25°1'20"E 60°14'49"N 13 0.49 Clay and silt HDR 64 Tapaninvainio (South) 24°59'0"E 60°15'23"N 10 1.22 Clay and silt LDR

126 65 Tapaninvainio (North) 24°58'51"E 60°15'54"N 10 1.70 Clay and silt LDR 67 Siltamäki 24°59'38"E 60°16'45"N 12 0.23 Clay and silt LDR 70 Suutarila 25°0'50"E 60°16'50"N 17 0.23 Clay and silt Industrial 72 Viertola 25°1'32"E 60°17'21"N 13 0.16 Clay and silt LDR 75 Simonlaakso 25°1'48"E 60°18'35"N 23 0.20 Clay and silt HDR 77 Herttoniemi 25°3'2"E 60°11'43"N 2 0.32 Clay and silt HDR 78 Roihuvuori 25°3'57"E 60°11'46"N 2 0.27 Thin sediment cover HDR 84 Rajakylä 25°6'4"E 60°14'36"N 30 0.32 Sand and gravel LDR 86 Mellunmäki (West) 25°7'15"E 60°13'57"N 6 0.25 Till HDR 87 Mellunmäki (East) 25°7'46"E 60°13'49"N 4 0.12 Till HDR 88 Vuosaari 25°8'58"E 60°12'53"N 5 0.81 Sand and gravel HDR 91 Puistola 25°3'25"E 60°15'56"N 16 0.95 Sand and gravel LDR 93 Lommila 24°39'53"E 60°13'5"N 9 0.42 Clay and silt HDR 94 Juvanmalmi (East) 24°46'20"E 60°16'19"N 24 1.18 Clay and silt Industrial 97 Kaivoksela (West) 24°52'26"E 60°15'29"N 25 0.44 Sand and gravel HDR 240 Karamalmi 24°46'4,9"E 60°13'21,7"N 25 0.54 Thin sediment cover HDR 300 Kivenlahden teollisuusalue 24°37'49"E 60°9'35"N 10 0.49 Clay and silt Industrial 330 Koivuvaara 24°48'35,2"E 60°14'54,9"N 29 0.77 Clay and silt HDR 410 Myyrmäki (East) 24°51'50"E 60°15'53"N 20 0.25 Clay and silt HDR 900 Malmi (East) 25°1'51"E 60°15'18"N 15 0.97 Clay and silt Industrial 901 Oulunkylä 24°58'48"E 60°14'19"N 10 0.42 Till HDR 902 Varisto 24°48'52"E 60°16'32"N 23 0.97 Clay and silt HDR 903 Tammisto 24°58'52"E 60°16'46"N 12 0.77 Clay and silt HDR 904 Tattarisuo 25°3'11"E 60°15'36"N 16 0.14 Clay and silt Industrial 905 Petikko 24°49'34"E 60°17'19"N 26 0.77 Clay and silt Unbuilt 906 Kaivoksela (East) 24°52'57"E 60°16'11"N 23 0.43 Clay and silt Industrial 940 Juvanmalmi (East) 24°45'22"E 60°16'55"N 30 0.39 Bedrock outcrops Unbuilt 96 Paloheinä (North) 24°55'31"E 60°15'24"N 20 0.84 Clay and silt Unbuilt 960 Paloheinä(East) 24°55'40"E 60°15'25"N 20 0.30 Clay and silt Unbuilt

127 Appendix 2. Correlations between metals and sediment components in the sediments of the Stream Gräsanoja and its tributaries. Degrees of freedom for the calculation of the p-value=22 (24 independent samples, separated by more than 0.75 km)

log10Cu log10Zn log10Pb log10Cd TOC 0.49 0.48 0.22 0.59 Am. Fe-oxides 0.02 0.10 0.25 0.27 Fe-oxides -0.03 0.06 -0.24 0.11 Mn-oxides 0.00 0.16 -0.24 -0.12 Clay-% 0.08 0.06 -0.05 -0.10 Coarse silt-% -0.29 -0.24 -0.10 -0.10 Al 0.09 -0.04 -0.11 0.00 Li 0.40 0.31 0.21 0.12

Appendix 3. Correlations between metals and sediment components in the areal dataset.

log10Cu log10Zn log10Pb log10Cd TOC 0.51*** 0.55*** 0.60*** 0.44*** ** ** *** ** log10Clay-% -0.41 -0.37 -0.44 -0.35 log10Fe 0.04 0.14 0.11 0.17 Al -0.10 -0.02 -0.14 0.00 Li -0.24 -0.01 -0.21 0.06 log10Mn -0.08 -0.16 -0.05 -0.01

Appendix 4. Correlations between SPM metal concentrations (µg/g) and land use parameters within the urban sites of the areal dataset. Land use percentages were arcsine-transformed, metal concentrations log10-transformed and traffic and population densities sqrt-transformed.

Low-flow period High-flow period CuSPM ZnSPM CdSP PbSPM CuSPM ZnSPM CdSPM PbSPM M Urban 0.07 0.10 0.11 0.09 -0.01 0.20 0.12 0.17 Dense urban 0.22 0.04 -0.01 -0.16 0.56*** 0.47*** 0.31 0.32* TIA 0.05 -0.04 -0.22 -0.20 0.57*** 0.49*** 0.30 0.40** EIA 0.10 -0.04 -0.12 -0.15 0.60*** 0.45** 0.32* 0.31 Low density -0.16 0.05 0.09 0.14 -0.51*** -0.37* -0.31 -0.26 residential High density 0.30 0.19 0.02 -0.03 0.67*** 0.07 -0.16 -0.18 residential Institutional 0.24 0.10 -0.07 -0.11 0.20 0.23 0.13 -0.26 Commercial & traffic 0.17 0.17 0.08 0.11 0.25 0.13 0.13 -0.05 Industrial -0.20 -0.31 -0.06 -0.01 0.50*** 0.45** 0.47*** 0.69***

128 Appendix 5. Correlations between metal concentrations in the particulate phase (µg/l) and land use parameters within the urban sites of the areal dataset. Land use percentages were arcsine- transformed, metal concentrations log10-transformed and traffic and population densities sqrt- transformed.

Low-flow period High-flow period CuSPM ZnSPM CdSPM PbSPM CuSPM ZnSPM CdSPM PbSPM Urban 0.06 0.11 0.12 0.09 0.01 0.05 0.07 0.06 Dense urban 0.08 -0.08 -0.12 -0.13 0.12 0.03 -0.02 -0.05 TIA 0.03 -0.05 -0.26 -0.21 0.16 0.07 0.01 0.00 EIA 0.00 -0.11 -0.21 -0.21 0.18 0.06 0.03 -0.01 Low density residential -0.05 0.16 0.20 0.20 -0.08 0.02 -0.03 0.08 High density residential 0.07 -0.02 -0.18 -0.16 -0.19 -0.18 -0.28 -0.25 Institutional -0.00 -0.14 -0.30 -0.25 -0.07 -0.07 -0.10 -0.23 Commercial & traffic 0.12 0.07 0.02 0.07 0.26 0.19 0.20 0.15 Industrial -0.02 -0.09 0.12 0.11 0.32 0.24 0.27 0.26

Appendix 6. Correlations between metal concentrations in the dissolved phase (µg/l) and land use parameters within the urban sites of the areal dataset. Land use percentages were arcsine- transformed, metal concentrations log10-transformed and traffic and population densities sqrt- transformed.

Low-flow period High-flow period CuSPM ZnSPM CdSPM PbSPM CuSPM ZnSPM CdSPM PbSPM Urban 0.07 0.19 -0.03 0.15 0.04 0.14 0.04 Dense urban 0.12 0.16 0.08 0.43** 0.19 0.19 ‐0.30 TIA 0.09 0.11 0.05 0.43** 0.13 0.16 ‐0.33 EIA 0.09 0.14 0.07 0.45** 0.11 0.15 ‐0.38* Low density residential -0.03 -0.05 -0.08 ‐0.28 ‐0.13 ‐0.13 0.29 High density residential 0.26 0.16 0.01 0.13 ‐0.03 ‐0.10 ‐0.14 Institutional 0.14 -0.02 -0.18 0.13 ‐0.15 ‐0.19 ‐0.28 Commercial & traffic -0.10 -0.10 0.08 0.27 0.10 0.25 ‐0.16 Industrial 0.06 0.13 0.11 0.29 0.32 0.38* ‐0.09

129