Multivariate statiscal analyses for water and sediment quality index development: A study of susceptibility in an urban river

1. Multivariate statistical analysis for water and sediment quality index 1 development: A study of an urban river susceptibility.2 2. Evaluation of an urban river susceptibility applying multivariate statistical3 analysis for water and sediment quality index development. 4 5 Keywords: Quality Index; Principal Component Analysis; Urban catchment; Seasonality 6 Abstract: Applying a multimetric index for river quality evaluation becomes suitable for 7 identifying those sites susceptible to ecological deterioration due to multiple human 8 pressures. In this study, a Principal Component Analysis was performed to develop water 9 and surface sediment quality indexes, allowing us (i) to weighting the influence of each 10 individual environmental variable depending on the maximum gradients observed for 11 historical data monitored in the study river, (ii) to consider the possible synergism or 12 antagonism derived from the combined effect of several pollutants, and (iii) to express 13 the quality as a deviation with respect to a reference conditions selected based on 14 biological data previously obtained. 15 Water and sediment quality indexes assisted us in the rapid detection of the deleterious 16 effect of industrial, wastewater treatment plants and, mainly, untreated urban wastewater 17 effluents discharges into the Deba River catchment. On the other hand, high river flows 18 helped to dilute contamination enhancing water quality from January to March. River 19 quality also appeared to be coupled to sediment dynamics, since metals were 20 preferentially adsorbed onto sediments during the dry season, whereas there was potential 21 for metal mobilization to water during sediment resuspension in the wet season. 22 Therefore, an annual determination of surface sediments quality becomes suitable for 23 monitoring water quality during the dry season, identifying those sites which could 24 deserve special attention, and planning future strategies for river quality improvement. 25 This is the accepted manuscript of the article that appeared in final form in Science of The Total Environment 711 : (2020) // Article ID 135026, which has been published in final form at https://doi.org/10.1016/j.scitotenv.2019.135026. © 2019 Elsevier under CC BYNC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) However, two limitations were found: (1) surface sediment was not appropriate for water 26 physicochemical quality monitoring due to organic matter and nutrient continuous 27 transformation; and (2) a multimetric index did not provide a concise and definitive 28 quality information, thus a new tool for combining with quality index was proposed for 29 specifically evaluate the water and surface sediment quality at each location. 30 1. Introduction 31 Water quality index has become an appreciate tool in evaluating river quality since it 32 transforms multiple environmental indicators measurements into a single dimensionless 33 number. Consequently, quality index assists water authorities, policy makers and also the 34 general public in rapidly detecting spatial and temporal trends, identifying pollutants 35 sources, assessing regulatory policies and environmental programs, and making 36 recommendations for future improvements (Yisa and Jimoh, 2010; Finotti et al., 2015; 37 Gitau et al., 2016). 38 Although many water quality indexes have been developed and worldwide applied, 39 there is still no commonly accepted methodology for index development. In general, there 40 are four steps undertaken: (1) indicator selection; (2) sub-index obtaining or 41 transformation of measured values to a common scale; (3) assignment of weights; and (4) 42 aggregation of weighted sub-index to compute the final index value (Sutadian et al., 43 2016). Typically, the involvement of expert judgement (Delphi method) has been applied 44 for the first three steps in the formulation of the most used indexes on a national or global 45 level (e.g. National Sanitation Foundation Water Quality Index (Brown et al., 1970), 46 Scottish Research Development Department Index (SRDD, 1976), House’s Water 47 Quality Index (House, 1986) or Oregon Water Quality Index (Cude, 2001)), thus 48 introducing subjectivity in the index elaboration process. However, application of 49 multivariate statistical techniques, such as Principal Component Analysis (PCA), could 50 help gain objectivity and certainty. This method offers the possibility of introducing 51 historical data monitored at different locations along the study area, and the maximum 52 gradients observed are used to weighting the influence of each indicator on water quality 53 (URA, 2008; Primpas et al., 2010; Selle et al., 2013). 54 For river quality evaluation, sediments are also appreciated as quality indicators 55 because of their capacity to continuously accumulate pollutants (Devesa-Rey et al., 2010; 56 Bartoli et al., 2012). Their transport along the river is also coupled to sediment dynamics, 57 where suspended particles deposition favours pollutants being accumulated on the 58 riverbed and sediment resuspension usually occurring during flood events promotes their 59 mobilization (Rügner et al., 2014; Herrero et al., 2018). 60 To date, numerous indexes have been used for estimating the quality of river 61 sediments, such as the Geoaccumulation Index (Igeo) (Nowrouzi and Pourkhabbaz, 62 2014), Enrichment Factor (EF) (Kaushik et al., 2009), Potential Ecological Risk Index 63 (RI) (Kabir et al., 2011) or Pollution Load Index (PLI) (Banu et al., 2013). However, they 64 are not based on matching chemical and biological data (Birch, 2018). On the other hand, 65 the so-called Sediment Quality Guidelines (SQGs), such as Effects Range (ERM/ERL) 66 (Tokatli, 2017), Effects Level (TEL/PEL) (Zheng et al., 2008) or Ecological Risk Factor 67 (ERF) (Kabir et al., 2011), have been developed from empirical and mechanistic 68 approaches, and used for individual chemicals or a mixture of substances to screen 69 contaminants posing a risk to benthic communities (Birch, 2018). However, they are 70 purely additives and they do not consider possible synergism or antagonism derived from 71 the combined effect of several pollutants. 72 The overall aim of this study is therefore to develop a multimetric index for both water 73 and surface sediments, linking chemical with biological data for a more reliable 74 evaluation of the combining effect of various environmental indicators on the quality of 75 the Deba River catchment. The specific objectives were (i) to use a multivariate statistical 76 analysis enabling the consideration of possible synergic/antagonist effect, and the 77 quantification of the degree of closeness between the sampling site and a reference 78 conditions characteristics of the study area, (ii) to identify sources of anthropogenic 79 contamination influencing the river quality, and (iii) to evaluate the influence of 80 seasonality on physical mechanisms governing the exchange of pollutants between water 81 and surface sediments. We hypothesized that combined analysis of water and surface 82 sediment quality will provide us more comprehensive and detailed information about the 83 susceptibility of a catchment subjected to multiple human pressures. 84 2. Materials and methods 85 2.1 Study area and sampling description 86 Historically, the Deba River catchment (538 km2, Fig. 1) has been notable for its 87 greatest ecological deterioration caused by a dense population and an intense industrial 88 development, which contribute to metals, nutrients and/or organic-rich compounds 89 discharges into the main river and streams (Martínez-Santos et al., 2015). Until the recent 90 construction of a sewerage system and the commissioning of three wastewater treatment 91 plants (WWTPs) –the Apraitz (2007), Mekolalde (2008) and Epele (2012) −, a moderate, 92 poor or even bad ecological potential was reported in the main river, as well as in various 93 streams like the Oñati and Ego (URA, 2013). However, improvements in the compliance 94 with the environmental objectives have been registered over the last years (2013-2017), 95 especially in the mid-high part of the main river and the Oñati stream. Even the Ego 96 stream, which continues to gravely breach all biological indicators, presented a slight 97 positive change for the invertebrate community and the fish fauna in the last campaigns 98 (URA, 2018), probably due to the construction and connection of the sewer from Ermua-99 Eibar to the Apraitz WWTP in June 2014. To date, untreated urban wastewaters (UWW) 100 from Mallabia are still discharged in the Ego stream (Fig. 1). 101 According to meteorological and hydrological data measured and recorded every 10 102 min since October 1995 in the Altzola gauging station by Gipuzkoa Provincial Council 103 (www.gipuzkoa.eus/es/deba), mean annual precipitation (P) and discharge (Q) for the last 104 ten years (2008-2018) were 1245 mm and 12.3 m3 s-1, respectively. The wettest period 105 extended from January to March and the driest months were usually from July to October. 106 Therefore, two different sets of hydrological conditions were established during the 107 research year (from Jan. ’15 to Jan. ’16): a high flow or wet season corresponding to 108 sampling campaigns with Q values of over 12 m3 s-1 (Jan.’15, Feb. ’15, Mar. ‘15 and Jan. 109 ‘16); and a low flow or dry season comprising months with Q values of below 12 m3 s-1 110 (Fig. 1). 111 With a view to studying the influence of different anthropogenic contamination 112 sources on the river water and surface sediment quality, eleven locations along the Deba 113 River catchment were chosen: in the main river (D1-D7), and in the Mazmela (M1), Oñati 114 (O1) and Ego (E1-E2) tributaries (Fig. 1). Monthly or bimonthly water samples were 115 collected in polyethylene bottles and water electrical conductivity (EC) was measured in 116 situ with a Crison EC-Meter Basic 30+. By contrast, surface sediment sampling was 117 exclusively carried out on the campaign of October 2015, a low flow month (1.435 m3 s118 1; Fig. 1), in an attempt to diminish the influence of hydrological pattern over the chemical 119 and, predominantly, biological characteristics. As per USEPA (2001), surface sediment 120 subsamples (0-5 cm depth) from multiple points within each sampling location were 121 collected using a sterilized spoon, sieved through a 2 mm mesh, composited in the field 122 and sealed in sterile polypropylene containers. All water and sediment samples were 123 refrigerated in the dark and transported to the Chemical and Environmental Engineering 124 laboratory (University of the Basque Country) on the same day. 125 2.2 Environmental variables analysis 126 Once in the laboratory, water samples were filtered through 0.45 µm Milipore 127 nitrocellulose filter. One replicate of each sample was acidified to pH < 2 with HNO3 128 (69%) for cations (Ca2+, Mg2+, Na+ and K+) and metals (Cu, Zn, Cr, Ni and Pb) analysis 129 using ICP-OES (Perkin Elmer Optima 2000) and an Ultrasonic Nebulizer (CETAC, 130 U5000AT+; only for metals). Dissolved organic carbon (DOC) and anions (NO3-, SO42131 and Cl-) were measured in the non-acidified replicate using a Total Organic Analyzer 132 (TOC-L Shimadzu) and an ion chromatography (DIONEX ICS 3000), respectively. 133 Additionally, concentration of NH4+ was determined by the modified Berthelot reaction. 134 The ascorbic acid method (4500-P E) and the N-(1-naphthly) ethylenediamine 135 dihydrochloride method (4500-NO2B) were used for PO43and NO2determination, 136 respectively (APHA-AWWA-WPCF, 2018). 137 Surface sediments samples (< 2mm) were air-dried and ground with a pestle and 138 mortar for homogenization. Their moisture content was determined in accordance with 139 APHA-AWWA-WPCF (2018). One replicate of each sample was directly stored fresh at 140 -20 ºC for performing a DNA extraction and a real-time quantitative PCR analysis. 141 Analysis of chemical variables. Total carbon (TC) and nitrogen (TN) were analysed 142 using a TruSpec CHNS Elemental Determinator (Leco Corporation). Total organic 143 carbon (TOC) was determined as described in Method 2540 E (APHA-AWWA-WPCF, 144 2018). Total inorganic carbon (TIC) was calculated by the difference between TC and 145 TOC. Inorganic nitrogen (NH4+ and NO3ˉ) was measured after 2M KCl extraction 146 (Mulvaney, 1996) using a Jasco V630 spectrophotometer. Total organic nitrogen (TON) 147 was calculated by the difference between TN and inorganic nitrogen. 148 Based on the methodology described in a previous study (Unda-Calvo et al, 2017) to 149 estimate the potential for metals to cause non-carcinogenic harmful effects in exposed 150 people (children) to contaminated surface sediments, hazard quotient (HQ) was 151 calculated as shown in the following equation: 152 HQ=  /  (1) 153 Chemical daily intake (CDI) represents the possible entry of metals (mg kg-1 day-1) 154 into the human body, B is the percentage of bioaccessible metal pseudo-content in surface 155 sediments when human gastric or intestinal environmental conditions were reproduced, 156 and RfD is the estimated amount of the daily oral exposure level for the population that 157 is likely not to have an appreciable risk of deleterious effects during its lifetime. If the 158 sum of HQ corresponding to the gastric and intestinal bioaccessibilities (∑HQ) for at least 159 one of the metals (Cu, Zn, Cr, Ni and Pb) considered at each site exceeds 1, the surface 160 sediment may be a concern for potential harmful effects. 161 Analysis of biological variables. After DNA extraction from surface sediment samples 162 (0.25 g of dry weight sediment) using Power SoilTM DNA Isolation Kit, real-time qPCR 163 (qPCR) was carried out for denitrifying genes (nirK, nirS and nosZ) measurements as 164 described in Martínez-Santos et al. (2018). The ratio nosZ:nir was calculated, where nir 165 corresponds to the sum of nirK and nirS. 166 Additionally, the activity of Nitrate Reductase (NR) –the enzyme responsible for 167 reducing NO3to NO2in denitrification– for each surface sediment sample was analysed 168 following the procedure established in a previous study (Unda-Calvo et al., 2019a). Since 169 NR activity was considered the main limiting step of denitrification, and in order to 170 estimate the capacity of the microbial communities harboured in the surface sediments 171 for cumulative nitrate complete elimination, the authors proposed the calculation of the 172 Response Time as shown in the following equation: 173 Response Time (min)=    (  )  (    ) (2) 174 All analytical procedures for both water and surface sediment samples were performed 175 in triplicate. 176 2.3 Statistical analysis for river quality assessment 177 All statistical processing of the data described below were performed using SPSS 22.0 178 software. 179 2.3.1 Variables and reference conditions selection criteria 180 In general, similar environmental variables were involved in the quality index 181 developed in different reports (Şener et al., 2017; Wang et al., 2017; Birch, 2018). 182 However, since this research aims at evaluating the influence of anthropogenic 183 contamination on water and surface sediment quality, EC, DOC, nutrients (PO43-, NO3-, 184 NO2-, NH4+) and dissolved metals (Cu, Zn, Pb, Ni and Cr) were selected as indicators of 185 water quality; instead, TOC, TIC, NO3-, NH4+, TON and hazardous quotient (∑HQ) of 186 various metals (Cu, Zn, Pb, Ni and Cr) were used for the development of sediment quality 187 index. All these variables are known to alter biological quality of rivers, have a reduced 188 analytical cost and help to identifying the impact of human activities. 189 Firstly, all data were log-transformed in order to reduce the skewness. After applying 190 the Levene’s test to confirm which variables had equality of variance or not, one-way 191 ANOVA (taking ρ < 0.05 as significant, in accordance with Tukey’s multiple range test) 192 and the U-Mann Whitney non-parametric test were performed, respectively. Examination 193 of significant differences among sampling sites helped us to recognize the anthropogenic 194 origin of variables. In addition, a Spearman correlation analysis (non-parametric test) was 195 performed to identify common sources. 196 The establishment of reference conditions is an essential second step since the quality 197 of any sampling location must be expressed as a deviation with respect to those 198 conditions. According to WFD CIS guidance document No. 10 (REFCOND, 2003), 199 reference conditions shall be represented by values of the relevant biological quality 200 elements (i.e., composition and abundance of aquatic flora, composition and abundance 201 of benthic invertebrate fauna or composition, abundance and age structure of fish fauna) 202 in classification of ecological status. Alternatively, microorganisms are also particularly 203 suitable for the monitoring of river quality due to their key role in biogeochemical cycling 204 and their sensitivity and quick response to any ecosystem perturbation (Chaer et al., 2009; 205 Guo et al., 2012). 206 The nosZ:nir ratio represents the relative abundance of the nitrous oxide reductase 207 (nosZ) gene – responsible for the conversion from N2O to N2 – with respect to the nitrite 208 reductase genes (nir = nirS + nirK) -responsible for the reduction of NO2to NO. Whereas 209 nirS and nirK are considered to be universal to all denitrifiers (Azziz et al., 2017), nosZ 210 gene lack was observed (Jones et al., 2008; Ligi et al., 2014). Consequently, a low 211 nosZ:nir ratio will suggest that N2O greenhouse gas emissions could be expected as a 212 result of an incomplete denitrification. In addition, since the presence of denitrifying 213 bacteria does not necessarily imply that it is operating (Veraart et al., 2017), the Response 214 Time was also used to ensure the microbial community capacity to completely eliminate 215 the nitrate excess. Therefore, we used a combination of these parameters involved in 216 microbial denitrification in surface sediments, –the process by which NO3and NO2are 217 reduced to NO and N2O, and finally are converted to N2 and returned to the atmosphere 218 –, as river biological quality indicator for reference conditions establishment. 219 Zn-Pb-Cu (∑HQ) in surface sediments. As observed, metal availability and, hence, metal 365 bioaccessibility in sediments modulates their toxicity. In fact, while Cr-Ni were 366 predominantly retained in the mineral lattice of surface sediments, Pb was the most 367 available metal (Unda-Calvo et al., 2019b). 368 According to water QI spatial distribution (Fig. 4A), the highest median value was 369 detected at headwaters (D1, E1), and at the Mazmela (M1) and Oñati (O1) tributaries, 370 while the lowest median value was at E2, both for physicochemical variables (I) and 371 specific pollutants (II). Despite the high impact of industrial activities, it is especially 372 noticeable the improvement of the Oñati tributary water quality to a level similar to those 373 non-impacted sites (D1, M1 and E1), also registered by the Basque Water Board (URA, 374 2018). However, the water quality was better when physicochemical variables were 375 considered (all samples at O1 had a QI value above 0.5) than with specific pollutants 376 (almost 25% of samples at O1 had QI value below 0.5). 377 Regarding contamination source type (Fig. 1), effluents from WWTPs and, 378 primordially, untreated UWW seem to have more negative effects on water 379 physicochemical quality than exclusively industrial wastewaters. In fact, median water 380 QI based on physicochemical variables was higher at D2-D4-D6 than at D3-D5-D7 and, 381 especially, at E2 (Fig. 4A). In contrast to D2-D4-D6, more than 75% of samples had a QI 382 value below 0.5 at D3-D5-D7 and at E2. Moreover, samples percentage with QI value 383 below 0.5 increased from less than 25% at D2 to more than 25% at D4 and to more than 384 50% at D6, due to the indirect impact of the Mekolalde WWTP and the Ego tributary, 385 respectively. Conversely, adverse effects of specific pollutants on water quality could not 386 be distinguished based on contamination source type. Compared to a 20 % of deviation 387 among physicochemical QI median values, D2-D3-D4-D5 showed a higher homogeneity 388 (almost 3% of deviation among median values) concerning specific pollutants and, 389 therefore, the negative effects of industrial activities or WWTPs becomes indiscernible. 390 Surface sediment QI shows the same spatial trend as water (Fig. 4A), where the 391 highest median values were exhibited at headwaters (D1 and E1) while the lowest 392 median values was found at E2, both for physicochemical variables (I) and specific 393 pollutants (II). Moreover, the influence of anthropogenic activities on surface sediment 394 quality was more pronounced. Regarding effluents from WWTPs, similar water quality 395 was detected downstream of the Epele and Mekolalde WWTPs (D3 and D5, 396 respectively), and lower downstream of Apraitz WWTP (D7) due to the Ego tributary 397 confluence. However, the increase of TIC, NO3and NH4+ content in surface sediments 398 downstream of Epele WWTP (Table S2) was so considerable that the physicochemical 399 quality was lower at D3 (QI < 0.5) and, even at D4, than at D7 (Fig. 4A). In addition, 400 specific pollutants load from industrial activities extremely deteriorated surface 401 sediment quality at D2 and D4 (75% and 100% of QI reduction compared to D1 and 402 D3, respectively). 403 3.3 Effects of seasonality and sediment dynamics on water quality 404 The influence of the hydrological conditions on the quality of the Deba River was also 405 evaluated. Overall, the water quality was better from January to March than from May to 406 November, as exhibited by the 25th percentile of QI values calculated for the wet and the 407 dry seasons, respectively (Fig. 4A). However, this is not true at those sampling sites (D1, 408 M1, D2, O1 and E1) where higher median values of the most influencing environmental 409 variables (PO43--NO3--NO2--NH4+) were observed for the wet season (Table S1). Despite 410 winter rainfalls increased river discharge measured in the Altzola gauging station (R2 = 411 0.65; Fig. 1) and, consequently, might favour contamination dilution, previous findings 412 suggested that the shallow depth of the water column together with the abrupt slope at 413 just upstream from these sites (Fig. 1) could promote higher sediment resuspension 414 (Unda-Calvo et al., 2019b). Resuspension together with changes in the pH or the 415 oxidation conditions of the river environment caused by floods could promoted the 416 release of elements (e.g. organic matter, N, P or metals) trapped in surface sediments to 417 the overlying water (Zhu et al., 2017; Camino Martín-Torre et al., 2017; Petranich et al., 418 2018), contributing to its quality decline. In fact, a previous study (García-García et al., 419 2019) focused on evaluating the variability of particulate metal pollution during flood 420 events in the Deba River catchment established hydrodynamic processes as main factor 421 controlling the behaviour of particulate metals. 422 Sediment plays an important role in contamination transfer between aquatic 423 environment compartments, acting as a sink or source for many pollutants. For a more 424 accurate evaluation of the kindness of surface sediment analysis for a rapid water quality 425 monitoring, linear regressions were applied between water and sediment QIes during two 426 hydrological sets (Fig. 4B). As opposed to physicochemical variables (I), a flux of 427 specific pollutants (II) in the sediment-water interface could be well identified, especially 428 for the dry season. Metals are nonbiodegradable and, consequently, long persistent; 429 therefore, physical and biochemical processes only could control their mobility (Violante 430 et al., 2010), simplifying the comprehension of metal exchange at the water-sediment 431 interface. Conversely, organic matter and nutrients are subjected to a continuous 432 transformation in river environment; thus, several processes should be also considered for 433 a realistic modelling of organic matter and nutrient fluxes between water and sediment 434 (Thouvenot et al., 2007). This fact illustrates how an annual determination of 435 bioaccessible metal in surface sediments can be applied as an appropriate strategy for 436 monitoring metal-dependent water quality, and for identifying those sites which could 437 deserve special attention, especially during the dry period in which the river quality might 438 be severely compromised. 439 It should be noted that surface sediments underestimate water quality based on specific 440 pollutants (II), except at D1, D3 and E1 (Fig. 4B). The high capacity of surface sediments 441 to adsorb metals from the overlying water has been extensively discussed (Li et al., 2014; 442 Lundy et al., 2017; Chu et al., 2019), and organic matter content has been reported as a 443 key factor controlling metal adsorption (Yang et al., 2010). Indeed, Zn, Cu and Cr 444 adsorption onto surface sediments, which were notably bound to the oxidizable fraction 445 (from 17.7% to 30.3% of the pseudo-total content; Unda-Calvo et al., 2019b), seems to 446 be favoured by a positive organic matter gradient between water and sediments (Fig. S2). 447 The good linear relationship observed between physicochemical variables-dependent and 448 specific pollutants-dependent QIes for water samples during the dry season (Fig. 5A) 449 suggests a common source of both environmental variables categories along the 450 catchment, posing the physicochemical ones a higher threat to the water quality 451 downstream of D2 and in the Ego tributary. Conversely, this behavioural pattern changed 452 during the wet season to another closely related to surface sediments, where specific 453 pollutants greatly conditioned their quality (Fig. 5B). The alteration on metal performance 454 with river hydrological conditions evidences that surface sediments act as sink of 455 dissolved metals during sedimentation in the dry season, whereas there is potential for 456 mobilization of available metals in surface sediments to the overlying water during 457 sediment resuspension in the wet season. 458 For identifying the key environmental variable responsible for the decline of water and 459 surface sediment quality along the river during the dry season, the contribution percentage 460 of each variable was calculated (Fig. 6). Sampling sites with water QI (25th percentile of 461 values) above 0.5 for the dry season (D1, M1, D2, O1 and E1; Fig. 4A) presented positive 462 contributions of almost all physicochemical variables, except for EC (specially at D2) and 463 DOC (mainly at E1). While groundwater circulation from evaporitic rocks was identified 464 as responsible of high EC values from D1-M1 to D2, soil erosion and the subsequent 465 organic matter solubilization could originate high DOC contents in water at E1. On the 466 other hand, sampling sites with water QI (25th percentile of values) below 0.5 for the dry 467 season (D3, D4, D5, E2, D6 and D7; Fig. 4A) presented negative contributions of all 468 physicochemical variables, being DOC and inorganic nitrogen (NO3-, NO2or NH4+) the 469 key factors declining water quality. Each contamination source type (Fig. 1) seems to 470 differently alter nitrogen cycle along the river. While NO3content in water at sites 471 immediately downstream of WWTPs (D3, D5 and D7) are of greater concern, NH4+ and, 472 primordially, NO2from untreated UWW deteriorated water quality at E2. Those sites 473 with direct impact of industrial activities but indirect influence of WWTPs effluents (D4) 474 or UWW (D6) presented a combination of high NO3-+NO2negative contribution to water 475 quality (both between 20-30%; Fig. 6). 476 Regarding specific pollutants, all sampling sites except D1 presented negative 477 contribution of some metal. Despite their non-polluted nature, headwaters from the 478 Mazmela (M1) and the Ego (E1) tributaries presented negative CP of Pb to water quality, 479 possibly as a result of the solubilization of mobile Pb in surface sediments. Among the 480 sites with water QI (25th percentile of values) below 0.5 for the dry season (D4, D5, E2, 481 D6 and D7; Fig. 4A), those which showed high negative CP of Cr+Cu+Zn (at least 50%; 482 Fig. 6) had the worst water quality. 483 Only surface sediments from D3 and E2 showed physicochemical QI values below 0.5 484 (Fig. 4A), being NO3the main physicochemical variable contributing to the decline of 485 sediment quality. The high Time Response and the low nosZ:nir ratio measured at these 486 sites (Table S2) indicate a low potential for denitrifying the excess of nitrates. On the 487 other hand, surface sediments from D2, D4, D5, E2 and D7 presented a low quality (QI 488 < 0.5; Fig. 4A), mainly due to the high negative CP for Cu, Zn and, principally, Pb (>50% 489 of negative contribution at D4 and D5). 490 In conjunction with the QI, calculating the CP of environmental variables to river 491 quality also becomes an essential tool for basin managers to specifically evaluate the 492 quality decline at each sampling site. In fact, despite the low influence (wj; Table 2) of 493 DOC and dissolved Pb on water quality with respect to other environmental variables 494 involved in QI calculation, they were the main cause of the bad quality registered at D6 495 and D7 (Fig. 6). 496 4. Conclusions 497 Recognition of the susceptibility of a catchment subjected to multiple human pressures 498 is essential for an effective decision-making of managers, whose mission aims to 499 achieving a sustainable development of an urban ecosystem together with the compliance 500 of the environmental objectives established by the European Water Framework Directive 501 (WFD, 2000/60/CE). In this context, a multimetric index was a useful tool for evaluating 502 the combined effect of various environmental indicators on river quality. Performing a 503 PCA for the quality index development allowed us (i) to assign a weight or relative 504 influence on the overall quality to each environmental indicator according to the statistical 505 distribution (mean and standard deviation) of all monitored data, and (ii) to express the 506 quality as a deviation with respect to a reference conditions, as required by the WFD 507 (2000/60/CE). 508 The discharge of industrial, WWTP and, mainly, untreated UWW effluents into the 509 Deba River catchment increased organic matter, nutrient and metal content in water and 510 surface sediments, hence contributing to a lower QI, especially in the Ego tributary, and 511 midand downstream of the main river. On the other hand, high flows helped to dilute 512 contamination and, therefore, higher water QI was observed from January to March. 513 Hydrological conditions also seemed to induce different behavioural pattern of sediments, 514 acting as metal sink in the dry season, whereas there is potential for metal mobilization 515 to water during sediment resuspension in the wet season. Therefore, an annual 516 determination of surface sediments QI becomes suitable for monitoring water quality 517 during the dry season, identifying those sites which could deserve special attention, and 518 planning future strategies for river quality improvement. Two limitations were found: (1) 519 surface sediment was not appropriate for water physicochemical quality monitoring due 520 to organic matter and nutrient continuous transformation; and (2) a multimetric index did 521 not provide a concise and definitive quality information, thus the calculation of both the 522 QI and the CP of environmental variables was proposed for specifically evaluate the water 523 and surface sediment quality at each location. 524 Acknowledgements 525 The authors wish to thank the Ministry of Economy and Competitiveness (CTM2014-526 55270-R), the Basque Government (Consolidated Group of Hydrogeology and 527 Environment, IT1029-16) and the University of the Basque Country (UPV-EHU, 528 UFI11/26) for supporting this research. 529 References 530 Alloway, B.J., Jackson, A.P., 1991. The behaviour of heavy metals in sewage sludge-amended soil. Sci. Total Environ. 100, 151–176. 531 APHA-AWWA-WPCF (American Public Health Association, American Water Works Association & Water Pollution Control 532 Federation), 2018. Standard Methods for the Examination of Water and Wastewater. Am. Public Assoc., Washington, DC. 533 Available online at: https://www.standardmethods.org/about/ 534 Azziz, G., Monza, J., Etchebehere, C., Irisarri, P., 2017. nirSand nirK-type denitrifier communities are differentially affected by soil 535 type, rice cultivar and water management. Eur. J. Soil Biol. 78, 20-28. https://doi.org/10.1016/j.ejsobi.2016.11.003. 536 Banu, Z., Chowdhury, M.S.A., Delwar, M.H., Nakagami, K., 2013. Contamination and ecological risk assessment of heavy metal in 537 the sediment of Turag River, Bangladesh: an index analysis approach. J. Water Resour. Prot. 5, 239–248. 538 https://doi.org/10.4236/jwarp.2013.52024 539 Bartoli, G., Papa, S., Sagnella, E., Fioretto, A., 2012. Heavy metal content in sediments along the Calore river: relationships with 540 physical–chemical characteristics. J. Environ. Manag. 91, S9–S14. http://dx.doi.org/10.1016/j.jenvman.2011.02.013. 541 Birch, G.F., 2018. A review of chemical-based sediment quality assessment methodologies for the marine environment. Mar. Pollut. 542 Bull. 133, 218-232. https://doi.org/10.1016/j.marpolbul.2018.05.039 543 Brown, R.M., McClelland, N. I., Deininger, R.A., Tozer, R.G., 1970. A Water Quality Index: Do We Dare? Water and Sewage Works, 544 117(10), 339-343. 545 Camino Martín-Torre, M., Cifrian, E., Ruiz, E., Galán, B., Viguri, J.R., 2017. Estuarine sediment resuspension and acidification: 546 Release behaviour of contaminants under different oxidation levels and acid sources. J. Environ. Manage. 199, 211-221. 547 http://dx.doi.org/10.1016/j.envman.2017.05.044 548 Chaer, G.M., Myrold, D.D., Bottomley, P.J., 2009. A soil quality index based on the equilibrium between soil organic matter and 549 biochemical properties of undisturbed coniferous forest soils of the Pacific Northwest. Soil. Biol. Biochem. 41, 822-830. 550 https://doi.org/10.1016/j.soilbio.2009.02.005. 551 Chu, Z., Gu, W., Li, Y., 2019. Adsorption Mechanism of Heavy Metals in Heavy Metal/Pesticide Coexisting Sediment Systems 552 through Factorial Design Assisted by 2D-QSAR Models. Pol. J. Environ. Stud. 27(6), 2451-2461. 553 https://doi.org/10.15244/pjoes/80962 554 Cude, C. G., 2001. Oregon Water Quality Index: A Tool for Evaluating Water Quality Management Effectiveness. J. Am. Water 555 Resour. Assoc. 37(1), 125-137. https://doi.org/10.1111/j.1752-1688.2001.tb05480.x. 556 Devesa-Rey, R., Díaz-Fierros, F., Barral, M.T., 20120. Trace metals in river bed sediments: an assessment of their partitioning and 557 bioavailablity by using multivariate exploratory analysis. J. Environ. Manag. 91, 2471-2477. 558 http://dx.doi.org/10.1016/j.jenvman.2010.06.024 559 Finotti, A.R., Finkler, R., Susin, N., Schneider, V.E., 2015. Use of water quality index as a tool for urban water resources management. 560 Int. J. Sus. Dev. Plann. 10(6), 781-794. https://doi.org/10.2495/SDP-V10-N6-781-794 561 García-García, J., Ruiz-Romera, E., Martínez-Santos, M-, Antigüedad. I., 2019. Temporal variability of metallic properties during 562 flood events in the Deba River urban catchment (Basque Country, Northern Spain) after the introduction of sewage treatment 563 systems. Environ. Earth Sci. 78, 1-23, https://doi.org/10.1007/s12665-018-8014-1 564 Gitau, M., Chen, J., Ma, Z., 2016. Water Quality Indices as Tools for Decision Making and Management. Water Resour. Manag. 565 30(8), 2591-2610. https://doi.org/10.1007/s11269-016-1311-0 566 Guo, H., Yao, J., Cai, M., Qian, Y., Guo, Y., Richnow, H.H., et al., 2012. Effects of petroleum contamination on soil microbial 567 numbers, metabolic activity and urease activity. Chemosphere 87, 1273-1280. https://doi.org/10.1016/j. 568 Herrero, A., Vila, J., Eljarrat, E., Ginebreda, A., Sabater, S., Batalla, R.J., Barceló, D., 2018. Transport of sediment borne contaminants 569 in a Mediterranean river during a high flow event. Sci. Total Environ. 633, 1392-1402. 570 https://doi.org/10.1016/j.scitotenv.2018.03.205 571 Hooper, R.P., 2003. Diagnostic tools for mixing models of stream water chemistry. Water Resour. Res. 39, 1055. 572 https://doi.org/10.1029/2002WR001528, 3. 573 House, M.A., 1989. A water quality index for river management. Water Environ. J. 3(4), 336-344. https://doi.org/10.1111/j.1747-574 6593.1989.tb01538.x 575 Jones, C.M., Stres, B., Rosenquist, M., Hallin, S., 2008. Phylogenetic analysis of nitrite, nitric oxide, and nitrous oxide respiratory 576 enzymes reveal a complex evolutionary history for denitrification. Mol. Biol. Evol. 25, 1955-1966. 577 https://doi.org/10.1093/molbev/msn146 578 Kabir, M.I., Lee, H., Kim, G., Jun, T., 2011. Correlation assessment and monitoring of the potential pollutants in the surface sediments 579 of Pyeongchang River, Korea. Int. J. Sediment Res. 26 (2), 152–162. https://doi.org/10.1016/S1001-6279(11)60083-8 580 Kausik, A., Kansal, A., Santosh, Meena, Kumari, S., Kaushik, C.P., 2009. Heavy metal contamination of river Yamuna, Haryana, 581 India: Assessment by Metal Enrichment Factor of the Sediments. J. Hazard. Mater, 164(1), 265-270. 582 https://doi.org/10.1016/j.jhazmat.2008.08.031 583 Li, S., Zhang, C., Li, Y., 2014. Adsorption of multi-heavy metals Zn and Cu onto surficial sediments: modelling and adsorption 584 capacity analysis. Environ. Sci. Pollut. Res. Int. 21(1), 399-406. https://doi.org/10.1007/s11356-013-1916-2. 585 Ligi, T., Truua, M., Truua, J., Nõlvaka, H., Kaasikb, A., Mitsch, W.J., Mander, U., 2014. Effects of soil chemical characteristics and 586 water regime on denitrification genes (nirS, nirK, and nosZ) abundances in a created riverine wetland complex. Ecol. Eng. 72, 587 47-55. https://doi.org/10.1016/j.ecoleng.2013.07.015 588 Lundy, L., Alves, L., Revitt, M., Wildeboer, D., 2017. Metal Water-Sediment Interactions and Impacts on an Urban Ecosystem. Int. 589 J. Environ. Res. Public Health 14(7):722. https:/doi.org/10.3390/ijerph14070722 590 Martínez-Santos, M., Lanzén, A., Unda-Calvo, J., Martín, I., Garbisu, C., Ruiz-Romera, E., 2018. Treated and untreated wastewater 591 effluents alter river sediment bacterial communities involved in nitrogen and sulphur cycling. Sci. Total Environ. 633, 1051-592 1061. https://doi.org/10.1016/j.scitotenv.2018.03.229 593 Martínez-Santos, M., Probst, A., García-García, J., Ruiz-Romera, E., 2015. Influence of anthropogenic inputs and a high-magnitude 594 flood event on metal contamination pattern in surface bottom sediments from the Deba River urban catchment. Sci. Total 595 Environ. 514, 10-25. https://doi.org/10.1016/j.scitotenv.2015.01.078. 596 Mulvaney, R.L., 1996. Nitrogen-Inorganic Forms. In: Sparks, D.L., Page, A.L., Helmke, P.A., et al. (Eds.), Methods of Soil Analysis: 597 Part 3. Chemical Methods, Soil Science Society of America Book Series no Vol. 5. Soil Science Society of America, Inc., 598 Madison, WI, pp. 1123-1184. 599 Nowrouzi, M., Pourkhabbaz, 2014. Application of geoaccumulation index and enrichment factor for assessing metal contamination 600 in the sediments of Hara Biosphere Reserve, Iran. Chem. Spec. Bioavailab. 26(2), 99-105. 601 https://doi.org/10.3184/095422914X13951584546986 602 Petranich, E., Covelli, S., Acquavita, A., Vittor, C.D., Faganeli, J., Contin, M., 2018. Benthic nutrient cycling at the sediment-water 603 interface in a lagoon fish farming system (northern Adriatic Sea, Italy). Sci. Total Environ. 644, 137-149. 604 http://dx.doi.org/10.1016/j.scitotenv.2018.06.310 605 Primpas, I., Tsirtsis, G., Karydis, M., Kokkoris, G.D., 2010. Principal component analysis: Development of a multivariate index for 606 assessing eutrophication according to the European water framework directive. Ecol. Indic. 10(2), 178-183. 607 https://doi.org/10.1016/j.ecolind.2009.04.007 608 REFCOND, Working Group 2.3 from Common Implementation Strategy for the Water Framework Directive (2000/60/EC). Guidance 609 Document No 10. Rivers and Lakes – Typology, Reference Conditions and Classification Systems. Available online at: 610 http://ec.europa.eu/environment/water/water-framework/ 611 Richardson, D.J., 2000. Bacterial respiration: a flexible process for a changing environment. Microbiology 146:551–571. https://doi. 612 org/10.1099/00221287-146-3-551. 613 Rügner, H., Schwientek, M., Egner, M., Grathwohl, P., 2014. Monitoring of event-based mobilization of hydrophobic pollutants in 614 rivers: Calibration of turbidity as a proxy for particle facilitated transport in field and laboratory. Sci. Total Environ. 490, 191-615 198. https://doi.org/10.1016/j.scitotenv.2014.04.110 616 Selle, B., Schwientek, M., Lischeid, G., 2013. Understanding processes governing water quality in catchments using principal 617 component scores. J. Hydrol. 486, 31-38. https://doi.org/10.1016/j.jhydrol.2013.01.030 618 Şener, Ş., Şener, E., Davraz, A., 2017. Evaluation of water quality using water quality index (WQI) method and GIS in Aksu River 619 (SW-Turkey). Sci. Total Environ. 584-585, 131-144. http://doi.org/10.1016/j.scitotenv.2017.01.102 620 SRDD, 1976. Development of a water quality index. Scottish research development department, applied research & development 621 report number ARD3 (p. 61). Edinburg, UK: Engineering Division. 622 Sutadian, A.D., Muttil, N., Yilmaz, A.G., Perera, B.J.C., 2016. Development of river water quality indices-a review. Environ. Monit. 623 Assess. 188:58. https://doi.org/10.1007/s10661-015-5050-0. 624 Thouvenot, M., Billen, G., Garnier, J., 2007. Modelling nutrient exchange at the sediment-water interface of river systems. J. Hydrol. 625 341(1-2), 55-78. https://doi.org/10.1016/j.jhydrol.2007.05.001 626 Tokatli, 2017. Bioecological and statistical risk assessment of toxic metals in sediments of a worldwide important wetland: Gala Lake 627 National Park (Turkey). Arch. Environ. Prot. 43(1), 34-47. https://doi.org/10.1515/aep-2017-0007 628 Unda-Calvo, J., Martínez-Santos, M., Ruiz-Romera, E., 2017. Chemical and physiological metal bioaccessibility assessment in surface 629 bottom sediments from the Deba River urban catchment: Harmonization of PBET, TCLP and BCR sequential extraction 630 methods. Ecotox. Environ. Safe. 138, 260-270. http://dx.doi.org/10.1016/j.ecoenv.2016.12.029 631 Unda-Calvo, J., Martínez-Santos, M., Ruiz-Romera, E., Lechuga-Crespo, J.L., 2019a. Implications of denitrification in the ecological 632 status of an urban river using enzymatic activities in sediments as an indicator. J. Environ. Sci. 75, 255-268. 633 https://doi.org/10.1016/j.jes.2018.03.037 634 Unda-Calvo, J., Ruiz-Romera, E., Fdez-Ortiz de Vallejuelo, S., Martínez-Santos, M., Gredilla, A., 2019b. Evaluating the role of 635 particle size on urban environmental geochemistry of metals in surface sediments. Sci. Total Environ. 646, 121-133. 636 https://doi.org/10.1016/j.scitotenv.2018.07.172 637 URA (Agencia Vasca del Agua – Ur Agentzia), 2008. Establecimiento de objetivos de calidad relativos a indicadores fisicoquímicos 638 generals en los ríos de la CAPV según la directive 2000/60/CE. Available online at: http://www.uragentzia.euskadi.eus 639 URA (Agencia Vasca del Agua – Ur Agentzia), 2013. Red de Seguimiento del Estado Biológico de los Ríos de la Comunidad 640 Autónoma del País Vasco. Informe de resultados. Campaña 2013. Available online at: http://www.uragentzia.euskadi.eus 641 URA (Agencia Vasca del Agua – Ur Agentzia), 2018. Red de seguimiento del Estado Biológico de los Ríos de la Comunidad 642 Autónoma del País Vasco. Informe de resultados. Campaña 2017. Available at: http://www.uragentzia.euskadi.eus 643 USEPA, 2001: Methods for collection, storage and manipulation of sediments for chemical and toxicological analyses: technical 644 manual. EPA-823-B-01-002. Washington, DC. Available online at: http://nepis.epa.gov 645 Veraart, A.J., Dimitrov, M.R., Schrier-Ujil, A.P., Smidt, H., de Klein, M., 2017. Abundance, activity and community structure of 646 denitrifiers in drainage ditches in relation to sediment characteristics, vegetation and land-use. Ecosystems 20(5), 928-943. 647 https://doi.org/10.1007/s10021-016-0083-y. 648 Violante, A., Cozzolino, V., Perelomov, L., Caporale, A.G., Pigna, M., 2010. Mobility and bioavailability of heavy metals and 649 metalloids in soil environments. J. Soil. Sci. Plant Nutr. 10(3), 268-292. http://dx.doi.org/10.4067/S0718-95162010000100005 650 Wang, J., Liu, G., Liu, H., Lam, P.K.S., 2017. Multivariate statistical evaluation of dissolved trace elements and a water quality 651 assessment in the middle reaches of Huaihe River, Anhui, China. Sci. Total Environ. 583, 421-431. 652 http://doi.otg/10.1016/j.scitotenv.2017.01.088 653 WFD, 2000/60/EC. Directive 2000/60/EC of the European Parliament and of the Council of 23 October 2000 Establishing a 654 Framework for Community Action in the Field of Water Policy. European Parliament, Council of the European Union. Available 655 at: http://data.europa.eu/eli/dir/2000/60/oj 656 Yang, X., Xiong, B., Yang, M., 2010. Relationships among Heavy Metals and Organic Matter in Sediment Cores from Lake Nanhu, 657 an Urban Lake in Wuhan, China. J. Freshw. Ecol. 25(2), 243-249. https://doi.org/10.1080/02705060.2010.9665074 658 Yisa, J. and Jimoh, T., 2010. Analytical Studies on Water Quality Index of River Landzu. Am. J. Appl. Sci. 7(4), 453-458. 659 https:/doi.org/ 10.3844/ajassp.2010.453.458 660 Zheng, Na., Wang, Q., Liang, Z., Zheng, D., 2011. Characterization of heavy metal concentrations in the sediments of three freshwater 661 rivers in Huludao City, Northeast China. Environ. Poll. 154(1), 135-142. https://doi.org/10.1016/j.envpol.2008.01.001 662 Zhu, L., Li, X., Zhang, C., Duan, Z., 2017. Pollutants’ Release, Redistribution and Remediation of Black Smelly River Sediment 663 Based on Re-Suspension and Deep Aeration of Sediment. Int. J. Environ. Res. Public Health 14(4), 374. 664 https://doi.org/10.3390/ijerph14040374 665 Zumft,W.G., 1997. Cell biology and molecular basis of denitrification. Microbiol. Mol. Biol. Rev. 61, 533–616. 666 FIGURE CAPTIONS 667 Fig. 1 Location of sampling sites, main urban areas, Altzola gauging station, untreated 668 urban wastewater (UWW) discharge, and wastewater treatment plants (WWTPs) in the 669 Deba River catchment. Table summarizes the sampling type and the most important 670 information of the sampling sites: location in the catchment and the impact of different 671 contamination sources in each site (according to the results obtained in the spatial 672 distribution of quality index (QI) in the Fig. 4). The height profile of the catchment, and 673 the water discharge (Q) and precipitation (P) evolution during the research period are also 674 included. 675 Fig. 2 Flow diagram summarizing all the steps for the classification of the contribution 676 of an environmental variable to water or surface sediment quality. 677 Fig. 3 Relationship between two biological variables related to denitrifying community 678 abundance (nosZ:nir) and activity (Response Time) for the reference conditions 679 establishment. 680 Fig. 4 Spatial distribution of water and surface sediment QI along the catchment (A), and 681 linear relationship between them (B) when physicochemical variables (I) or specific 682 pollutants (II) were taken into account. For water, each box in (A) shows the 25th, 50th 683 and 75th percentiles of values calculated for the research period Jan’15 - Jan16’. The 25th 684 percentile of values calculated for the dry (circles) and wet (triangles) seasons are also 685 plotted in (A) and used for linear relationships in (B). 686 Fig. 5 Linear relationships between QI related to physicochemical variables and specific 687 pollutants in water samples during the dry and wet seasons (A), and in surface sediments 688 (B). For water, the 25th percentile values from Fig. 4B were used. Sampling sites within 689 the grey area are characterized by worse physicochemical variables-dependent QI than 690 specific pollutants-dependent QI. 691 II I D1 D2 D3 D4 D5 E1 E2 D7 D1 D2 D3 D4 D5 D7 E1 E2 D1 D2 D3 D4 D5 D7 E1 E2 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 Water QI Sediment QI Dry Season: R 2 = 0.56 Wet Season: R2= 0.45 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 D1 M1 D2 O1 D3 D4 D5 E1 E2 D6 D7 QI Water Sediment AB D1 D2 D3 D4 D5 D7 E1 E2 D1 D2 D3 D4 D5 D7 E1 E2 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 Water QI Sediment QI Dry Season ( ): R2= 0.73 Wet Season ( ): R2= 0.52 D1 D2 D3 D4 D5 E1 E2 D7 Water Sediment B Dry Season ( ): R2= 0.56 Wet Season ( ): R2= 0.45 A 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 D1 M1 D2 O1 D3 D4 D5 E1 E2 D6 D7 QI D1 D2 D3 D4 D5 D6 D7 E1 E2 M1 O1 D1 D2 D3 D4 D5 D6 D7 E1 E2 M1 O1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 Specific Pollutants QI Physicochemical variables QI A) D1 D2 D3 D4 D5 E1 E2 D7 R² = 0.32 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 Specific Pollutants QI Physicochemical variables QI B) R2= 0.78 R2= 0.57 Dry Season ( ): Wet Season ( ): 1 2 3 4 5 6 1 2 3 4 5 6 Positive Negative 0 ≤ 10% 10 ≤ 20% 0 ≤ 10% 10 ≤ 20% 20 ≤ 30% 20 ≤ 30% 30 ≤ 40% 30 ≤ 40% 40 ≤ 50% 40 ≤ 50% > 50% > 50% Cr Ni Pb Zn Cu Cr Ni Pb Zn Cu 2 1 2 2 5 1 1 6 2 1 4 1 2 1 3 2 6 6 4 2 4 6 6 5 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 D1 D2 D3 D4 D5 E1 E2 D7 Positive Contribution Negative Contribution 1111 1 111 11 1 1 1 1 1 2 22 2 2 4 4 66 1 1 11 1 1 1 2 2 4 3 5 5 66 6 3 2 1 1 5 1 1 5 5 1 5 1 3 3 2 3 3 6 1 6 3 4 5 2 4 1 1 3 4 1 3 3 4 1 1 1 1 1 1 1 Positive Contribution Negative Contribution D1 D2 D3 D4 D5 E1 E2 D7 1 1 1 1 2 2 3 333 3 45 5 5 1 1 11 1 11 1 1 1 1 1 1 2 3 3 3 3 4 4 66 55 4TON TON TOC TOC TIC TIC NO3NO3NH4+ NH4+ SEDIMENT 1 1 1 1 2 3 1 1 1 1 1 3 2 5 6 3 3 1 2 3 2 4 3 2 1 2 1 2 2 2 2 2 2 2 2 2 1 2 4 3 4 2 2 3 2 2 1 2 1 1 1 2 3 2 2 2 2 1 2 1 1 1 1 3 2 1 Positive Contribution Negative Contribution 1 3 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 44 3 3 3 2 2 2 2 2 1 1 1 1 D1 M1 D2 D3 O1 D4 D5 E1 E2 D6 D7 EC EC DOC DOC NO3PO43NO2NO2NH4+ NH4+ NO3PO43WATER 1 6 3 3 2 4 1 1 2 2 2 2 1 3 1 1 1 3 1 1 3 2 2 1 2 2 2 3 3 4 3 3 2 2 2 2 2 2 1 1 2 1 2 3 2 1 2 3 3 3 2 1 2 5 3 3 D1 M1 D2 D3 O1 D4 D5 E1 E2 D6 D7 Negative Contribution Positive Contribution 1 11 2 2 22 22 2 2 33 3 33 3 4 5 1 1 11 1 2 22 22 2 3 3 3 6 Cr Ni Pb Zn Cu Cr Ni Pb Zn Cu I II 1 1 2 2 1 2 2 5 3 3 12 4 4 3 4 1 3 4 3 3 2 1 1 1 1 3 4 2 2 3 3 3 2 64 4 1 3 3 55 I II Table 1. Parameters for the calculation of quality index (QI): mean (M j), standard deviation (σj), reference conditions (Very Bad Quality (VBQ) and Very Good Quality (VGQ)) and loading factors (lpc) from the PCA for the environmental variables measured in water and surface sediment samples from the Deba River catchment. Correlation factors, eigenvalues and explained variance for each Principal Component (PC) were also included. M j ± σj Reference Conditions Loading factors (lpc.j) Correlation factors VBQ VGQ PC1 PC2 PC1 PC2 WATER Physicochemical Variables EC 2.62 ± 0.219 2.66 2.43 0.068 -0.924 0.127 -0.965 DOC 0.584 ± 0.160 0.819 0.213 0.360 0.136 0.673 0.142 PO431.378 ± 0.622 2.53 0.918 0.489 0.133 0.913 0.139 NO32.77 ± 0.408 3.27 2.17 0.422 -0.246 0.789 -0.256 NO20.904 ± 0.554 2.35 0.279 0.483 -0.087 0.902 -0.091 NH4+ 1.66 ± 0.555 3.03 0.805 0.463 0.205 0.865 0.214 Eigenvalues 3.49 1.09 Explained variance 58% 18% Specific Pollutants Cu 0.319 ± 0.263 0.462 -0.602 0.420 -0.277 0.641 -0.285 Zn 0.837 ± 0.595 2.37 -0.602 0.582 -0.011 0.889 -0.012 Pb -0.437 ± 0.232 -0.129 -0.602 -0.186 -0.878 -0.284 -0.903 Ni 0.197 ± 0.469 0.35 -0.602 0.487 0.221 0.744 0.228 Cr -0.373 ± 0.414 0.79 -0.602 0.462 -0.321 0.706 -0.329 Eigenvalues 2.34 1.06 Explained variance 47% 21% SEDIMENT Physicochemical Variables TOC 1.28 ± 0.111 1.22 1.28 0.192 -0.657 0.303 -0.951 TIC 1.18 ± 0.313 1.61 0.702 0.480 0.340 0.757 0.493 NO30.933 ± 0.250 1.41 0.789 0.535 0.254 0.844 0.369 NH4+ 0.897 ± 0.369 1.56 0.512 0.617 -0.023 0.973 -0.033 TON 0.239 ± 0.233 0.143 0.214 0.256 -0.623 0.403 -0.903 Eigenvalues 2.49 2.10 Explained variance 50% 42% Specific Pollutants ∑HQCu 0.260 ± 0.250 0.0010 0.0040 -0.467 -0.379 -0.591 -0.554 ∑HQZn 0.422 ± 0.463 1.04 -0.498 -0.660 0.069 -0.834 0.101 ∑HQPb 0.761 ± 0.137 0.869 0.668 -0.580 0.315 -0.733 0.460 ∑HQNi 1.05 ± 0.206 1.10 1.25 0.100 0.580 0.126 0.847 ∑HQCr 1.381 ± 0.354 1.95 1.71 -0.010 0.645 -0.013 0.942 Eigenvalues 1.60 2.13 Explained variance 32% 43% Table 2 Constant term (K) and weights (wj) atrributed to selected environemntal variables (j) for water and surface sediment QI equations construction (Eq. 7). Standardized weights were also calculated: wj’=wjx(σj/σQI). WATER SEDIMENT Physicochemical variables wj w'j wj w'j K 1.733 [-] K 1.535 [-] EC -0.061 -0.054 TOC 0.219 0.075 DOC -0.296 -0.191 TIC -0.389 -0.374 PO43- -0.104 -0.261 NO3- -0.497 -0.382 NO3- -0.141 -0.233 NH4+ -0.309 -0.350 NO2- -0.117 -0.262 TON 0.041 0.028 NH4+ -0.109 -0.244 Specific Pollutants wj w'j wj w'j K 0.537 [-] K 2.031 [-] Cu -0.249 -0.350 ∑HQCu -0.468 -0.297 Zn -0.114 -0.363 ∑HQZn -0.446 -0.524 Pb -0.140 -0.174 ∑HQPb -1.449 -0.505 Ni -0.091 -0.228 ∑HQNi -0.044 -0.023 Cr -0.176 -0.390 ∑HQCr -0.133 -0.119 A: Euclidean distance between the orthogonal projection of the sampling site on the “Very Good-Bad Quality”axis and the VGQ point. B: Euclidean distance between the VBQ and VGQ points. “Very Good-Bad Quality”axis. C: Euclidean distance between the sampling site and the VGQ point. D: Euclidean distance between the sampling site and the VBQ point. D1 D1 D1 D2 D2 D2 D2 D2 D2 D2 D2 D2 D3 D3 D3 D3 D3 D3 D3 D3 D3 D4 D4 D4 D4 D4 D4 D4 D4 D4 D5 D5 D5 D6 D6 D6 D6 D6 D6D6 D6 D6 D7 D7 D7 E2 E2 E2 E2 E2 E2 E2 E2 E2 E1 E1 E1 E1 E1 E1 E1 E1 E1 M1 M1 M1 M1 M1 M1 M1 M1 M1 O1 O1 O1 O1 O1 O1 O1 O1 O1 VGQ VBQ -3.0 -2.0 -1.0 0.0 1.0 2.0 3.0 -4.0 -3.0 -2.0 -1.0 0.0 1.0 2.0 3.0 4.0 5.0 PC2 (18%) PC1 (58%) Dry Wet EC DOC PO43NO3NO2NH4+ PC2 (18%) PC1 (58%) 1 1 D1 D2 D3 D4 D5 E1 E2 [Cuw/Cuss]= 1.51[DOC/TOC] - 0.08 R² = 0.61 0.00 0.20 0.40 0.60 0.80 0.00 0.10 0.20 0.30 0.40 0.50 Cuw/Cuss DOC/TOC D1 D2 D3 D4 D5 E1 E2 D7 [Znw/Znss] = 0.69 [DOC/TOC] - 0.01 R² = 0.61 0.00 0.10 0.20 0.30 0.40 0.50 0.00 0.10 0.20 0.30 0.40 0.50 Znw/Znss DOC/TOC D1 D2 D3 D4 E1 E2 D7 [Crw/Crss] = 0.34 [DOC/TOC] + 0.009 R² = 0.54 0.00 0.10 0.20 0.30 0.40 0.50 0.00 0.10 0.20 0.30 0.40 0.50 Crw/Crss DOC/TOC D1 D2 D3 D4 D5 E2 D7 R² = 0.70 0 5 10 15 20 25 30 35 40 45 0 1 2 3 4 TIC (mg g-1) Alkalinity (mg CaCO3g-1) UWW WWTP Table S1. Median values of environmental variables measured in water samples of all sampling sites during the dry (Q < 12 m3 s-1) and wet (Q > 12 m3 s-1) seasons, and water quality reference conditions (VGQ or VBQ). For the dry season, maximum values are shown in bold and different uppercase letters within the same parameter indicate that medians are significantly different at ρ = 0.05 among sampling sites. Once all data were log-transformed in order to reduce the skewness and the Levene’s test confirmed which parameters had equality of variance (*) or not (**), one-way ANOVA (taking ρ < 0.05 as significant, in accordance with Tukey’s multiple range test) and the U-Mann Whitney non-parametric test were performed, respectively, to analyse the differences. EC** DOC** PO 4 3 - * NO 3 - * NO 2 - * NH 4 + * Cu** Zn* Pb** Ni** Cr** Season Site µS·cm - 1 mg·L - 1 µg P·L - 1 µg N·L - 1 µg N·L - 1 µg N·L - 1 µg·L - 1 µg·L - 1 µg·L - 1 µg·L - 1 µg·L - 1 Dry D1 746 abcd 2.85 ab 1.27 a 233 ab 1.57 a 10.4 ab 0.250 ab 4.11 abc 0.250 a 0.475 abcd 0.250 abc (N = 5) M1 956 bc 3.47 ab 4.90 a 243 b 2.38 a 11.0 b 0.920 b 1.67 bc 0.650 a 0.760 ac 0.250 b D2 1030 c 4.26 ab 10.3 ab 328 abc 6.22 ab 36.4 abc 1.11 bc 4.90 abc 0.730 a 1.64 ab 0.250 b D3 652 bc 4.73 ab 103 c 1918 e 12.2 bc 47.5 acd 1.48 ac 5.89 bd 0.580 a 3.25 bd 0.250 b O1 267 d 2.20 b 5.48 a 301 abd 2.86 a 11.6 b 1.26 abc 5.70 abc 0.660 a 0.690 c 0.250 b D4 393 ab 4.07 ab 38.9 bcd 1585 aef 12.3 bc 62.0 ce 1.41 ac 9.78 bd 0.250 a 2.30 abd 0.250 bc D5 511 abcd 4.22 ab 110 bcd 2116 cde 26.6 bcd 93.6 ce 1.65 abc 12.1 bde 0.250 a 7.28 abcd 2.45 abc E1 422 a 2.13 ab 10.5 ad 229 bf 3.59 ab 20.0 ab 0.550 b 0.650 c 0.780 a 0.250 c 0.250 b E2 420 a 5.92 a 182 c 1606 de 99.5 d 664 f 1.81 ac 78.8 e 0.610 a 1.55 ab 3.06 a D6 370 a 4.19 ab 56.6 bcd 1238 ae 21.4 cd 140 de 1.86 a 18.4 ade 0.250 a 6.33 d 0.900 c D 7 441 abcd 5.55 ab 119 bcd 2177 cde 27.3 bcd 96.9 cd 1.71 abc 22.5 abe 0.250 a 6.95 abcd 0.955 abc Wet D1 190 3.07 2.64 226 1.82 12.8 0.250 1.80 0.250 0.820 0.250 (N = 4) M1 407 3.92 9.72 468 3.48 27.3 1.59 3.60 0.250 2.53 0.250 D2 396 4.93 24.9 453 4.75 38.6 2.18 7.51 0.250 2.17 0.250 D3 359 4.89 53.4 456 9.37 78.7 2.12 4.86 0.250 2.61 0.250 O1 184 3.65 13.4 292 2.77 27.6 2.13 6.59 0.250 1.06 0.250 D4 276 3.35 25.8 533 6.30 46.2 1.61 6.18 0.250 1.70 0.250 D5 279 4.71 34.3 1027 8.95 31.2 1.63 24.3 0.250 3.60 0.250 E1 301 4.72 19.3 265 2.65 20.2 1.53 3.57 0.250 0.585 0.250 E2 312 3.97 45.1 634 13.8 140 2.65 8.60 0.250 1.35 1.29 D6 271 3.96 39.1 545 8.20 46.4 2.02 9.68 0.250 2.34 0.520 D 7 365 4.50 97.7 1043 27.5 120 1.63 24.3 0.250 3.60 0.250 VGQ 268 1.63 8.27 149 1.90 6.38 0.250 0.250 0.250 0.250 0.250 VBQ 454 6.58 342 1853 223 1079 2.9 232 0.743 2.24 6.22 Table S2. Environmental variables determined in the surface sediments at each sampling site in October 2015. (*) Sediment quality reference conditions: E1 (VGQ) and E2 (VBQ). TOC TIC NO3NH4+ TON ∑HQ (x103) Response Time nosZ:nir (x107) Cu Zn Pb Ni Cr Site mg·g-1 mg·g-1 µg N·g-1 µg N·g-1 mg·g-1 [-] [-] [-] [-] [-] min [-] D1 27.5 5.75 6.02 8.96 3.19 0.985 1.18 4.12 8.76 12.9 23.0 5.62 D2 18.4 14.4 5.97 4.26 1.89 4.19 2.37 6.37 6.58 10.9 24.2 3.10 D3 26.4 29.0 14.6 20.1 3.97 2.26 4.72 3.58 9.67 11.0 47.0 2.05 D4 15.9 17.3 8.39 7.31 1.28 2.16 4.91 8.09 27.9 46.5 30.0 2.54 D5 18.5 20.1 4.41 6.42 1.49 1.65 3.36 8.25 8.14 14.3 16.5 2.01 E1* 18.9 5.04 6.15 3.25 1.64 1.01 0.317 4.65 17.9 51.8 19.9 6.61 E2* 16.5 40.4 25.8 36.1 1.39 1.00 10.9 7.40 12.6 89.5 74.7 2.29 D7 12.8 14.8 9.38 3.57 0.787 3.60 3.16 5.65 9.08 23.4 33.3 2.85