**Assessment of Ciguatera and Other Phycotoxin-Related Risks in Anaho Bay (Nuku Hiva Island, French Polynesia): Molecular, Toxicological, and Chemical Analyses of Passive Samplers**

**Mélanie Roué 1,2,\*, Kirsty F. Smith 3, Manoella Sibat 4, Jérôme Viallon 2, Kévin Henry 2, André Ung 2, Laura Biessy 3, Philipp Hess 4, Hélène Taiana Darius 2 and Mireille Chinain 2**

Received: 2 April 2020; Accepted: 1 May 2020; Published: 13 May 2020

**Abstract:** Ciguatera poisoning is a foodborne illness caused by the consumption of seafood contaminated with ciguatoxins (CTXs) produced by dinoflagellates from the genera *Gambierdiscus* and *Fukuyoa*. The suitability of Solid Phase Adsorption Toxin Tracking (SPATT) technology for the monitoring of dissolved CTXs in the marine environment has recently been demonstrated. To refine the use of this passive monitoring tool in ciguateric areas, the effects of deployment time and sampler format on the adsorption of CTXs by HP20 resin were assessed in Anaho Bay (Nuku Hiva Island, French Polynesia), a well-known ciguatera hotspot. Toxicity data assessed by means of the mouse neuroblastoma cell-based assay (CBA-N2a) showed that a 24 h deployment of 2.5 g of resin allowed concentrating quantifiable amounts of CTXs on SPATT samplers. The CTX levels varied with increasing deployment time, resin load, and surface area. In addition to CTXs, okadaic acid (OA) and dinophysistoxin-1 (DTX1) were also detected in SPATT extracts using liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS), consistent with the presence of *Gambierdiscus* and *Prorocentrum* species in the environment, as assessed by quantitative polymerase chain reaction (qPCR) and high-throughput sequencing (HTS) metabarcoding analyses conducted on passive window screen (WS) artificial substrate samples. Although these preliminary findings await further confirmation in follow-up studies, they highlight the usefulness of SPATT samplers in the routine surveillance of CP risk on a temporal scale, and the monitoring of other phycotoxin-related risks in ciguatera-prone areas.

**Keywords:** ciguatera monitoring; *Gambierdiscus*; ciguatoxins; SPATT passive samplers; HP20 resin; CBA-N2a; LC-MS/MS; WS artificial substrate; qPCR; HTS metabarcoding

**Key Contribution:** SPATT technology allowed the detection of CTXs, OA, and DTX1 in samples from Anaho Bay, French Polynesia, consistent with concomitant reports of the occurrence of *Gambierdiscus* and *Prorocentrum* species in the area. The deployment of 44 cm2 SPATT devices filled with 2.5 g of HP20 resin for 24 h allowed the highlighting of the circulation of dissolved CTXs in this well-known ciguatera hotspot. These preliminary results suggest that increasing both the deployment time and resin load could be useful when monitoring areas with a moderate-to-low CP risk.

#### **1. Introduction**

Harmful algal blooms (HABs) cause major sanitary, environmental, and economic problems in many aquatic environments worldwide [1]. Numerous countries thus maintain routine monitoring programs for the early detection of HAB events and the presence of associated toxins in order to mitigate the associated impacts. Most of these programs are based on the direct monitoring of potentially toxic microalgal and cyanobacterial species and qualitative/quantitative analyses of toxins accumulated in marine products intended for human consumption [2,3]. Since 2004, the Solid Phase Adsorption Toxin Tracking (SPATT) technique, which enables the passive sampling of dissolved toxins present in the water column associated with episodic HAB events, has increased in popularity as a complementary method [4]. Numerous laboratory- and field-based studies have demonstrated the ability of this technique to detect a large array of lipophilic and hydrophilic toxins produced by various microalgae and cyanobacteria, in both marine and freshwater environments [5,6]. SPATT samplers contain porous synthetic resins, and of the many adsorbent substrates tested to date, the DIAION® HP20 resin, an aromatic type adsorbent based on a cross-linked polystyrene matrix, appears to be the most versatile, and thus most used, substrate [6].

Ciguatera poisoning (CP) is a seafood-borne illness caused by the consumption of tropical marine seafood products (i.e., fish and invertebrate species) contaminated with ciguatoxins (CTXs) [7]. These polyether neurotoxins are produced by benthic dinoflagellates belonging to the genera *Gambierdiscus* and *Fukuyoa* [8]. Ciguatera is endemic to coral reef areas in the Caribbean Sea and Pacific and Indian Oceans [9]; however, its expansion into previously unaffected temperate regions has been documented since 2004 [10,11]. This rise of CP prevalence worldwide has prompted increased research efforts, most notably towards the improvement of current monitoring methods to predict and manage outbreaks. The suitability of SPATT samplers for the in situ monitoring of CTXs was recently assessed in a ciguatera hotspot in French Polynesia (Anaho Bay, Nuku Hiva Island, Marquesas archipelago) [12]. Previous studies in Anaho Bay have shown the recurrent presence of *Gambierdiscus* communities with the co-occurrence of at least five species and the predominance of *G. polynesiensis* [13–15], a species known to be a high producer of Pacific CTXs (P-CTXs) [16,17]. Moreover, P-CTXs were detected in fish [13], as well as in trochus [14] and sea urchins [15] collected from Anaho Bay. Finally, both trochus and sea urchin specimens were recently involved in atypical CP cases [15,18]. P-CTXs were successfully detected in methanol extracts of SPATT devices containing 10 g of HP20 resin, following a two-day deployment in Anaho Bay in July 2015 [12]. The presence of P-CTXs in these samplers was concomitant with the observation of *Gambierdiscus polynesiensis* populations [15], as well as the detection of P-CTXs in sea urchins [15]. These results confirmed the potential of SPATT technology as a passive environmental surveillance tool to support CP risk assessments and management programs, particularly in areas at high risk of ciguatera [12]. SPATT samplers—which are known for their relatively low cost as well as simplicity of preparation, deployment, transport, and storage [19]—appear well-suited for monitoring CTXs in remote and widely dispersed island locations, such as Nuku Hiva Island, and, more globally, other Pacific Islands Countries and Territories (PICTs), whose populations are the most vulnerable to CP [20].

The present work is a follow-up study to previous investigations conducted by Roué, et al. [12] in the Anaho Bay. To this end, two successive field experiments were conducted in the same study site using SPATT technology in an attempt to (i) follow the dissolved CTX trend in the environment over a 22 month period, and (ii) study the effects of different deployment times and device formats (i.e., resin load and surface of exposure) on the overall adsorption efficacy of SPATT samplers towards CTXs. Practically, SPATT devices of various surface areas, filled with 2.5 or 5 g of HP20 resin, were deployed in Anaho Bay in November 2016 and August 2018 for one, three, or six days. The levels of CTXs in methanol extracts were assessed using the mouse neuroblastoma cell-based assay (CBA-N2a), and the identity of congeners was determined using liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS). In parallel, the abundance of specific *Gambierdiscus* species in Anaho Bay was assessed through quantitative polymerase chain reaction (qPCR) assays conducted on passive window screen (WS) artificial substrate samples. Finally, in order to further characterize the potential human

health risks associated with the consumption of marine products from Anaho Bay, the composition of benthic dinoflagellate assemblages and presence of related toxic compounds in this area were also assessed using the (i) high-throughput sequencing (HTS) metabarcoding of WS samples and (ii) LC-MS/MS based multi-toxin screening of SPATT samples, respectively.

#### **2. Results**

#### *2.1. Molecular Analyses of Environmental Samples*

Benthic/epiphytic dinoflagellate cells were collected from macroalgal substrates as well as WS artificial substrates during November 2016 and August 2018 field campaigns in Anaho Bay.

Microscopic observations revealed the presence of *Gambierdiscus*, *Ostreopsis*, and *Prorocentrum* cells on macroalgal substrates collected in November 2016, whereas only *Gambierdiscus* and *Ostreopsis* cells were observed in August 2018 (Table 1).

Semi-quantitative, species-specific qPCR assays were performed on WS samples to determine the *Gambierdiscus* spp. relative abundance and species distribution. A reduction in *Gambierdiscus* cell abundance was observed between November 2016 and August 2018, with *Gambierdiscus* cells no longer detected in the 2018 samples (Table 1). Among the five species detected in November 2016, *G. polynesiensis* was the dominant species, followed by *G. carpenteri* (Table 1).

**Table 1.** Dinoflagellate genera observed by light microscopy on macroalgal substrates and semi-quantitative polymerase chain reaction (qPCR) estimates of *Gambierdiscus* species composition identified from window screen (WS) samples, from Anaho Bay in November 2016 (n = 6 for each substrate) and August 2018 (n = 5 for each substrate).

1 Data from Darius, et al. [14]; 2 Density expressed in cells 150 cm−2. ND = not detected.

In order to fully characterize the composition of benthic assemblages in *Gambierdiscus* and other HAB species, HTS metabarcoding analyses were carried out on artificial WS samples from August 2018. Sequencing targeting the large subunit ribosomal RNA (LSU) D1-D2 region generated between 27,069 and 97,610 Dinophyceae reads per sample after quality filtering, size trimming, and chimera detection (Table S1). Only 2% of the reads corresponded to *Gambierdiscus* species (Figure 1a), with four species identified: *G. polynesiensis* was the dominant species, representing 46% of *Gambierdiscus* reads, followed by *G. pacificus* (31%), *G. carpenteri* (22%), and *G. toxicus* (1%) (Figure 1b, Table S1). The most abundant Dinophyceae genus detected was *Ostreopsis*, representing nearly 48% of all Dinophyceae reads (Figure 1a), with three species identified: *O.* cf. *ovata*, *O.* cf. *lenticularis*, and *O.* cf. *rhodesiae* (Table S1).

*Toxins* **2020**, *12*, 321

**Figure 1.** Relative abundances of (**a**) Dinophyceae reads at the genus level and (**b**) *Gambierdiscus* reads at the species level from window screen samples collected in Anaho Bay in August 2018 (n = 5). All the classified genera representing less than 1% of all Dinophyceae reads (Table S1) were identified as "other Dinophyceae".

In addition, other known toxin-producing genera were detected, e.g., *Amphidinium*, *Azadinium*, *Coolia*, *Dinophysis*, *Karenia*, and *Prorocentrum*, although the numbers of reads for these taxa were low (Table S1). Bayesian phylogenetic analyses showed that all taxa classifications using the custom dinoflagellate sequence databases were correct, although the short sequences (approximately 350 bp) were not able to resolve some deeper phylogenetic relationships (Figure S1). Some taxa were able to be classified to the species level, in particular, *Gambierdiscus* spp., while other groups could not be successfully resolved. For example, some *Ostreopsis* spp. and *Amphidinium* spp. were not able to be assigned to specific species (Figure S1) either because they represent undescribed species or species that are not present in the GenBank database. Therefore, some taxa have been assigned to the genus-level only, or "cf." has been used to indicate some uncertainty with the classification.

#### *2.2. Evaluation of SPATT Samples Toxicity Using CBA-N2a*

Regardless of the resin load (2.5 or 5 g of HP20 resin), deployment time (1, 3, or 6 days), or surface area (44 or 71 cm2) of the SPATT devices deployed in Anaho Bay in November 2016 and August 2018, all methanol extracts were found to be toxic toward neuroblastoma (N2a) cells, with activity typical for CTXs: cytotoxic effects were observed in the presence of ouabain (O) and veratridine (V) (i.e., OV+ conditions), whereas no cytotoxicity was detected in the absence of O and V (i.e., OV− conditions) (Figure 2).

Mean half-maximal effective concentration (EC50) values ranging from 614 ± 123 to 6319 ± 1237 pg of dry extract μL−1 were obtained. These raw EC50 values were then converted into quantities of resin (ng HP20 resin equiv. μL−1) to be further compared with each other whatever the amount of resin used (i.e., 2.5 or 5 g), which ranged from 270 ± 51 to 1.5 × 104 ± 0.5 × 104 ng HP20 resin μL−1 (Table 2). Corresponding toxin contents were estimated at between 0.14 ± 0.04 and 7.0 ± 1.2 ng P-CTX3C equiv. g−1 HP20 resin (Table 2).

When the toxin contents were calculated according to the total quantity of resin per SPATT device, i.e., 2.5 or 5 g, these toxin contents varied from 0.34 ± 0.10 to 23.5 ± 3.2 ng P-CTX3C equiv. SPATT−1 (Figure 3). The toxicity data indicated a significant decrease (*p* < 0.0001) in CTX levels in Anaho Bay in 2018 compared to 2016; indeed, the amounts of CTXs detected on SPATT devices from August 2018 were 10- to 34-fold lower than those from November 2016, for a deployment time of 24 h and resin loads of 5 and 2.5 g, respectively (Figure 3).

**Figure 2.** Dose–response curves for neuroblastoma (N2a) cells exposed, in the absence (OV− condition) and presence (OV+ condition) of ouabain and veratridine, to increasing concentrations of methanol extracts (expressed in pg dry extract μL−1 and then converted to ng HP20 resin equiv. μL−1) from control HP20 resin or Solid Phase Adsorption Toxin Tracking (SPATT) devices (n = 3) filled with 2.5 g of HP20 resin and deployed for three days in Anaho Bay in November 2016. Data represent the means ± standard errors (SE) of values obtained from three independent neuroblastoma cell-based assays (CBA-N2a), each point run in triplicate.

**Table 2.** Mean half-maximal effective concentration (EC50) values (CBA-N2a) and toxin content estimates for SPATT devices deployed in Anaho Bay in November 2016 and August 2018.

1 Data represent the mean ± SE of values obtained from two or three different SPATT devices, each tested in two to three independent CBA-N2a experiments.

Concerning the effect of resin load, whatever the period of study, time of deployment, and surface of exposure, the use of 5 g of HP20 resin led to the adsorption of 1.4- to 6.2-fold higher levels of CTXs compared with a resin load of 2.5 g (Figure 3).

As for the deployment time, 44 cm2 SPATT devices deployed for three and six days accumulated 1.3- to 1.5- and 2.1- to 2.3-fold more CTXs than the ones deployed for one day, respectively, with the highest increases with deployment duration being observed for resin loads of 2.5 g (Figure 3).

Finally, regarding the sampler surface (only tested in 2018), after six days of deployment, the amounts of CTXs contained in larger SPATT devices were found to be 1.7- and 2.1-fold higher than in smaller SPATT samplers for resin loads of 2.5 and 5 g, respectively (Figure 3).

**Figure 3.** Toxin contents adsorbed on SPATT devices deployed in Anaho Bay in November 2016 and August 2018, based on the CBA-N2a data detailed in Table 1. Error bars represent SE.

#### *2.3. Characterization of Toxins Adsorbed by HP20 Resin Using LC-MS*/*MS*

All methanol extracts from SPATT devices deployed in Anaho Bay in November 2016 and August 2018 were analyzed using a LC-MS/MS based multi-toxin screening approach, leading to the detection of two groups of marine toxins. The presence of P-CTXs was confirmed in all samples, although at non-quantifiable concentrations (limit of quantification of P-CTX3C: 6 ng/mL). Two uncharacterized P-CTX3C isomers, consistently present in methanol extracts of TB92—a highly toxic strain of *Gambierdiscus polynesiensis* (Figure 4a)—were also detected in SPATT samples collected in November 2016 and August 2018 (Figure 4b,c), whereas P-CTX3B was found only in November 2016 samples (Figure 4b).

Among the other toxins sought, okadaic acid (OA) and dinophysistoxin-1 (DTX1) were also detected in all SPATT devices for OA, depending on the deployment time for DTX1 (Figure 5).

The toxin contents of OA per SPATT device ranged from 3.3 to 47.0 ng OA SPATT−1 (Table 3). For similar conditions, an increase in the time of exposure, resin load, or surface area led to 1.4- to 5.6-, 1.4- to 2.9-, or 1.4- to 1.9-fold higher levels of OA, respectively (Table 3). For a 24 h deployment time, a 3-fold decrease in OA contents was observed between November 2016 and August 2018.

Low toxin contents of DTX1 per SPATT device were detected, ranging from 2.0 to 6.9 ng DTX1 SPATT−1, provided that SPATT devices were deployed for at least three days (Table 3). As observed for OA, the highest level of DTX1 was detected after six days of exposure in August 2018 using 71 cm2 SPATT devices filled with 5 g of HP20 resin.

None of the other marine biotoxins sought (see Section 5.6 for details) were detected through LC-MS/MS multi-toxin screening analyses.

**Figure 4.** Liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) chromatograms of methanol extracts from (**a**) TB92-*Gambierdiscus polynesiensis* strain, and (**b**,**c**) SPATT devices filled with 5 g of HP20 resin and deployed for 24 h in Anaho Bay in November 2016 and August 2018, respectively. Chromatograms were acquired in positive scheduled multi-reaction mode (MRM) mode with a retention window of 90 sec, representing the MRM transitions of P-CTX3B/C at *m*/*z* 1023.5 > 1005.6 (in blue), 1040.6 > 1005.6 (in black), and 1023.6 > 125.1 (in green). The signal-to-noise (S/N) was calculated with three standard deviations for the *m*/*z* transition 1023.6 > 1005.6. The two potential P-CTX3C isomers were tentatively identified on the basis of two MRM transitions and the correct retention time of these isomers compared to that of the highly toxic *G. polynesiensis* strain TB92.

**Figure 5.** LC-MS/MS chromatograms of (**a**) okadaic acid (OA), dinophysistoxin-1 (DTX1), and dinophysistoxin-2 (DTX2) standards mix solution (NRC certified reference material); and (**b**,**c**) methanol extracts from SPATT devices filled with 5 g of HP20 resin and deployed in Anaho Bay for three days in November 2016 and for six days in August 2018, respectively. Chromatograms were acquired in negative MRM mode on *m*/*z* transitions 803.4 > 255.1 (black) and 803.4 > 113.5 (blue) for OA and its analog DTX2, as well as on *m*/*z* transitions 817.5 > 257.9 (green) and 817.5 > 112.9 (pink) for DTX1.

1 Data represent the mean ± SE of values obtained from two or three different SPATT devices and are expressed in ng of toxin per SPATT device; 2 ND: not detected; 3 Only one SPATT device was analyzed.

#### **3. Discussion**

#### *3.1. Benthic Harmful Algae Populations and Toxin Profiles over Time in Anaho Bay*

This study confirmed the ciguateric status of Anaho Bay. Indeed, the CBA-N2a toxicity data showed that dissolved CTXs previously detected on SPATT samplers deployed in the area in July 2015 [12] were still present in the water column at decreasing, yet quantifiable, amounts in November 2016 and August 2018. Molecular analyses of WS samples showed that *G. polynesiensis*, a species known to be highly toxic [16,17], was the dominant *Gambierdiscus* species both in November 2016 [14] and August 2018, although the abundance of total *Gambierdiscus* cells was low, especially in August 2018. The concentrations of CTXs detected on SPATT samplers after 24 h of deployment in August 2018 were significantly lower than in November 2016, consistent with the qPCR data for WS samples indicating a reduction in *Gambierdiscus* cells between November 2016 and August 2018. In addition, HTS metabarcoding analyses of environmental samples confirmed the very low abundance of *Gambierdiscus* in August 2018, in comparison with other dinoflagellate genera, in particular, *Ostreopsis*. Similarly, a two- to three-fold decrease in the levels of CTXs detected in locally sourced marine invertebrates was observed between November 2016 [14,15] and August 2018 (i.e., 0.15 and 0.19 ng P-CTX3C equiv. g−1 of tissue for sea urchins and trochus, respectively; unpublished data). Finally, the LC-MS/MS data showed that P-CTX3B, as well as two uncharacterized P-CTX3C isomers (tentative identification), were detected on SPATT devices deployed in November 2016, whereas only the two potential P-CTX3C analogs were present in the August 2018 samples. The detection of fewer CTX analogs in the SPATT devices in August 2018 vs. November 2016 could be due to the lower levels of total P-CTXs, as confirmed by the CBA-N2a results. As observed for trochus or sea urchin, the number of analogs detected decreased when the amount of total P-CTXs decreased [14,15]. Another explanation could be that uncharacterized P-CTX3C analogs are more stable in the water column and thus take longer to degrade than P-CTX3B; however, this hypothesis still needs to be confirmed.

The LC-MS/MS multi-toxin screening approach resulted in the detection of dissolved OA and DTX1 in Anaho Bay for the first time (in both November 2016 and August 2018). This result is consistent with the concomitant detection of *Prorocentrum* cells, by both microscopic observation and HTS metabarcoding analyses of environmental samples, although at very low abundance at the time of the field campaigns. Dinoflagellates of the genus *Prorocentrum* are potential producers of diarrheic shellfish poisoning (DSP) toxins [21]. Traces of *Dinophysis*, another potential producer of DSP toxins [21], were also detected in Anaho Bay in August 2018 using HTS metabarcoding. The low levels of OA and DTX1 (ng order) detected on the SPATT devices are consistent with the low number of reads detected for *Prorocentrum* (0.35%) and the very low number of reads detected for *Dinophysis* (0.03%). In bloom scenarios, much higher concentrations of OA-group toxins are typically detected in such SPATT devices, e.g., as was the case in France in 2016 [22]. The low concentrations of OA-group toxins are coherent with the low numbers of *Prorocentrum* cells in Anaho Bay, and it is not clear whether these were in decline, similarly to *Gambierdiscus* cells, or had been low over longer periods. In any case, the presence of OA and DTX1 in Anaho Bay may have contributed to the gastrointestinal symptoms reported by patients following the consumption of marine invertebrates from Anaho Bay [15,18], even though detectable amounts of these two toxins could not be positively confirmed in trochus and sea urchins analyzed one month after the poisoning occurred [14,15].

Conversely, neither palytoxin nor its analogs (i.e., ovatoxins) were found in SPATT samples by LC-MS/MS despite the high predominance of *Ostreospsis* spp. in Anaho Bay. This result is consistent with recent findings that both *O. lenticularis* [23] and *O.* cf. *ovata* (pers. comm., M. Chinain and D. Réveillon) strains from French Polynesia show no toxicity (as assessed by CBA-N2a and LC-MS/MS analyses), whereas strains of both species were found to be toxic in the Caribbean and Mediterranean regions [21,24]. However, the reporting of other potentially toxic HAB genera, such as *Amphidinium*, *Azadinium*, *Coolia*, *Karenia*, and *Ostreopsis* [21] in Anaho Bay emphasizes the importance of extending the environmental surveillance in this CP-endemic area to toxin groups other than CTXs.

Using the LC-MS/MS multi-toxin screening approach, several other biotoxin groups were screened for but were not detected, because of either the absence or low levels of the producing dinoflagellates in Anaho Bay. As an example, the non-detection of cyclic imines and azaspiracids is consistent with the very low number or absence of metabarcoding reads for the genera *Alexandrium*, *Azadinium*, *Karenia*, and *Vulcanodinium* (0% to 0.01% of the total Dinophyceae reads).

#### *3.2. E*ff*ects of Deployment Time on the E*ffi*cacy of CTX Detection*

To date, there is no consensus about the minimum/optimal deployment time of SPATT devices in the field and, as highlighted by Kudela [5], the deployment duration usually depends on the research or monitoring design of the programs. The present study provides some preliminary data about the potential of SPATT samplers to adsorb different amounts of CTXs under varying deployment times. In most of the field studies reported in the literature, SPATT samplers were deployed for one week [25–30], but sometimes for longer periods (up to one month) [31,32]. However, from a practical point of view, a deployment time of several days/weeks may be difficult or even impossible to implement, most notably in remote areas that are difficult to access, as is the case for Anaho Bay or many ciguatera-prone areas in PICTs. In the present study, using a highly sensitive detection method such as the CBA-N2a, the results showed that CTXs could be detected on SPATT samplers deployed in a long-standing ciguatera hotspot after only 24 h of deployment time. To the best of our knowledge, only one laboratory study has investigated the effect of different sorbent materials on SPATT adsorption efficiency [33], and two field studies have previously documented the successful monitoring of various toxin groups using limited deployment times; detectable amounts of OA, DTX1, yessotoxin (YTX), and pectenotoxins (PTXs) were found on HP20 SPATT devices deployed for only 3.5 h at Wedge point, New Zealand [4], while anatoxin-a and homoanatoxin-a were successfully detected on Strata-XTM SPATT devices exposed for only 4 h in Waipoua River, New Zealand [34].

Most importantly, using longer deployment times—i.e., three and six days—led to the adsorption of higher levels of CTXs onto HP20 resin. However, the highest level of CTXs adsorbed per gram of HP20 resin (i.e., 7 ng P-CTX3C equiv. g−1 HP20 resin) was nearly 8-fold lower than the saturation rate previously determined under laboratory conditions by Roué, et al. [12] (i.e., 55 ng P-CTX3C equiv. g−1 HP20 resin for a deployment of 10 g of HP20 resin during 48 h in an in vitro culture of TB92, a highly toxic *G. polynesiensis* strain). These findings suggest that SPATT devices deployed in Anaho Bay could potentially have retained higher amounts of CTXs if they had been deployed for a longer period, provided the presence of higher concentrations of CTXs in the environment. Likewise, the results also showed an increase in OA adsorption with increasing deployment times but with maximum levels per gram of HP20 resin lower than those reported in other studies [22,29], suggesting the limit of saturation of the SPATT has not been reached in the field experiments.

The results from the present study showed that a time of exposure of at least three days was necessary to detect DTX1. A previous study had already demonstrated a linear increase in the quantities of several polyether biotoxins adsorbed by HP20 resin for at least the first eight days of exposure at Wedge point, New Zealand [4]. Taken together, all these observations are consistent with laboratory studies suggesting that HP20 resin is a slow accumulator of various toxins as compared with other adsorbent substrates (e.g., Sepabeads®, Oasis® HLB, or Strata-X resins) and is thus more appropriate for long exposure periods [33,35,36]. Consequently, when low concentrations of toxins are expected to circulate in the water column (i.e., in areas with low-to-moderate CP risk), using longer deployment times (i.e., ≥1 week) could prove useful for increasing the detectability of toxins. Similarly to other kinetic samplers, if the uptake kinetic of SPATT devices is faster than their offload kinetic, toxin adsorption would increase linearly under prolonged exposure [5]. However, this is not a general rule for all toxins; for example, a fast desorption rate of paralytic shellfish poisoning (PSP) toxins from SP700 resin was observed in toxin-free seawater, indicating that for these hydrophilic toxins, the field deployment of SPATT samplers should not exceed seven days to avoid toxin loss [37]. Furthermore, it should be noted that a longer deployment time is likely to increase biofouling on SPATT devices, thus reducing water flow through the resin and toxin adsorption, although biofouling may play less of a role in tropical oligotrophic areas than in temperate eutrophic areas.

#### *3.3. E*ff*ects of Resin Load on the E*ffi*cacy of CTX Detection*

No consensus currently exists on the minimal/optimal resin load to use in SPATT samplers. In the literature, the resin loads tested in the different studies ranged from 0.3 g [22,33] to 10 g [12,27], although the amount most commonly used is between 3 and 5 g [4,25,28,30–32,36]. To the best of our knowledge, only one field-study by Zendong, et al. [22], conducted in the Mediterranean Sea, has compared the efficacy of SPATT devices to detect several lipophilic phycotoxins when filled with different resin loads (i.e., 0.3, 3, and 10 g of HP20 resin). The authors concluded that 3 g of resin offered the best compromise in terms of cost (lesser resin and solvent consumption), adsorption capacity, and efficacy (lesser potential for clogging) [22]. They noted that the 69 cm2 exposure surface used in their study was insufficient to avoid the superimposition of resin particles for a 10 g resin load, leading to a decrease in toxin adsorption efficiency. The results obtained in the present study are thus consistent with previous findings by Zendong et al. [22], as SPATT devices filled with 5 g of HP20 resin always led to the adsorption of higher levels of P-CTXs, OA, and DTX1 per device than with 2.5 g. The same observation applies when larger devices were used, i.e., 71 vs 44 cm2. These findings suggest that the contact surface between resin and seawater was relatively well optimized in the 44 cm2 SPATT samplers filled with 2.5 g and that 71 cm2 devices should be used for resin loads of 5 g in order to allow a better distribution of the resin (or higher surface/resin weight ratio) and thus a higher toxin adsorption.

#### **4. Conclusions**

This study provides preliminary data on the modular use of the SPATT technique (HP20 resin)—i.e., different deployment time, resin loads and surface areas—for the in situ monitoring of CTXs in CP-prone areas, as well as the survey of additional HAB-related toxin groups potentially present in ciguateric biotopes. In the Anaho ciguatera hotspot, the deployment of 44 cm2 SPATT devices filled with 2.5 g of HP20 resin for only 24 h allowed the assessment of a decrease in CTX concentrations over a 22 month time period. The results obtained over longer deployment times led us to speculate that in areas where the ciguatera risk is unknown or considered low-to-moderate, increasing the amount of resin and time of deployment could help improve toxin detection, provided the surface of the device is optimized to avoid resin clogging. From a practical point of view, it is suggested that when the concentrations of dissolved toxins are potentially low, the deployment of 71 cm2 SPATT samplers filled with 5 g of resin for at least one week could be considered.

More widely, both the design of the passive sampler (e.g., adsorbent substrate, resin load, and device surface) and deployment times should be adapted to the characteristics of the study site (e.g., the accessibility, hydrodynamics, trophic state, and diversity of HAB communities) and the targeted toxin(s) (e.g., lipophilic or hydrophilic toxins). Similarly to that of other environmental passive samplers, the calibration of SPATT samplers is problematic and is perhaps the single greatest issue in current efforts to achieve better acceptance of this technology in HAB monitoring and management programs [5]. While waiting for the inclusion of this useful tool in the context of routine monitoring programs, the use of SPATT technology in combination with classic monitoring techniques (e.g., microalgal cell enumeration/identification or the quantification of toxins in fish/shellfish) should be strongly encouraged, in order to gain better knowledge of the extent of toxin contamination of aquatic environments.

#### **5. Materials and Methods**

#### *5.1. Environmental Analyses*

The study site is located in Anaho Bay (Nuku Hiva Island, Marquesas archipelago, French Polynesia; 08◦49.171 S, 140◦03.923 W). Anaho Bay is a remote area, difficult to access from the main island of Tahiti (i.e., several hours' travel by plane, 4 × 4 car, and boat) and field campaigns cannot be regularly conducted.

Environmental samples of phytobenthos were collected from Anaho Bay in November 2016 and August 2018, using both natural (i.e., macroalgae) and artificial (i.e., window screen, WS) substrate methods, as previously described by Darius et al. [14]. The presence of *Gambierdiscus*, *Ostreopsis*, and *Prorocentrum* cells was assessed microscopically in macroalgal samples.

Semi-quantitative, species-specific qPCR assays were performed on WS samples to assess the relative cell abundance and distribution of the following *Gambierdiscus* species: *G. polynesiensis*, *G. toxicus*, *G. pacificus*, *G. australes, G. caribaeus*, and *G. carpenteri*, which are currently reported in French Polynesia, as well as four additional species/phylotypes, i.e., *G. belizeanus*, *G. carolinianus*, *G. ruetzleri*, and *Gambierdiscus*ribotype 2. The qPCR assays were performed using the protocol previously described in Vandersea et al. [38] and Darius et al. [14], with a sensitivity of detection of the assays established at approximately 10 cells, i.e., ranging from ≈ 20 to 5000 gene copy numbers depending on the species targeted [38]. The same procedure was followed for the taxonomic identification at the species level of the eight clonal cultures of *Gambierdiscus* further established from field samples.

Additionally, the WS samples collected in August 2018 were further analyzed using HTS metabarcoding, using primers targeting the LSU D1-D2 region, as previously described by Smith et al. [39]. Briefly, samples were centrifuged, and DNA was extracted from the pellets using Qiagen DNeasy PowerSoil kits (Qiagen, Carlsbad, CA, USA). The primers (D1R-F and D3B-R) were modified to include IlluminaTM overhang adaptors. Libraries were prepared using the IlluminaTM two-step PCR amplicon library preparation method and sequenced using an IlluminaTM MiSeq sequencer with 2 × 250 bp paired-end reads at Auckland Genomics (University of Auckland, Auckland, New Zealand). All data generated were quality checked using the following tools: FastQC, FastQscreen, and SolexaQA [40]. Further analyses were carried out in Mothur v1.37.6 [41] as described by Smith et al. [42]. Phylogenetic analyses were carried out in Geneious® using MrBayes 3.1.2 [43].

#### *5.2. HP20 Resin and SPATT Device Preparation*

Adsorbent styrene-divinylbenzene resin (Diaion® HP20, Supelco, USA) was activated overnight by stirring at a low speed in methanol (MeOH) at a 10:1 ratio (mL g−1). The HP20 resin was then filtered under vacuum using a fritted funnel and, subsequently, thoroughly rinsed with de-ionized (MilliQ) water to remove any methanol residues. The hydrated resin was kept in MilliQ water at a 10:1 ratio (mL g−1) at 4 ◦C until the preparation of the SPATT devices.

SPATT samplers consisted of two layers of 100 μm nylon mesh filled with HP20 resin (2.5 or 5 g wet weight) and clipped between either two PVC circular disks (7.5 cm in diameter, corresponding to a surface of 44 cm2) or embroidery frames (9.5 cm diameter, corresponding to a surface of 71 cm2). SPATT devices were set up shortly before the field experiments (i.e., less than 1 week before) and were kept in MilliQ water at 4 ◦C until in situ deployment.

#### *5.3. Field Deployments of SPATT Devices*

On-site, SPATT devices were placed in plastic grids to prevent damage from fish grazing and then maintained in a vertical position in the water column using weights and floats. The deployment occurred near dead coral blocks colonized by macroalgae likely to shelter benthic *Gambierdiscus* cells, in locations with a moderate current to allow the circulation of seawater through the HP20 resin. Two series of experiments were conducted: one in November 2016, where 44 cm2 SPATT devices were filled with 2.5 and 5 g of HP20 resin and retrieved after one or three days of deployment (Table 1); and the second one in August 2018, where 44 and 71 cm2 SPATT devices were filled with 2.5 and 5 g of HP20 resin and retrieved after one or six days of deployment (Table 1). Each condition was tested in triplicate (n = 30 SPATT devices). A total of 6 samplers could not be retrieved due to damage or loss. Following collection, SPATT devices were stored at 4 ◦C in natural seawater until toxin extraction.

#### *5.4. Toxin Extraction from HP20 Resin*

Each SPATT device was rinsed thoroughly with MilliQ water, in order to remove any cell debris or epiphytes possibly adhering to the nylon mesh, and then carefully dismantled to retrieve the HP20 resin. Using a fritted funnel under vacuum, the resin was washed thoroughly with MilliQ water in order to eliminate salt residues. Toxins were extracted from HP20 resin with MeOH in a 10:1 ratio (mL·g−1). The resulting methanol extracts were dried under vacuum at 40 ◦C using a rotary evaporator (Rotavapor RII, Büchi, Switzerland) and stored at 4 ◦C until CBA-N2a or LC-MS/MS analyses.

#### *5.5. Neuroblastoma Cell-Based Assay*

Methanol extracts were screened for the presence of CTXs using the CBA-N2a assay performed following the protocol previously described by Darius et al. [14]. The final concentrations for ouabain/veratridine treatment (OV+ conditions) ranged from 85/8.5 to 100/10 μM in order to obtain 90–100% of cell viability against control cells without ouabain/veratridine treatment (OV− conditions). Each sample was tested in two or three independent experiments, with each concentration run in triplicate per plate. Methanol extracts were tested using a serial dilution at 1:2 of eight concentrations ranging from 74 to 9524 or 186 to 23,809 pg dry extract μL−1 for SPATT devices deployed in November 2016 or August 2018, respectively.

Absorbance data were fitted to a sigmoidal dose–response curve (variable slope) based on the four-parameter logistic model (4PL), allowing the calculation of raw EC50 values expressed in pg dry extract μL−1 using the Prism v6.07 software (GraphPad, San Diego, CA, USA). For a more convenient presentation of our results, the CBA-N2a curves and EC50 of samples were further converted into ng HP20 resin equiv. μL−1. The calibration of the assay was achieved using a P-CTX3C reference material sourced from the Institut Louis Malardé (Papeete, French Polynesia) and that had been quantified gravimetrically. The EC50 value obtained for P-CTX3C was 1.84 ± 0.31 fg μL−1 (n = 5). The toxin contents (T) of samples were estimated using the formula T = (P-CTX3C EC50 / sample EC50) and expressed in ng P-CTX3C equiv. g−1 HP20 resin. The toxin content per SPATT device was also estimated using the following formula: T × 2.5 or 5 g and expressed in ng P-CTX3C equiv. SPATT−1. A Mann–Whitney test was applied to the CBA-N2a data using the Prism v6.07 software (GraphPad, San Diego, CA, USA) in order to compare all the toxin contents obtained in November 2016 vs. August 2018.

The maximum concentration of dry extract (MCE) that does not induce non-specific cytotoxic effects on N2a cells was assessed using a methanol extract obtained from 10 g of non-exposed activated HP20 resin. MCE was found to be higher than 1 × 104 pg dry extract μL−1, corresponding to 1 × 105 ng HP20 resin equiv. μL−1 (Figure 1). The limit of quantification (LOQ) was thus estimated to be at 0.02 ng P-CTX3C equiv. g−1 HP20 resin.

#### *5.6. Liquid Chromatography Coupled with Tandem Mass Spectrometry Analyses*

LC-MS/MS analyses were conducted on methanol extracts for the detection of several marine biotoxin groups.

#### 5.6.1. Detection Method for P-CTXs

P-CTX analysis [44] was carried out using an LC system (UFLC Nexera, Shimadzu, Kyoto, Japan) coupled to a hybrid triple quadrupole/ion-trap mass spectrometer (API4000Qtrap, Sciex, Redwood City, CA, USA) equipped with a turbo spray® interface. A C18 Zorbax Eclipse Plus column (1.8 μm, 50 × 2.1 mm, Agilent Technologies, Santa Clara, CA, USA) was employed at 40 ◦C and eluted at 400 μL min−1 with a linear gradient. Eluent A was water and Eluent B was methanol, with both eluents containing 2 mM ammonium formate and 50 mM formic acid. The elution gradient ran from 78% to 88% B over 10 min and was held for 4 min before re-equilibration over 5 min.

Mass spectrometric detection was operated in positive mode and using scheduled Multiple Reaction Monitoring (MRM) with a detection window of 90 s, with *m*/*z* values of the transitions listed in Table 4. MRM experiments were established using the following electrospray ionization (ESI) parameters: curtain gas (CG) set at 25, ion spray (IS) at 5500 V, a turbogas temperature (T) of 300 ◦C, gas 1 (GS1) set at 40, and gas 2 (GS2) set at 60 psi, with an entrance potential (EP) of 10 V.

Data processing and analysis were achieved with the Analyst software (Sciex, Redwood City, CA, USA). Quantification was performed from a linear calibration curve generated from the P-CTX3C standard (Wako chemicals GmbH, Neuss, Germany) using the MRM transition [M + H]+/[M + H − H2O]+. The LOD and LOQ for P-CTX3C were 2 and 6 ng mL−1, respectively. For the comparison of retention times, an extract of TB92, a highly toxic *G. polynesiensis* strain provided by the Institut Louis Malardé (Papeete, French Polynesia), was injected in the sequence.

#### 5.6.2. Detection Method for OA and DTXs

OA and DTXs are part of our analytical method for lipophilic toxins, performed in negative ionization mode. This analytical method grouped OA, DTXs, and yessotoxins (YTXs) and was carried out on the same instrument system as described above.

Chromatographic separation was carried out on a C18 Kinetex (50 × 2.1 mm, 2.6 μm, Phenomenex, Le Pecq, France), using a mobile phase composed of (A) water and (B) 95% acetonitrile, both containing 2 mM ammonium formate and 50 mM formic acid at a flow rate of 400 μL min−1 and maintained at 40 ◦C. The elution gradient ran from 10% to 50% B over 2 min, to 95% B over the next 4 min and was held for 2 min before re-equilibration.

MRM experiments were carried out in negative mode and the *m*/*z* selected are listed in Table 4. The ESI parameters were set as follows: CG, 20; IS, −4500 V; T, 550 ◦C; GS1, 40 psi; GS2, 50 psi; and EP, −13 V. Quantification was performed from linear calibration curves generated from certified standards of OA, DTX2, DTX1, and YTX (National Research Council, Ottawa, ON, Canada). The LOD and LOQ for the standards available were determined with the ordinary least-squares regression data method [45,46] (Table 4).

**Table 4.** List of selected *m*/*z* for the MRM experiments.

**Table 4.** *Cont.*

1 MRM transition used for quantification in each method.

#### 5.6.3. Detection Methods for Other Toxins

In addition, quantitative targeted analyses (MRM mode) were conducted following the methods previously described for some of the other major marine biotoxin groups: (i) lipophilic toxins, i.e., pectenotoxins (PTXs) and azaspiracids (AZAs) [47]; (ii) cyclic imines, i.e., gymnodimins (GYMs), spirolids (SPXs), and pinnatoxins (PnTXs) [47]; (iii) palytoxin-like toxins, i.e., palytoxin (PLTX), 42-hydroxy-palytoxin (42-OH-PLTX), and ovatoxins (OvTXa to OvTXh) [48]; (iv) maitotoxins (MTX1 to MTX4) [49]; and (v) cyanotoxins, i.e., microcystins (dm-MC-RR, MC-RR, MC-YR, MC-LR, dm-MC-LR, MC-LA, MC-LY, MC-LW, and MC-LF) and nodularin (NOD) [50].

**Supplementary Materials:** The following are available online at http://www.mdpi.com/2072-6651/12/5/321/s1, Table S1: Read number and classification levels at 98% identity for the large subunit ribosomal RNA gene region from each window screen sample collected from Anaho Bay (Nuku Hiva Island, French Polynesia) in August 2018. Figure S1: Phylogenetic analysis of large subunit ribosomal DNA (D1-D2 region) sequences obtained from the high-throughput sequencing metabarcoding using Bayesian analyses. Sequences in grey represent the consensus sequence from all reads of each taxonomic assignment. Values at nodes represent Bayesian posterior probability support. Scale bar is substitutions per site.

**Author Contributions:** Conceptualization, M.R. and M.C.; Data curation, M.R., K.F.S., M.S., P.H. and M.C.; Field experiments, M.R., K.H., A.U. and M.C.; Formal analysis, M.R., K.F.S., M.S., J.V. and L.B.; Funding acquisition, M.R. and M.C.; Methodology, M.R., K.F.S., M.S., J.V., K.H., A.U. and L.B.; Supervision, M.R.; Validation, M.R., K.F.S., P.H., H.T.D. and M.C.; Writing—original draft, M.R., K.F.S., M.S. and M.C.; Writing—review & editing, M.R., K.F.S., M.S., L.B., P.H., H.T.D. and M.C. All authors have read and agreed to the published version of the manuscript.

**Funding:** The present work was supported by funds from France and French Polynesia in the framework of the "CARISTO-Pf" (no. 7937/MSR/REC of 4 December 2015 and Arrêté no. HC/491/DIE/BPT of 30 March 2016) and "CYANOTOX" (no. AFD CPF 1440 01 N of 22 December 2016) research programs, and from the New Zealand Ministry for Business, Innovation and Employment (no. CAWX1801).

**Acknowledgments:** The authors wish to thank Clémence Gatti and Taina Revel from Institut Louis Malardé for their technical assistance in field experiments and qPCR analyses, respectively, as well as the two anonymous reviewers whose comments greatly helped improve the manuscript.

**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

### **References**

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).


**4.1 The waste hierarchy**

The aim of the waste hierarchy is to recognize the problem and to generate the suitable environment friendly product from the waste. As shown in **Figure 1**, 'prevention' is the most favorable and disposal is the least favorable activity in the waste management 'pyramid'. It has been advised to consider the social and economic impacts as well as the environmental, the waste hierarchy, as a framework, primarily focuses on delivering the best environmental option by the European Waste Framework Directive (European Parliament Council, 2008).


**1. Introduction**

The renin-angiotensin system (RAS) was described in the 1980s, and since then studies have focused on its role in the hemodynamic control [1]. The RAS is classically associated with blood pressure regulation and electrolyte balance. The system main peptide angiotensin II acts through two major receptors termed type 1 and type 2. They are widely distributed in the tissues, and have different functions; hemodynamic changes such as vasoconstriction and cellular proliferation are related to type 1 receptor and vasodilation and anti-cellular proliferation to type 2 [2].

© 2017 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

All components of the RAS, including the receptors, are present very early in the human development (24–35 days of gestation), suggesting a role for angiotensin II in the organogenesis [3].

The RAS components are present in many tissues, and there are evidences for a tecidual angiotensin II biosynthesis with high concentrations in the kidneys (intrarenal RAS) [4, 5]. They are synthesized by different cells and interact locally with autocrine and paracrine effects. It has been suggested that the plasma RAS is important for acute regulatory mechanisms, whereas the tissue RAS may be more involved in chronic cardiovascular and renal regulation [6, 7].

Therefore, RAS maintains hemodynamic homeostasis and controls tecidual growth.

Pathologic consequences can result in overactivity of this cascade with an involvement of the RAS in several renal diseases. Regardless of the initial type of injury, all chronic renal diseases develop glomerular and vascular sclerosis, tubular atrophy and interstitial fibrosis, with progressive nephron loss and chronic renal failure. Adaptative changes in the remaining nephrons after initial injury cause more scarring and nephron loss, thus perpetuating a vicious cycle that results in the end-stage kidney. Chronic RAS activation is involved in these maladaptive mechanisms of progressive renal damage. Angiotensin II-mediated effects such as haemodynamics changes, glomerular and tubular hypertrophy and hyperplasia, infiltration of mononuclear cells and fibrogenesis were observed [8].

The system hyperactivity leads to progressive lesions presenting an important role in the pathophysiology of chronic cardiovascular and renal diseases. The RAS activation has been demonstrated in various kidney diseases, in both experimental and clinical studies [9, 10]. The system blockade, with inhibitors of angiotensin converting enzyme (ACE) of angiotensin II and angiotensin II-receptor blockers, shows large benefits in the treatment of chronic kidney diseases [11].

Thus, the classical approach of Angiotensin II as a vasoactive agent that participates in the systemic hemodynamic changes was expanded to recognize its role as a growth factor that modulates cell proliferation, synthesis and degradation of extracellular matrix.

A significant complication on renal transplantation is the chronic and progressive allograft dysfunction that develops months or years after transplantation. Recent advances with new immunosuppressive drugs did not improve the long-term allograft survival. Despite the well established knowledge of the ability of renin-angiotensin system blockade to control blood pressure and urinary protein excretion, the use of RAS inhibitors and blockers in renal transplant has been limited [12, 13].

We review our own observations and recent reports from the literature about the important role of RAS in the pathogenesis of chronic inflammatory process and local tecidual growth, in the chronic allograft dysfunction.


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**127**

Section 5

Formula Feeding: Thermal

Processing Contaminants

Section 5


**5. Conclusions**

Under the conditions of model experiments, the effect of bacteria that are present in human urine (*Escherichia coli, Pseudomonas aeruginosa, Klebsiella pneumoniae* and *Staphylococcus aureus*) on the formation of the renal stone mineral phases, such as brushite, struvite, vitlocite, octacalcium phosphate, apatite, whewellite, and weddellite, was studied in systems simulating the composition of human urine and using two types of nutrient media (Muller–Hinton Broth and Meat–Peptone Broth). Multidirectional changes in the pH values of the solutions were analyzed, which are the result of all system components' interactions with the crystallization process.

It was shown that the presence of bacteria has a different effect on the phosphate and oxalate phases' formation. The presence of pathogens and nutrient media significantly affect the precipitant phase composition and the position of the resulting phosphate phase's crystallization boundaries, which can shift both to more acidic (struvite, apatite) and more alkaline (brushite) areas. Under conditions of oxalate mineralization, bacteria accelerate the nucleation of calcium oxalates by almost two times and also increase the amount of oxalate precipitates along with phosphates and stabilize the calcium oxalate dihydrate to weddellite.

As it can be seen from the reported results and the available literature data, the bacterial effect on oxalate and phosphate phase formation is different. Thus, in the case of oxalate mineralization, primarily (most likely), the inflammatory process will contribute to the decrease of oxalate supersaturation in urine due to calcium oxalate crystallization. In the case of phosphate mineralization, the change in urine pH and the products of bacterial metabolism will be of major importance. Studies aimed at identifying the specific action of certain microorganisms on the crystallization of certain mineral phases should serve to develop individual methods of treatment and prevention of urolithiasis.

The obtained results could be regarded as the scientific basis for understanding the mechanisms of bacterial participation stone formation in the human urinary system and the creation of biotechnological methods for the prevention of this disease.

**Author Contributions:** Conceptualization, A.R.I., O.V.F.-K., and V.V.M.; methodology, A.R.I., M.A.K., and O.V.F.-K.; investigation, A.R.I., A.M.N., M.A.K., and V.V.M.; writing—original draft preparation, A.R.I., M.A.K., and A.M.N.; writing—review and editing, A.R.I., O.V.F.-K., and V.V.M.; visualization, A.R.I. and A.M.N.

**Funding:** This research was funded by the Russian Science Foundation (grant 18-77-00026 to A.R.I.).

**Acknowledgments:** The XRD have been performed at the X-ray Diffraction Centre of the St. Petersburg State University. We are grateful to reviewers for useful comments.

**Conflicts of Interest:** The authors declare no conflict of interest.


3. Results and discussion

This section presents the results of hydrological and hydraulic model calibration and validation and some results of the investigation of the influence of the proposed flood safety protection measures in the Savinja River catchment.

#### 3.1. Hydrological model and analysis

The hydrological model was calibrated based on the flood event that occurred in September 2007 and caused large damage in different parts of Slovenia [2]. The average value of the Nash-Sutcliffe coefficients for the calibration of the model for the 21 sub-catchments (with available discharge data) was 0.85. Figure 9 shows an example of the calibration results for the location of the Laško gauging station on the Savinja River with the Nash-Sutcliffe coefficient as 0.93.

Figure 9. Hydrological model calibration results using the data from year 2007 for the station Laško on the Savinja River (in the lower figure with red and blue is simulated and observed discharge, respectively).

The validation of the model was performed using the data from floods that occurred in years 1990 and 1998 and also caused large damage in the Savinja River catchment [3, 4]. For the 1990 event, the average value of the Nash-Sutcliffe coefficients for nine stations with available data was 0.85. Using the calibrated and validated hydrological model, we were able to reconstruct the hydrological situation in the Savinja catchment also for the locations where discharge data were not available (either no gauging station or station was damaged during the flood) for floods that occurred in years 1990, 1998 and 2007. Table 1 shows calibration results for the 2007 flood event for 19 sub-catchments where measured discharge data were available in order to perform evaluation of the hydrological model. Moreover, Table 2 shows hydrological model validation results for the 1990 flood event for gauging stations with available measured discharge data. The number of gauging stations in the 1990 was smaller than in the case of 2007 because gauging network was extended in the recent decades and several gauging stations were damaged during the 1990 flood event.

properties table based on the underlying digital terrain model used (1 m resolution). The HEC-RAS pre-processor computes the elevation-volume relationship and other geometric characteristics crucial for hydraulic calculations for each cell face [16]. Figure 8 shows the main rivers that were included in the hydraulic model from the confluence of the Savinja and Dreta Rivers to the confluence of the Savinja and Sava Rivers. It should be noted that due to the improved 2D modelling algorithm that is implemented in the HEC-RAS version 5 [16], the entire 135 km of the river network with multiple flood areas was modelled as one model. Moreover, the total

This section presents the results of hydrological and hydraulic model calibration and validation and some results of the investigation of the influence of the proposed flood safety protec-

The hydrological model was calibrated based on the flood event that occurred in September 2007 and caused large damage in different parts of Slovenia [2]. The average value of the Nash-Sutcliffe coefficients for the calibration of the model for the 21 sub-catchments (with available discharge data) was 0.85. Figure 9 shows an example of the calibration results for the location of the Laško gauging station on the Savinja River with the Nash-Sutcliffe coefficient as 0.93.

Figure 9. Hydrological model calibration results using the data from year 2007 for the station Laško on the Savinja River

(in the lower figure with red and blue is simulated and observed discharge, respectively).

computational time did not exceed 2.5 h.

76 Achievements and Challenges of Integrated River Basin Management

tion measures in the Savinja River catchment.

3.1. Hydrological model and analysis

3. Results and discussion

Note that computational period to calculate discharge sum was from 1.3.2007 to 14.12.2007.

Table 1. Hydrological model calibration results for the 2007 flood event for the 19 sub-catchments where measured discharge data were available.

Table 2. Hydrological model validation results for the 1990 flood event for the sub-catchments where measured discharge data were available.

In order to define the design hydrographs, the flood frequency analysis was also performed. The annual maximum method was used for sample definition and log-Pearson type III distribution was applied to define the relationship between design discharge and return period.

#### 3.2. Hydraulic model and analysis

The calibration and validation of the hydraulic model were also performed using the data from 1990, 1998 and 2007 floods. Besides discharge data, information about water level was also used (rating curves were used to transform water level data to discharge). Comparison between the measured maximum flood extent on the floodplain areas and computed inundation extent was also carried out. Figure 10 shows an example of the calibration results for the gauging station Celje on the Savinja River in the year 1990. Similar results were also obtained for some other gauging stations in the Savinja catchment for the 1990, 1998 and 2007 events. Model evaluation was performed on rivers Dreta, Ložnica, Voglajna, Hudinja and Savinja. Figure 11 shows calibration results for the large natural floodplain area before the Celje city for the 1990 event. Similar graphical comparison was also carried out for other flooding areas.

#### 3.3. Flood safety

The calibrated and validated hydrological and hydraulic models of the Savinja River catchment were used to investigate the impact of the proposed flood protection measures on the flood safety. The main suggested flood protection measures are dry retention (flood-control) reservoirs that are planned to be built at several locations in the Savinja catchment. Eight floodcontrol reservoirs are to be constructed in the location of the large natural flood area before the Celje city (Figure 11). Relatively sophisticated and complex hydro-technical equipment is selected to operate these reservoirs with the total volume of approximately 8106 m3 . Figure 12 shows comparison between three different situations, namely natural-actual conditions during the 1990 event, full operation of the proposed flood-control reservoirs with increased volume

(retention of 10106 m3

floodplain inundation).

increase in peak discharge for approximately 100 m3

) and proposed flood-control reservoirs that failed to operate. We can

/s due to the exclusion of large natural

/s;

conclude that proposed flood-control reservoirs reduce the peak discharge for about 150 m3

however, potential technical problems with hydro-technical equipment would lead to an

Figure 11. Calibration results for the largest natural floodplain area before the Celje city for the 1990 event (light blue is modelled extent of floodplain inundation by combined 1D/2D model and grey with pink outline is measured extent of

Figure 10. Calibration results for the gauging station Celje on the Savinja River for the 1990 event (blue is modelled water

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level and red is measured water level by the Slovenian Environment Agency).

floodplain area (Figure 12). It can be seen that the construction of the reservoirs would lead to about 15% decrease in the peak discharge compared to the natural conditions during the 1990 event. This means that the flood risk downstream of the Celje city would decrease in case of

operation of reservoirs without any problems and according to the procedure.

Historical, Hydrological and Hydraulics Studies for Sustainable Flood Management http://dx.doi.org/10.5772/intechopen.74432 79

Figure 10. Calibration results for the gauging station Celje on the Savinja River for the 1990 event (blue is modelled water level and red is measured water level by the Slovenian Environment Agency).

In order to define the design hydrographs, the flood frequency analysis was also performed. The annual maximum method was used for sample definition and log-Pearson type III distribution was applied to define the relationship between design discharge and return period.

Table 2. Hydrological model validation results for the 1990 flood event for the sub-catchments where measured

Measured discharge sum

Nash-Sutcliffe R2

[mm/period]

1-Savinja1-Luče 2562 3010 0.85 0.89 4-Dreta-Kraše 4442 4901 0.90 0.92 5-Savinja3-Letuš 4285 3692 0.59 0.89 12-Ložnica-Levec 1453 1845 0.94 0.96 13-Savinja5-Celje\_brv 2970 3111 0.97 0.98 15-Hudinja2-Škofja\_vas 1278 1382 0.79 0.83 17-Voglajna2-Celje 1011 1478 0.79 0.87 19-Savinja6-Laško 2444 2582 0.97 0.97 21-Savinja7-Veliko Širje 2320 1301 0.84 0.91

The calibration and validation of the hydraulic model were also performed using the data from 1990, 1998 and 2007 floods. Besides discharge data, information about water level was also used (rating curves were used to transform water level data to discharge). Comparison between the measured maximum flood extent on the floodplain areas and computed inundation extent was also carried out. Figure 10 shows an example of the calibration results for the gauging station Celje on the Savinja River in the year 1990. Similar results were also obtained for some other gauging stations in the Savinja catchment for the 1990, 1998 and 2007 events. Model evaluation was performed on rivers Dreta, Ložnica, Voglajna, Hudinja and Savinja. Figure 11 shows calibration results for the large natural floodplain area before the Celje city for the 1990 event. Similar graphical comparison was also carried out for other flooding areas.

The calibrated and validated hydrological and hydraulic models of the Savinja River catchment were used to investigate the impact of the proposed flood protection measures on the flood safety. The main suggested flood protection measures are dry retention (flood-control) reservoirs that are planned to be built at several locations in the Savinja catchment. Eight floodcontrol reservoirs are to be constructed in the location of the large natural flood area before the Celje city (Figure 11). Relatively sophisticated and complex hydro-technical equipment is

shows comparison between three different situations, namely natural-actual conditions during the 1990 event, full operation of the proposed flood-control reservoirs with increased volume

. Figure 12

selected to operate these reservoirs with the total volume of approximately 8106 m3

3.2. Hydraulic model and analysis

discharge data were available.

Sub-catchment Model discharge sum

[mm/period]

78 Achievements and Challenges of Integrated River Basin Management

3.3. Flood safety

Figure 11. Calibration results for the largest natural floodplain area before the Celje city for the 1990 event (light blue is modelled extent of floodplain inundation by combined 1D/2D model and grey with pink outline is measured extent of floodplain inundation).

(retention of 10106 m3 ) and proposed flood-control reservoirs that failed to operate. We can conclude that proposed flood-control reservoirs reduce the peak discharge for about 150 m3 /s; however, potential technical problems with hydro-technical equipment would lead to an increase in peak discharge for approximately 100 m3 /s due to the exclusion of large natural floodplain area (Figure 12). It can be seen that the construction of the reservoirs would lead to about 15% decrease in the peak discharge compared to the natural conditions during the 1990 event. This means that the flood risk downstream of the Celje city would decrease in case of operation of reservoirs without any problems and according to the procedure.

influence on the situation in the lower Sava River. This kind of local measures mostly have minor impact on the global situation in the larger catchment such as the Savinja River catchment but can lead to improved situation locally. Similar conclusions were also made for the

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Furthermore, several other aspects of the flood safety such as the impact of high waters at the river confluences on the downstream flood safety were also investigated but are not discussed

Using the calibrated and validated combined hydrological (HBV-light) and hydraulic (HEC-RAS 5) models, we investigated the influence of the proposed flood protection measures (e.g. several flood-control reservoirs are to be built in the large natural flood area before the Celje city) on the flood safety. Moreover, using the hydraulic model HEC-RAS that is presented in Section 3.2, we also investigated the backwater effect on different tributaries in the Savinja catchment. Figure 14 shows an example of the backwater effect on the Ložnica River. It can be seen that due to the increased peak discharge on the Savinja River, the maximum water on the Ložnica River also increases. This increase is the largest for the cross section located near the

decreases for upstream river station. Moreover, the backwater effect is detected for the cross section that is located 1.5 km upstream of the confluence of the Savinja and Ložnica Rivers. Similar analysis was performed for other rivers (e.g. Hudinja and Voglajna; Voglajna and

of analysis can be very useful also for the policy makers because it is essential to understand

Figure 14. The influence of the Savinja River on the Ložnica River (backwater effect) when the Ložnica input hydrograph is constant during different hydraulic model runs. Different coloured lines represent different cross sections on the Ložnica River where the number indicates river station from the confluence with the Savinja River upstream [m].

/s) and generally,

/s. This kind

rivers' confluence (about 0.6 m for peak discharge increase at 400 m3

Savinja). The backwater effect can be up to 0.25 m for a peak discharge of 1000 m3

case study of the alpine Inn River in Austria [17].

in this chapter.

3.4. Backwater effect

Figure 12. Impact of the proposed flood-control reservoirs with increased total volume (10106 m3 ) on the situation at the Savinja outlet during the 1990 flood (dark blue), exclusion of large natural flood area before the Celje city (situation when proposed flood-control reservoirs fail to operate, purple) and actual situation during the 1990 flood (light blue).

Moreover, several smaller flood protection measures (e.g. channel widening at critical cross sections, river banks' reconstruction, local level construction) are also proposed in the Savinja catchment (mostly on rivers Ložnica, Hudinja and Voglajna). The analyses of these measures showed that they mostly positively influence the flood situation at the confluence of Savinja and Sava Rivers. Flood protection measures mostly fasten the hydrograph propagation but often do not significantly influence the peak discharge values (the decrease in the peak discharge is, in most cases, smaller than 1 or 2%). The analysis of catastrophic past flood events demonstrated that the peak discharge on the Savinja River mostly occurs before the peak discharge on the Sava River (Figure 13). Thus, faster hydrograph propagation has a positive

Figure 13. Analysis of time differences between peak discharge values at the confluence of the Savinja and Sava Rivers. Positive values indicate that peak discharge of the Savinja River occurs before the peak discharge of the Sava River.

influence on the situation in the lower Sava River. This kind of local measures mostly have minor impact on the global situation in the larger catchment such as the Savinja River catchment but can lead to improved situation locally. Similar conclusions were also made for the case study of the alpine Inn River in Austria [17].

Furthermore, several other aspects of the flood safety such as the impact of high waters at the river confluences on the downstream flood safety were also investigated but are not discussed in this chapter.

#### 3.4. Backwater effect

Moreover, several smaller flood protection measures (e.g. channel widening at critical cross sections, river banks' reconstruction, local level construction) are also proposed in the Savinja catchment (mostly on rivers Ložnica, Hudinja and Voglajna). The analyses of these measures showed that they mostly positively influence the flood situation at the confluence of Savinja and Sava Rivers. Flood protection measures mostly fasten the hydrograph propagation but often do not significantly influence the peak discharge values (the decrease in the peak discharge is, in most cases, smaller than 1 or 2%). The analysis of catastrophic past flood events demonstrated that the peak discharge on the Savinja River mostly occurs before the peak discharge on the Sava River (Figure 13). Thus, faster hydrograph propagation has a positive

Figure 13. Analysis of time differences between peak discharge values at the confluence of the Savinja and Sava Rivers. Positive values indicate that peak discharge of the Savinja River occurs before the peak discharge of the Sava River.

Savinja outlet during the 1990 flood (dark blue), exclusion of large natural flood area before the Celje city (situation when proposed flood-control reservoirs fail to operate, purple) and actual situation during the 1990 flood (light blue).

) on the situation at the

Figure 12. Impact of the proposed flood-control reservoirs with increased total volume (10106 m3

80 Achievements and Challenges of Integrated River Basin Management

Using the calibrated and validated combined hydrological (HBV-light) and hydraulic (HEC-RAS 5) models, we investigated the influence of the proposed flood protection measures (e.g. several flood-control reservoirs are to be built in the large natural flood area before the Celje city) on the flood safety. Moreover, using the hydraulic model HEC-RAS that is presented in Section 3.2, we also investigated the backwater effect on different tributaries in the Savinja catchment. Figure 14 shows an example of the backwater effect on the Ložnica River. It can be seen that due to the increased peak discharge on the Savinja River, the maximum water on the Ložnica River also increases. This increase is the largest for the cross section located near the rivers' confluence (about 0.6 m for peak discharge increase at 400 m3 /s) and generally, decreases for upstream river station. Moreover, the backwater effect is detected for the cross section that is located 1.5 km upstream of the confluence of the Savinja and Ložnica Rivers. Similar analysis was performed for other rivers (e.g. Hudinja and Voglajna; Voglajna and Savinja). The backwater effect can be up to 0.25 m for a peak discharge of 1000 m3 /s. This kind of analysis can be very useful also for the policy makers because it is essential to understand

Figure 14. The influence of the Savinja River on the Ložnica River (backwater effect) when the Ložnica input hydrograph is constant during different hydraulic model runs. Different coloured lines represent different cross sections on the Ložnica River where the number indicates river station from the confluence with the Savinja River upstream [m].

that some local measure can also have significant impact on the upstream flood conditions and also on the flood situation at the upstream tributary.

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#### 4. Conclusions

In this chapter, combined hydrological and hydraulic modelling was performed in order to investigate the influence of the proposed flood protection measures on the flood safety in the Savinja catchment and in the lower Sava River catchment in Slovenia. The main conclusions are: (1) some of the proposed flood protection measures have positive influence on the flood situation in the Savinja catchment and also at the confluence with the Sava River (either faster hydrograph propagation or peak discharge maximum water level reduction); (2) the main flood protection measures (several flood-control reservoirs) are to be built in the natural large floodplain area before the Celje city and potential problems with operation (or some other problems such as increased sediment transport at the reservoirs inflow) of these reservoirs would lead to the flood safety decrease; and (3) backwater effect in the Savinja River catchment can have a large impact on the flood safety, for example, the backwater effect at the confluence of Savinja and Ložnica Rivers can be up to 0.25 m at the 1000 m3 /s peak discharge of the Savinja River. These conclusions indicate that (small) local measures do not really play an important role in the global flood situation at the catchment and that some local measure can even worsen the flood situation upstream of the measure location. Therefore, complex models (hydrological and hydraulic) of the entire catchment are needed in order to really understand the flood behaviour and to select the most suitable measure that will have positive impacts on the flood safety. Moreover, the selection of the flood measure should also be in-line with the sustainable flood risk management, which means that environmental, social and economic conditions that are mutually connected should be investigated.

## Acknowledgements

The results of the study are part of the Slovenian national research Programme P2–0180: "Water Science and Technology, and Geotechnical Engineering: Tools and Methods for Process Analyses and Simulations, and Development of Technologies" that is financed by the Slovenian Research Agency (ARRS). We wish to thank the Slovenian Environment Agency for data provision.

#### Author details

Mitja Brilly\*, Andrej Kryžanowski, Mojca Šraj, Nejc Bezak, Klaudija Sapač, Andrej Vidmar and Simon Rusjan

\*Address all correspondence to<EMAIL_ADDRESS>
Faculty of Civil and Geodetic Engineering, University of Ljubljana, Ljubljana, Slovenia