Jurica ; Štiglić

Magdalena Ujević Bošnjak
email: magdalena.ujevic@hzjz.hr

Marina Héry

Livia Kurajica

Andrew Kinsela

Corinne Casiot

Krunoslav Capak

Kurajica

Ujević Bošnjak

; Héry

; Kurajica

; Kinsela

Bacterial diversity across four

Keywords: tap water, source water, bacterial diversity, disinfection by-products tap water, source water, bacterial diversity, disinfection by-products

Microbiota analysis from raw sequence data was performed using QIIME 2 2019.10 212 (Bolyen et al., 2019). Raw sequence data were demultiplexed by the sequencing center and 213 imported into QIIME 2 using the manifest file. Imported sequences were denoised, paired reads 214 were merged and chimeras removed using DADA2 (Callahan et al., 2016). All resulting

224

The rationale behind this approach was to maximize the number of samples retained while The alpha-diversity of ASVs was estimated with the software package PAST v4.03 232 (Hammer et al., 2001). The Shannon diversity (H) and Chao1 richness indices were both 233 calculated, with their medians and means determined using the Wilcoxon signed-rank test using Actinobacteria (34%), Firmicutes (16%), and Cyanobacteria (7%) were also predominantly 301 abundant in DWDS-A, whereas OD1 (17%), Bacteroidetes (9%), OP3 (4%) and TM7 (3%)

302

were additionally present in disinfected DWDS-B samples. In the non-disinfected waters of 303 DWDS-D, aside from Proteobacteria (56%), the most dominant phyla were OD1 (12%),

304

Nitrospirae, Chloroflexi (5%), Actinobacteria (5%) and Bacteroidetes (3%), whereas the 305 undisinfected DWDS-C samples were primarily Nitrospirae (11%), OD1 (6%) and 306 Acidobacteria (5%) (Figure 3a).

307

At the class level, Actinobacteria (34%), Betaproteobacteria (19%), Bacilli (15%),

353

In DWDS-A and -B, the relationships between physico-chemical parameters and the most 354 abundant taxa were also assessed using Spearman's correlation ( the case for the DWDS-A aquifer.

388

In sampling point A-T6, which was characterized by the highest level of free residual 389 chlorine (0.28 mg/L), the relative abundance (RA) of Alphaproteobacteria was particularly low 390 (< 1%) (Figure S3). This might be due to the fact that Alphaproteobacteria have been found to 391 be more sensitive to residual chlorine than Betaproteobacteria (Mathieu et al., 2009) ). This may be related to the fact that 421 groundwater from the well C-RW-Z8 was not used very often for water supply and corrosion 422 of the well screen was likely an issue. Although Sphingobium was also found in an , 2018;Stanish et al., 2016;Roeselers et al., 2015). Of the chlorinated systems studied,

445

DWDS-A was found to have consistently higher concentrations of free chlorine when compared 446 to DWDS-B (Figure 1a). Although the samples with higher levels of chlorine in DWDS-A had

495

was detected only in one sample (A-T1), and at a very low RA (0.1%). In line with our results, 496 a positive correlation between Sphingomonas, Mycobacterium and Ralstonia and HAAs (Table 497 3) may have an effect on promoting the formation of DBPs (HAAs), although they may not be 498

universally present in all DBP-containing DWDSs. These particular genera and their observed 524 3), indicating that a lack of Cu can suppress certain bacterial populations (Lehtola et al., 2004).

525

The associations between physico-chemical variables and bacterial community composition Sphingomonas (Table 3), which is a subgroup of Proteobacteria, which along with Firmicutes 547 dominated DWDS-A (Figure 3a). Individual comparisons between abundant taxa (genus) and 548 physico-chemical parameters (Table 3), suggested microbial abundance in DWDS-A waters

565

However, this influence is likely to be different for DWDS-A (Figure 4a) and B (Figure 4b),

mg L - 1 µg L -1 µg L -1 µg L -1 µg L -1 µg L -1 µg L -1 cfu mL -1 36°C/48h cfu mL -1 22°C/72h

F

  safe drinking water relies upon a combination of efficient source water 45 protection, adequate water treatment plant processes and a preservation of water quality in the with low DNA yields from background contamination could be improved 206 through the analysis of multiple methodological blanks. Importantly, the genera and their 207 observed prevalence within our studied DWDSs align with previous research (e.g., Montoya-208Pachongo et al., 2018), providing a degree of confidence in terms of the methodology used.

215

  amplicon sequence variants (ASVs) were aligned with mafft(Katoh et al., 2002) and used to 216 construct a phylogeny with fasttree2 (Price et al., 2010). ASVs were taxonomically assigned 217 using the naïve Bayes taxonomy classifier against the Greengenes 13_8 99% operational 218 taxonomic units (OTUs) reference sequences (McDonald et al., 2012). All features with 219 taxonomic assignments matching mitochondria or chloroplast as well as ASVs with frequency 220 lower than 0.1% were removed. Beta diversity metrics (weighted UniFrac (Lozupone et al., 221 2007), unweighted UniFrac (Lozupone and Knight, 2005), Jaccard distance, and Bray-Curtis 222 dissimilarity, and Principle Coordinate Analysis (PCoA) were estimated after samples were 223 rarefied to 12 500 sequences per sample and visualized with QIIME 2 view ordination emperor.

225

  keeping read numbers as high as possible. The sample with lowest number of sequences (6311 226 reads) was omitted from the analyses, where as the second lowest sample (with 12 572 reads) 227 defined the aforementioned 12 500 cut-off value -the point at which the alpha diversity 228 (Shannon metric) flattens(Cameron et al., 2021). Raw sequence reads have been deposited in

308

  Alphaproteobacteria (12%) and Cyanobacteria class 4C0d-2 (7%) were principally found in 309 disinfected DWDS-A waters. The community profile was somewhat different in the disinfected 310 DWDS-B waters, where Gammaproteobacteria (11%), Alphaproteobacteria (11%) and ZB2 311 (10%) predominated. Conversely, the undisinfected waters of DWDS-D were largely populated 312 by Epsilonproteobacteria (21%), Betaproteobacteria (17%) and Deltaproteobacteria (9%); 313 while Betaproteobacteria (23%), Deltaproteobacteria (15%), Alphaproteobacteria (11%) and 314 Nitrospira (11%) were most abundant at the undisinfected DWDS-C site (FigureS2).315At the genus taxonomic level, a high relative abundance of Mycobacterium spp. (32%), 316 Ralstonia spp. (18%), and the unknown genus from the order MLE1-12 (7%) were observed in 317 DWDS-A waters (Figure3b). DWDS-B was dominated by an unknown genus from the class 318 ZB2 (10%), Sulfuricurvum spp. (5%, only present in B-RW-Z18 and B-MRW), Bdellovibrio 319 spp. (4%) and an unknown genus from the phylum OD1 (4%). In the non-disinfected DWDS-320 D the most dominant genera were Sulfuricurvum spp. (21%) and an unknown genus from the 321 class ZB2 (6%). At DWDS-C, also a non-disinfected system, the principal genus was an 322 unknown genus of the family Comamonadaceae (8%). It is worth mentioning that 323 Sulfuricurvum spp. was dominant in raw water samples from two separate DWDSs (namely -B ). Despite the geographical distance between them, the hierarchical cluster analysis at 325 the genus level (Figure2a) shows a clear relationship between non-disinfected waters within 326 DWDS-D and raw water from DWDS-B. Additionally, raw water from DWDS-C (C-RW-Z8) 327 was found to be microbially divergent from the raw water in systems -B and -D, likely due to 328 their differing aquifer sources having distinct hydrogeological character (Figure2a).

  -A were generally low, with average values below 6 μg/L and 2 μg/L 332 for total THM and total HAA, respectively, and were in agreement with our previous findings 333(Kurajica et al., 2020) (Table1), and far below the MAL (100 and 60 µg/L; respectively for 334 THM and HAA). In our more extensive and long-term research that was conducted in parallel 335 with this study, average THM concentrations varied from 4 to 32 µg/L in DWDS-B while 336 average HAA concentrations ranged from 2 to 13 µg/L (Kurajica et al., 2021). The predominant 337 THM in DWDS-A and -B was trichloromethane (TCM). In our results, chlorinated 338 trichloroacetic acid (TCAA) and dichloroacetic acid (DCAA) were most abundant primarily in 339 DWDS-B and then in DWDS-A (TableS1, S2). Although our previous research conducted 340 solely at DWDS-A, found that dibromoacetic acid (DBAA) was consistently the major HAA 341 of concern (Kurajica et al., 2020), it was below the limit of quantification in this present study. 3423.5. Relationship between bacterial community and physico-chemical parameters 343To better visualize relationships between bacterial communities (genus level) and physico-344 chemical parameters, Canonical Correspondence Analysis (CCA) was performed on data from 345 the two disinfected DWDSs (-A and -B). According to the ordination plot for DWDS-A, the were oxidizability (KMnO 4 consumption), HAAs, THMs, bromide, dichloroacetic 348 acid (DCAA), and ORP while in a site with sampling points A-T4, A-T5 and A-T6 mostly were 349 free chlorine (Figure4a). For DWDS-B the major correlators with bacterial community 350 structure in raw and mixed raw water were arsenic, manganese and sulfate. Conversely, within 351 the distribution system sampling points (B-T1-BT8) the main correlators became free chlorine 352 and DBPs (THMs and HAAs).

423

  unchlorinated system previously(Liu et al., 2014), it is difficult to decipher whether there was 424 any influence due to the network biofilm in DWDS-C, given that, as already stated, the system 425 was disinfected with chlorine dioxide two weeks before sampling. A comparison of two non-426 disinfected systems delivering groundwater to consumers without any treatment (-C and -D), 427 revealed that bacterial diversity remained the same throughout the water supply chain. 428 However, the relative abundance of certain taxa was changed (e.g., while Alphaproteobacteria 429 dominated source waters, this was surpassed by Betaproteobacteria in end-user tap waters). 430 It should be noted that this observation on DWDSs -C and -D was made only at one 431 time point at each location. Indeed, it was apparent from our analyses that further investigations, 432 including additional sample locations along each network and temporal variations, are required 433 to verify our conclusions.

  been noted that disinfection reduces microbial diversity indices in 436DWDSs (Zhou et al., 2021). Our results support such findings since relatively higher diversity 437 indices were observed in unchlorinated DWDSs, as well as within the parts of networks with 438 lower chlorine levels (i.e., in DWDS-B). Experiments conducted in two French distribution 439 systems, which were exposed to reduced rates of residual chlorine over a two month period, 440 revealed that the reduction of free chlorine coincided with an absence of coliforms and still 441 maintained delivery of microbially and chemically safe water(Bertelli et al., 2018). Indeed, our the concentration of residual chlorine drives bacterial diversity and 443 community composition variation, in support of the existing information available (Bertelli et 444 al.

447

  lower bacterial diversity (Figure1a), correlations with the Shannon (R= -0.309) or Chao1 448 richness estimator (R= -0.145) indices revealed this was not statistically significant (p>0.05), 449 probably due to the small number of samples analyzed. It would be desirable to collect and 450 analyze additional samples especially over an extended period to further understand the strength 451 of this relationship.452Contrary to previous studies which showed that bacterial diversity decreases as water 453 moves through DWDSs(Han et al., 2020; Hull et al., 2017; Roeselers et al., 2015), those 454 examined in our study exhibited increasing diversity from the source (raw water) to the end-455 user. The differences in DWDS-B (at the Phylum level) clearly show increased RA of: 456 Bacteroidetes, Actinobacteria, GN02, Elusimicrobia, Nitrospirae, Planctomycetes, 457 Acidobacteria in the tap water (Figure S5); while at the class level: Alphaproteobacteria, ODI-458 ZB2(c), Deltaproteobacteria, Flavobacteria, Nitrospira, Elusimicrobia, GKS2-174, and 459 Planctomycetia all increased. Although a significant difference in diversity was not unexpected 460 in DWDS-B given the utilization of an advanced water treatment plant; it did, however, result 461 in higher measured diversity at consumer taps compared to raw water samples (B-RW-Z18 and 462 B-MRW). The most probable explanation is that the groundwater supplying DWDS-B is 463 sourced from very deep wells (average depth of 180 m), which are characterized by particularly 464 poor bacterial richness. This contrasts with the surface waters mostly used as raw water in other 465 studies, e.g., of the 46 source waters examined in Han et al. (2020), only five were 466 groundwaters. chemical properties of DWDS waters are critical factors determining DBP 470 formation. These include: disinfectant (type, dosage, contact time), water quality (pH, 471 temperature and precursor as type and concentration of NOM/TOC), pipe material (including 472 bacterial biofilm structure), flow-rate (hot weather and higher flow-rate promotes DBP 473 formation) and (seasonal) higher water temperatures causing higher concentrations of DBPs 474 (Dong et al., 2019; Srivastav et al., 2020). Multiple regression models developed as part of our 475 previous research showed strong (Croatia-wide) correlations between THMs levels and 476 temperature, TOC, bicarbonate concentrations and TNC 36 °C/48 h (R = 0.7), while HAAs 477 levels correlated with free chlorine, TOC, bicarbonate concentrations and TNC 36 °C/48 h (R 478 = 0.80) (Kurajica et al., 2020). Previous research has also shown that different classes of DBPs 479 behave differently across DWDSs (Guilherme and Rodriguez, 2015; LeBel et al., 1997; Mercier 480 Shanks et al., 2013; Rodriguez et al., 2004), with concentrations of THMs usually highest 481 towards the end of DWDSs, while HAA concentrations are typically elevated towards the 482 middle of DWDSs (Guilherme and Rodriguez, 2015). Among HAAs, monohalogenated acetic 483 acids have been shown to be more susceptible to biodegradation than dihalogenated acetic acids 484 (Bayless and Andrews, 2008), which is in line with our results, with the values of 485 monochloroacetic acid (MCAA) and monobromoacetic acid (MBAA) mostly below the limit 486 of quantification and in all samples being far below the MAL (

  499prevalence within disinfected DWDSs have also been found in previous studies (Calero 500Preciado et al., 2021; Montoya-Pachongo et al., 2018). According toZhang et al., (2009a, 501 2009b), certain Proteobacteria present in distribution systems have the ability to biologically 502 degrade HAAs. The results from DWDS-A also showed that the concentrations of zinc 503 significantly negatively correlated with the presence of DCAA (R= -0.971, p<0.05). Previous 504 research has shown that zinc, a component of galvanized water pipes, induced the 505 dehalogenation of HAAs via transformation to acetate, particularly for brominated acetic acids, 506 when compared to chlorinated HAAs (Wang and Zhu, 2010). 507 According to Hua et al. (2014), lignin phenols are precursors to the formation of DBPs, 508 generally following the order of TCAA > DCAA = trichloromethane, in chlorinated systems. 509 Trichloromethane was the most abundant DBP in all DWDS-A sampling points (Kurajica et 510 al., 2020), indicating the lignin phenol precursors had been degraded (Hua et al., 2014). of physico-chemical parameters (including DBPs) on the bacterial 513 communities 514 The physico-chemical parameters influencing the structure of bacterial communities 515 in DWDS-A and -B were determined by CCA (Figure 4). As previously mentioned, DWDS-A disinfection, while DWDS-B requires advanced water treatment to reduce 517 high concentration of metals such as iron, manganese and arsenic. Previous research has shown 518 that limiting the presence and type of organic carbon compounds can affect bacterial 519 community structure and composition (Prest et al., 2016). Similarly, a lack of trace elements 520 (especially metallic) can suppress the growth of certain bacterial populations (Finney and 521 O'Halloran, 2003). Given that concentrations of inorganic and organic components in the 522 DWDS-A water were minimal, a limited number of significant Spearman's correlations were 523 found. Copper (Cu) significantly negatively correlated with the Shannon diversity index (Table

526

  revealed complex inter-relationships, with multiple chemical and biological parameters 527 correlating, as would be expected in such large-scale field investigations. The first three 528 sampling points at DWDS-A (A-T1, A-T2, A-T3), which were primarily supplied from the 529 same water source, grouped together (Figure4a). Several bacterial taxa were negatively 530 correlated with free chlorine (including Alphaproteobacteria), whereas Lactobacillus was 531 negatively correlated with concentrations of DBPs (THMs and HAAs) (Figure 4a). Our results 532 are in line with previous studies showing that the physico-chemical parameters with the 533 strongest influence on the microbial composition in distribution systems were chlorine (Bertelli 534 et al., 2018), pH (Farkas et al., 2012), assimilable organic carbon (AOC) (Liu et al., 2002), P 535 and SO 4 (Pinto et al., 2014). In addition to these parameters, taxa in DWDS-A waters were also, 536 correlated with oxidizability (KMnO 4 consumption), THMs and HAAs, DCAA, Br -, and ORP 537 (Figure 4a). Collectively, these results may indicate that bacterial communities in DWDS-A 538 were related to the degradation of NOM and DBP formation, suggesting that it could potentially 539 play a role in DBP precursor composition and formation, as also hypothesized by Zeng et al., 540 (2021). Kurajica et al. (2020) also noted that the structural characteristics of NOM (humic-and ) were significantly correlated with DBP concentrations, along with 542 chemical parameters such as ORP, as well as certain trace metals (Pb, Cu, Mn and Fe). Sun et 543 al., (2014) revealed that alkalinity and oxidizability (KMnO 4 consumption) were positively 544 correlated with the RA of Proteobacteria and Firmicutes. Our results are in line with the above-545 mentioned study since oxidizability was strongly significantly correlated with genus 546

  549did not statistically correlate with many physico-chemical parameters. However, it should be 550 noted that metal and NOM concentrations were relatively low, very often below limits of 551 quantification.552In DWDS-B bacterial communities were shaped by certain physico-chemical 553 parameters, as evidenced by CCA (Figure4b). Raw water microbial community abundance 554 correlated with As, SO 4 and Mn. High levels of As, Fe, Mn in the raw water (B-RW-Z18) may 555 be linked to the abundance of the genera Sulfuricurvum and Thiothrix. These bacteria can use 556 sediment related elemental sulfur and sulfides as an electron source present in B-RW-Z18 and 557 it might cause dissolution of metals and metalloids associated with sulfide minerals. The 558 detected species Sulfuricurvum kujiense (RA 21.8%) may contribute to the anaerobic oxidation 559 of sulfide precipitates, sulfur and thiosulfate, all contributing to sulfur oxidation (Cron et al., 560 2019). Temperature, pH, and organic matter have been shown to influence the composition of 561 the microbial community in DWDSs (Zhou et al., 2021), and our results support these 562 observations, as per the length of the vectors relating to organic matter (expressed as 563 oxidizability, i.e., KMnO 4 consumption, SUVA, and UV_254 absorbance) shown in Figure 4a, 564 b. The microbial community composition can also be influenced by the levels of free chlorine.

  of the residual disinfectant is highly dependent on the level and reactivity of 567 the organic matter in the respective DWDS. As temperature can influence bacterial community 568 composition, it should be recognized that the differing seasons in which our samples were 569 collected may also impact upon these results. 570 Overall, our results indicated that water-associated physico-chemical parameters are 571 strongly related to bacterial diversity across DWDSs and further, more in-depth research is 572 needed to understand causal processes. 573 5. Conclusion 574 In this study, we provide the first 16S rRNA metabarcoding analysis for the in-depth 575 characterization of bacterial communities (expressed as amplicon sequence variants) from four 576 independent DWDSs in Croatia that differed in regard to source water characteristics, treatment 577 processes and disinfection practices. Disinfection was found to be a major driver shaping 578 bacterial community diversity and composition. As such, non-disinfected DWDS waters 579 grouped closely to each other, despite their geographic separation. The proportion of 580 Proteobacteria was higher in non-disinfected DWDSs (-C and -D) when compared to 581 disinfected systems (-A and -B). 582 Disinfection processes and their resulting by-products coincided with distinct 583 bacterial community changes. For example, Alphaproteobacteria was notably suppressed at 584 sampling points containing higher concentrations of free chlorine. Based on the present work, 585 genera such as Mycobacterium, Sphingomonas, Lactobacillus and Ralstonia in DWDS-A, and 586 the Shannon diversity index in DWDS-B, are related to concentrations of DBPs, and should 587 therefore be investigated further to ascertain their role in DBP formation and / or their

Figure 1

 1 Figure 1 Alpha diversity per sample grouped by a) Shannon diversity index b) Chao1 richness at genus level. L-left, R-right. Box and whisker plot of alpha diversity per sample grouped by a) Shannon diversity index b) Chao1 richness at genus level in A,B,CD-DWDSs. The box represents the first and third quartiles, the whiskers depict maximum and minimum values, while the square marker defines the median value. Number of samples for individual groups are as follows; A-DWDS N = 6, B-DWDS N = 14, C-DWDS N = 3, D-DWDS N = 3. L-left, R-right.

Figure 2 .

 2 Figure 2. (a) Clustering bacterial communities (genus level) of all 4 DWDSs based on Bray-Curtis matrix, 3-D beta diversity principal coordinates analysis (PCoA) plot showing (b) weighted Bray-Curtis distance of all observed samples (triplicates) in four distinct DWDSs. Blue, yellow, green, and red colors represent DWDSs A, B, C and D, respectively.

Figure 3

 3 Figure 3. a) Comparative average relative abundances with error bars of the top ten major phyla for the four different DWDSs A-D. For display purposes the minor phyla were grouped together as 'Others'' b) Comparative average relative abundances with error bars of the thirty-three major genera in DWDSs A-D. Legend categories (c)-class, (o)-order, and (f)-family correspond to unidentified ASVs at the genus level. Category titles B-RW refers to DWDS-B without raw water and B+RW with raw water.

Figure 4 .

 4 Figure 4. Canonical correspondence analysis (CCA) visualizing relative abundance of bacterial genera and physico-chemical parameters across a) A DWDS and b) B DWDS. Grey lines represent physico-chemical parameters, green squares are bacterial taxa (genus level) and sampling points are purple circles for A DWDS and red circles for B DWDS. The length of the grey lines indicates which physico-chemical parameters most strongly determined the genera distribution.

FigureFigure S2 .

 S2 Figure S1. 3-D beta diversity principal coordinates analysis (PCoA) plot showing unweighted Jaccard distance of all observed samples (triplicates) in four distinct DWDSs. Blue color is A, yellow is B, green is C and red is D DWDS. Squares are raw waters, circles are tap waters, cylinders are watertank and monument are hydrants

BetaproteobacteriaFigure S4 .

 S4 Figure S4. Average relative abundance at genus level (RA>1%) of sampling points in DWDS-C. Empty cell have relative abundance <1%.

  Table 3). As possible indicators

	370	The metabarcoding approach used in this study, was well suited for characterizing bacterial
	371	community dynamics in DWDSs when compared to culture-based approaches from which
	372	interpretations can be extremely limited. This observation is supported with our findings from
	373	bacterial colony growth (TNC) experiments, from which only raw and mixed raw water samples
	374	from DWDS-B (samples B-RW-Z18 and B-MRW), and samples from the hydrants and in
	375	DWDS-C (C-WT and C-T) produced cultivable microbial growth (Table 1).
	376	Regardless of the presence of residual disinfectants, the capacity of bacterial communities
	377	to thrive in drinking water has been documented in numerous systems worldwide (Dai et al.,
	378 379	F 2020; Holinger et al., 2013). In our study, Proteobacteria, OD1 (Parcubacteria), Actinobacteria, o r Bacteroidetes, Nitrospirae, Firmicutes, Cyanobacteria were the dominant phyla in all analyzed
	380	P samples across multiple DWDSs. Such observations are consistent with previous studies that
	381 382	e reported Proteobacteria as the most abundant bacterial phylum in drinking water (Bautista-de e r los Santos et al., 2016; DEHP, 2009; El-Chakhtoura et al., 2015; Hull et al., 2017; Liu et al.,
	383 384	2014; Vaz-Moreira et al., 2013; Zanacic et al., 2017). The phylum Cyanobacteria was found at R e v a higher abundance in the groundwater which supplied DWDS-A (Figure 3a), as previously
	385 386	i observed in another groundwater fed DWDSs (Bruno et al., 2018). The presence of e w cyanobacteria in groundwater can be explained by the connectedness with surface water, as is
	364 387	p<0.05), manganese (R= 0.828, p<0.05), chloride (R= 0.886, p<0.05), and sulfate (R=0.928,
	365	p<0.05); and negatively correlated with alkalinity (R= -0.943, p<0.05). The unknown genus of
	366	phylum OD1, class ZB2 was positively correlated with nitrate (R= 0.823, p<0.05). The Shannon
	367	diversity index positively correlated with nitrate (R= 0.805, p<0.05), and negatively correlated
	368	with potassium (R= -0.699, p<0.05), and the HAAs (R= -0.857, p<0.05).
	369	4. Discussion
		17

355 for the presence of DBPs in DWDS-A waters, HAAs positively correlated with the genera 356 Mycobacterium (R=0.841, P<0.05), Sphingomonas (R=0.812, p<0.05), and negatively with 357 Lactobacillus (R= -0.812, p<0.05). The Shannon diversity index was negatively correlated with 358 copper concentrations (R=-0.943, p<0.05) and positively correlated with iron concentrations 359 (R= 0.845, p<0.05).

360

In DWDS-B, the genus Flavobacterium was negatively correlated with alkalinity (R= -361 0.900, p<0.05), calcium (R= -0.900, p<0.05), magnesium (R= -0.975, p<0.05) and positively 362 correlated with manganese (R= 0.999, p<0.05). Nitrospira positively correlated with calcium 363 (R= 0.971, p<0.05), magnesium (R= 0.845, p<0.05), nitrate (R= 0.829, p<0.05), iron (R= 0.820,

Table S1 ,

 S1 S2). Previous studiesSzwetkowski and Falkinham, 2020). One study showed that the presence of 492 Methylobacterium DSM 18358 in drinking water at the relatively low abundance of 1% still 493 played an important role in reducing THM concentrations to 48% of their initial values within

		FEMS Microbiology Ecology	Page 22 of 63
	491	et al., 2018;
		F
		o r
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		e e r
		R e v
		i e w
	487	by Zhang et al. (2009a; 2009b) revealed that Afipia and Methylobacterium spp. can degrade
	488	certain HAAs (especially DCAA) in drinking water systems. Furthermore, Methylobacterium
	489	spp. have been shown to thrive only in disinfected drinking water, and its presence in DWDSs
		22
		ScholarOne Support 1-434/964-4100

490 could be used as an indicator of DBP contamination (Gallego et al., 2005; Montoya-Pachongo 494 24 h (Tsagkari and Sloan, 2019). However, in this study the presence of Methylobacterium spp.

  Ujević Bošnjak, M., Kinsela, A.S., Bieroza, M., Štiglić, J., Waite, T.D., Capak, Ujević Bošnjak, M., Kinsela, A.S., Štiglić, J., Waite, T.D., Capak, K., Pavlić, Z.,

	590 738 788 813	when raw water is sourced from deep underground aquifers. Therefore, an increase in diversity next-generation sequencing-based diversity studies. Nucleic Acids Res. 41, 1-11. Mathieu, L., Bouteleux, C., Fass, S., Angel, E., Block, J.C., 2009. Reversible shift in the α-, trees for large alignments. PLoS One 5. https://doi.org/10.1371/journal.pone.0009490
	591 739 789	is possibly due to organic matter loading in the distribution system, as well as from water https://doi.org/10.1093/nar/gks808 β-and γ-proteobacteria populations of drinking water biofilms during discontinuous
	592 790	(pre)treatments such as quartz sand (or other media) filtration. Our results also revealed the chlorination. Water Res. 43, 3375-3386. https://doi.org/10.1016/j.watres.2009.05.005
	593 741 791	presence of the sulfur oxidizing genus, Sulfuricurvum, from deep raw groundwaters, suggesting K., Romić, Ž., 2022. Mixing of arsenic-rich groundwater and surface water in drinking McDonald, D., Price, M.N., Goodrich, J., Nawrocki, E.P., Desantis, T.Z., Probst, A.,
	594 742 792	that biogeochemical processes in corresponding aquifers are complex and that some processes water distribution systems: Implications for contaminants, disinfection byproducts and Andersen, G.L., Knight, R., Hugenholtz, P., 2012. An improved Greengenes taxonomy
	595 743 793	remained undetected by geochemical monitoring alone. organic components. Chemosphere 292, 133406. with explicit ranks for ecological and evolutionary analyses of bacteria and archaea.
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	F o r P e e r R e v i e w possible to monitor DWDSs with a greater degree of accuracy, both in terms of describing the F o r P e e r R e v i e w F o r P e e r R e v i e w F o r P e e r R e v i e w Vartiainen, T., Martikainen, P.J., 2004. Microbiology, chemistry and biofilm spatial and temporal microbial community dynamics in a large-scale drinking water distribution system with multiple disinfectant regimes. Water Res. 139, 406-419. https://doi.org/10.1016/j.watres.2018.03.077 Prest, E.I., Hammes, F., van Loosdrecht, M.C.M., Vrouwenvelder, J.S., 2016. Biological Kurajica, L., 759 597 603 604 745 806 807 808 809 F o r P e e r R e v i e w F o r P e e r R e v i e w 795 Mercier Shanks, C., Sérodes, J.B., Rodriguez, M.J., 2013. Spatio-temporal variability of non-F o r P e e r R e v i e w F o r P e e r R e v i e w F F o r o r P e P e e r e r R e R v e v i e i w e w
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degradation. In addition, and contrary to previously reported results, we noted that bacterial 589 diversity may not necessarily decrease after advanced water treatment processes, particularly 598 collective microbial diversity and detecting fluctuations in bacterial communities between and 599 within networks. Thus, this study provides an opportunity for drinking water utilities to 600 understand the bacterial community structures and composition across various systems, 601 allowing a more detailed assessment to be made regarding the microbial implications of 602 reducing free chlorine concentrations and thereby DBPs.

740 Kurajica, L., 746 2021. Effects of changing supply water quality on drinking water distribution networks: 747 Changes in NOM optical properties, disinfection byproduct formation, and Mn 748 deposition and release. Sci. Total Environ. 762, 144159.

749 https://doi.org/10.1016/j.scitotenv.2020.144159 750 Kurajica, L., Ujević Bošnjak, M., Novak Stankov, M., Kinsela, A.S., Štiglić, J., Waite, D.T., 751 Capak, K., 2020. Disinfection by-products in Croatian drinking water supplies with 752 special emphasis on the water supply network in the city of Zagreb. J. Environ. Manage. 753 276, 111360. https://doi.org/10.1016/j.jenvman.2020.111360 754 LeBel, G.L., Benoit, F.M., Williams, D.T., 1997. A one-year survey of halogenated 755 disinfection by-products in the distribution system of treatment plants using three 756 different disinfection procesess. Chemosphere 34, 2301-2317.

757 https://doi.org/10.1017/CBO9781107415324.004 758 Lehtola, M.J., Miettinen, I.T., Keinänen, M.M., Kekki, T.K., Laine, O., Hirvonen, A., 761 Water Res. 38, 3769-3779. https://doi.org/10.1016/j.watres.2004.06.024 762 Lin, W., Yu, Z., Zhang, H., Thompson, I.P., 2014. Diversity and dynamics of microbial 796 regulated disinfection by-products within a drinking water distribution network. Water 797 Res. 47, 3231-3243. https://doi.org/10.1016/j.watres.2013.03.033 798 Montoya-Pachongo, C., Douterelo, I., Noakes, C., Camargo-Valero, M.A., Sleigh, A., 799 Escobar-Rivera, J.C., Torres-Lozada, P., 2018. Field assessment of bacterial 800 communities and total trihalomethanes: Implications for drinking water networks. Sci. 801 Total Environ. 616-617, 345-354. https://doi.org/10.1016/j.scitotenv.2017.10.254 802 Pinto, A.J., Schroeder, J., Lunn, M., 2014. Spatial-Temporal Survey and Occupancy-803 Abundance Modeling To Predict Bacterial Community Dynamics in the Drinking Water 804 Microbiome. mBio.asm 5, 1-13. https://doi.org/10.1128/mBio.01135-14.Editor 805 Potgieter, S., Pinto, A., Sigudu, M., du Preez, H., Ncube, E., Venter, S., 2018. Long-term 811 Microbiol. 7, 1-24. https://doi.org/10.3389/fmicb.2016.00045 812 Price, M.N., Dehal, P.S., Arkin, A.P., 2010. FastTree 2 -Approximately maximum-likelihood

Table 1 .

 1 Major physico-chemical characteristics of collected water samples.

	Sample	System	Type of location	Disinfection/ Treatment	Sampling date/2019	T °C	pH	EC µS cm -	TOC	Fe	Mn	As	HAA sum	DCAA	THM sum
								1

  TOC=total organic carbon, Fe, Mn, As = dissolved iron, manganese and arsenic concentration, HAA sum =sum of haloacetic acid, DCAA= dichloroacetic acid, THM sum= sum of trihalomethanes, cfu-colony forming units (Heterotrophic plate count method on Yeast extract agar), LOQ=Limit of quantification

	A-T1 A-T2 A-T3 A-T4 A-T5 A-T6 B-RW-Z18 B-MRW B-T1 B-T2 B-T3 B-T4 B-T5 B-T6 B-T7 Sample C-WT C-RW-Z8 D-T1 D-T2 D-T3 EC = electrical conductivity, Page 49 of 63 A Tap A Tap A Tap A Tap A Tap A Tap B Wellhead B Mixed raw water B Tap B Tap B Tap B Tap B Tap B Tap B Tap System Type of location C Watertank C Wellhead D Tap D Tap D Tap F Disinfection/ Sampling +/-March +/-March +/-March +/-March +/-March +/-March -/-February 13.6 7.3 14.0 7.5 16.0 7.2 12.0 7.1 15.7 7.0 13.0 7.3 15.4 7.1 -/-February 15.8 7.5 +/+ February 10.9 7.5 +/+ February 15.1 7.5 +/+ February 12.4 7.5 +/+ February 17.5 7.5 +/+ March 11.0 7.4 +/+ March 14.7 7.4 +/+ March 14.2 7.3 FEMS Microbiology Ecology 413 0.45 <LOQ 769 0.45 <LOQ <LOQ <LOQ 3.2 <LOQ 813 0.52 9.2 <LOQ <LOQ 832 0.43 10.9 <LOQ <LOQ 469 0.52 <LOQ <LOQ <LOQ 644 0.44 <LOQ <LOQ <LOQ 1115 2.7 2055 139 74.2 838 3.5 1175 75.4 212 821 2.8 <LOQ 1.9 24.1 824 2.8 8 2.7 24 823 2.8 13.1 2.6 24.7 838 2.7 10.8 2.7 23.6 852 2.8 <LOQ 2.5 24.2 799 2.7 <LOQ 1.6 22.5 798 2.7 <LOQ 1.6 22.7 date/2019 T pH EC TOC Fe Mn As -/-November 14.2 7.95 324 <LOQ 2.4 20.2 3.7 -/-November 13.6 8.07 325 <LOQ 6.1 73.7 4.9 -/-March 11.0 7.0 761 0.8 307 129 <LOQ -/-March 11.0 7.1 762 0.72 1032 132 <LOQ -/-March 13.2 7.0 757 0.75 414 126 <LOQ FEMS Microbiology Ecology o r P F o r P e e r R e v i e w e e r	0.4 0.8 0.7 0.25 0.25 0.1 --2.1 0.7 2.6 2.7 4.5 13.3 8.5 HAA sum -----	0.4 0.2 0.1 0.05 0.05 0.1 --1.4 0.6 1.9 2 1.8 4.4 4.3 DCAA -----	4.5 5.2 5.3 4.2 4.5 4.2 --9.5 9.2 9.5 3.7 9.2 9.2 5.1 THM sum -----	0 0 0 0 0 0 4 1 0 0 0 0 0 0 0 cfu mL -1 36°C/48h 12 -0 0 0	0 0 0 0 0 0 6 11 0 0 0 0 0 0 0 Page 48 of 63 cfu mL -1 22°C/72h 64 -0 0 0
	B-T8 B-H1-1 B-H1-2 B-H2-1	B B B B	Tap Hydrant Hydrant Hydrant	+/+ +/+ +/+ +/+	March June June June R e 14.0 7.3 18.9 7.7 17.8 7.6 20.1 7.6 v	737 595 632 785	2.5 2.7 2.4 4	<LOQ 4275 102.5 519.4	1.4 70.5 4628 4697	19.1 22.7 19.4 30.2	5.6 -3.9 4.7	3.3 -2.6 1.8	11.2 -6.6 4.8	0 >300 12 250	0 >300 128 268
	B-H2-2 C-T	B C	Hydrant Tap	+/+ -/-	June November 14.7 7.89 17.6 7.7 i e w	832 326	2.9 <LOQ	27.5 0.5	0.719 18.1	30.2 3.8	1.1 -	0.05 -	5.1 -	46 37	53 92
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Table 2

 2 Planktonic bacterial diversity and richness in each sample expressed as mean (genus level).

					Page 50 of 63
					Free
	System	Sample	Shannon index H	Chao1 richness	chlorine
					(mg L -1 )
		A-T1	2.17	46	0.17
		A-T2	1.56	39	0.19
	A	A-T3 A-T4	2.64 2.75	50 61	0.10 0.11
		A-T5	2.17	36	0.14
		A-T6	2.46	45	0.28
		B-RW-Z18	2.93	166	-
		B-MRW	3.13	166	-
		B-T1	4.39	377	0.025
		B-T2	4.31	368	0.025
		B-T3	4.25	362	0.025
		B-T4	4.21	368	0.025
	B	B-T5 B-T6	4.02 4.19	327 312	0.07 0.12
		B-T7	4.21	334	0.09
		B-T8	3.85	328	0.1
		B-H1-1	3.73	241	0.025
		B-H1-2	3.35	205	0.025
		B-H2-1	4.07	237	0.025
		B-H2-2	4.21	285	0.025
		C-T	4.11	169	-
	C	C-WT	4.22	157	-
		C-RW-Z8	3.72	126	-
		D-T1	3.14	234	-
	D	D-T2	3.50	234	-
		D-T3	3.11	212	-

Table 3 .

 3 Spearman correlation coefficient between most abundant taxa (genus level exept ZB2-class level ), alpha diversity (Shannon H index) and physio-chemical parametrs of observed samples in ZG and OS DWDSs. *Correlations are significant at 0.05 level and signed bold p*<0.05

	Page 51 of 63
			T (°C)	pH	HCO 3 -	KMnO 4	Ca 2+	Mg 2+	TOC	Mn	NO 3 -	Fe	Cu	K +	Cl¯SO 4 2-	DCAA	HAA sum
		Mycobacterium	0.371	0.203	0,086	0.698	0.086	0.116	-0.029	0.393	0.086	-0.034	-0.029	0.314	0.086	0.143	0.794	0.841*
	A-DWDS	Ralstonia Lactobacillus	0.200 0.086	0.319 -0.696	-0.086 0.371	0.759 -0.759	-0.086 0.371	-0.058 0.086	-0.029 -0.441 -0.655 0.655	-0.086 0.371	-0.034 0.270	-0.086 -0.371	0.086 0.086	-0.086 0.371	-0.029 0.853* 0.257 -0.736	0.725 -0.812*
		Sphingomonas	-0.314	0.174	0.143	0.941*	0.143	0.232	0.412	0.393	0.143	0.304	-0.200	0.371	0.143	0.314	0.500	0.812*
		Shannon H index	-0.200 -0.754	-0.600	-0.030	0.600	0.580	-0.696 -0.131	0.600	0.845* -0.943*	0.257	0.600	0.543	-0.412	-0.377
		OD1(p)_ZB2(c)	-0.200	0.421	-0.012	-0.297	0.176	0.246	0.226	0.024	0.823* -0.162	0.067	-0.529	0.474	0.012	0.476	-0.476
		Bdellovibrio	0.024	0.051	0.311	0.476	0.209	0.327	0.048	0.253	0.335	0.136	0	0.152	-0.024	-0.252	0.190	0.310
	B-	Flavobacterium	0	-0.791 -0.900*	-0.800	-0.900* -0.975* -0.872 0.999* -0.400	-0.354	0.400	0.359	-0.051	-0.154	0.200	0.200
	DWDS	Crenothrix	0.367 0.775*	0.234	0.333	0.140	0.06	0.575	0.257	0.546	0.313	-0.548	-0.079	0	0.134	-0.214	-0.393
		Nitrospira	-0.086	0.772	-0.943*	0.429	0.971*	0.845*	0.488 0.828* 0.829* 0.820*	-0.600	0.151	0.886* 0.928* -0.429	-0.600
		Shannon H index	-0.127	0.599	-0.318	-0.550	-0.019	-0.034	0.409	-0.195 0.805* -0.304	0.079	-0.699*	0.231	0.012	-0.619	-0.857*

Table S1 .

 S1 Physico-chemical characteristics of tap water from different points tested as environmental variables in CCA analysis in A DWDS

	Page 55 of 63		FEMS Microbiology Ecology
	monochloroacetic acid, DCAA= dichloroacetic acid, TCAA= trichloroacetic acid,
	MBAA=monobromoacetic acid, DBAA=dibromoacetic acid, HAA sum =sum of haloacetic acid,
	UV_254nm=ultra-violet absorbance at 254nm, SUVA=specific ultraviolet absorbance,
	ORP=oxidation-reduction potential
				A
	Parameters	A-T1	A-T2	A-T3	A-T4	A-T5	A-T6
	T_°C	14.0	16.0	12.0	15.7	13.0	15.4
	TU_ NTU	0.27	0.31	0.33	0.39	0.34	0.3
	pH	7.5	7.2	7.1	7.0	7.3	7.1
	EC_µScm -1 -20°C	413.0	769.0	813.0	832.0	469.0	644.0
	FreeCl 2 _ mgL -1	0.17	0.19	0.10	0.11	0.14	0.28
	HCO 3-_mgL -1	267.0	404.0	430.0	449.0	282.0	378.0
	KMnO 4 _mgL -1 O 2	1.7	1.1	2.5	0.25	0.25	0.25
	NO 3-_mgL -1	5.0	17.7	18.6	18.8	6.8	15.1
	Ca_mgL -1 Ca 2+ K_mgL -1 K + Na_mgL -1 Na + Mg_mgL -1 Mg 2+	72.0 1.6 F F 7.0 15.0 o 126.0 3.7 29.7 25.7 r o r	133.0 3.9 33.0 27.0	134.0 3.5 33.0 27.0	78.0 1.9 11.0 16.0	107.0 3.0 15.0 23.0
	TOC_mgL -1 C THM -sum_ µgL -1 TCM_ µgL -1 BDCM_ µgL -1 DBCM_µgL -1	0.45 4.5 1.5 1.0 1.1	0.45 5.2 P P 1.6 0.7 1.2 e 0.52 5.3 1.4 0.7 1.2 e r e e r	0.43 4.2 1.7 0.6 0.8	0.52 4.5 1.4 0.9 1.1	0.44 4.2 1.6 0.7 0.9
	TBM_µgL -1 Zn_ µgL -1 Fe_µgL -1 Mn_ µgL -1	0.9 12.8 3.0 3.2	1.7 15.6 3.0 0.5	2.0 34.7 9.2 0.5 R 1.1 104 10.9 0.5 e v R e v	1.1 47.8 3.0 0.5	1.0 24.9 3.0 0.5
	Al_µgL -1 Cu_mgL -1 Cl -_mgl -1 SO 4 2-_ mgL -1	5.3 0.0131 8.9 13	2.5 0.0354 48.6 42.3	2.5 0.0123 51.2 45.1	2.5 0.0084 53 44.9 i 2.5 0.0590 15.2 17.7 e w i e w	2.5 0.0128 28 21.2
	MCAA_µgL -1	<LOQ	<LOQ	<LOQ	<LOQ	<LOQ	<LOQ
	DCAA_µgL -1	0.40	0.20	0.10	<LOQ	<LOQ	0.10
	TCAA_µgL -1	<LOQ	0.6	0.6	<LOQ	<LOQ	<LOQ
	MBAA_µgL -1	<LOQ	<LOQ	<LOQ	<LOQ	<LOQ	<LOQ
	DBAA_µgL -1	<LOQ	<LOQ	<LOQ	<LOQ	<LOQ	<LOQ
	HAA-sum_µgL -1	0.40	0.80	0.70	0.25	0.25	0.10
	UV_254nm	0.03	0.01	0.03	0.03	0.03	0.00
	SUVA	0.00014	0.00005 0.00016 0.00013 0.00016 0.00000
	ORP_mV	801	818	793	792	700	830
	T=temperature, TU=turbidity, pH=potential of hydrogen, EC = electrical conductivity, FreeCl 2 =free
	residual chlorine, HCO 3 ¯=alkalinity, KMnO 4 =Oxidizability, NO 3-=nitrate,,Ca=calcium,
	K=potassium,Na=sodium, Mg=magnesium, TOC=total organic carbon, THM sum= sum of
	trihalomethanes, TCM=trichloromethane, BDCM=bromodichloromethane,
	DBCM=dibromochloromethane, TBM=tribromomethane, Zn, Fe, Mn, Al, Cu = dissolved zinc, iron,
	manganese and aluminium, copper concentration, Cl -=chloride, SO 4 2-=sulphate, , MCAA=
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Table S2 .

 S2 Physico-chemical characteristics of raw, tap and water from hydrant from different points tested as environmental variables in CCA analysis in B DWDS TBM=tribromomethane, Zn, Fe, Mn, Al, Cu, As = dissolved zinc, iron, manganese, aluminium, copper, arsenic concentration, PO 4 3-=phosphate, Cl -=chloride, SO 4 2- =sulphate, BrO3 -=bromate, MCAA= monochloroacetic acid, DCAA= dichloroacetic acid, TCAA= trichloroacetic acid, MBAA=monobromoacetic acid, DBAA=dibromoacetic acid, HAA sum =sum of haloacetic acid, UV_254nm=ultraviolet absorbance at 254nm, SUVA=specific ultraviolet absorbance, ORP=oxidation-reduction potential

	trihalomethanes, TCM=trichloromethane, BDCM=bromodichloromethane, DBCM=dibromochloromethane, Page 58 of 63 FEMS Microbiology Ecology
										B
		B-T6			B-T7	B-T8	B B-H1-1		B-H1-2	B-H2-1	B-H2-2
	T_°C	B-RW-Z18 14.7 14.2	B-MRW 14.0	B-T1 18.9	B-T2	B-T3 17.8	B-T4 20.1	B-T5 17.6
	T_°C TU_ NTU	13.6 0.32		0.35	15.8 0.33	10.9 23.2	15.1	12.4 2.07	17.5 3.19	11.0 0.62
	TU_ NTU pH	7.4	22.2		7.3	9.1 7.3	0.31 7.7	0.32	7.6	0.36	0.41 7.6	0.35	7.7
	pH EV_µScm -1 -20°C	7.3 799.0	798.0	7.5 737.0	7.5 595.0	7.5		7.5 632.0	7.5 785.0	7.4 832.0
	EV_µScm -1 -20°C FreeCl_ mgL -1	1,115.0 0.12	0.09	838.0 0.1	821.0 0.025	82.4	823.0 0.025	838.0 0.025	852.0 0.025
	FreeCl_ mgL -1 HCO 3-_mgL -1	556.0	-	555.0	-493.0	0.025 408.0	0.025	0.025 417.0	0.025 647.0	0.07 594.0
	HCO 3-_mgL -1 KMnO 4 _mgL -1 O 2	615.0 3.11		2.67	638.0 2.7	592.0 12.63	577.0	592.0 6.1	578.0 6.72	589.0 6.48
	KMnO 4 _mgL -1 O 2 NO 3-_mgL -1	7.2	3.47		6.9	4.08 5.7	2.04 6.0	3.29	6.7	3.24	2.71 6.6	3.14	7.7
	NO 3-_mgL -1 Br -_mgL -1	0.03	0.5		0.02	0.5 0.03	7.3 0.02	7.7		0.02	8.1	7.9 0.01	7.3	0.02
	Br -_mgL -1 Ca_mgL -1 Ca 2+ Ca_mgL -1 Ca 2+ K_mgL -1 K + Na_mgL -1 Na + Mg_mgL -1 Mg 2+ K_mgL -1 K + Na_mgL -1 Na + Mg_mgL -1 Mg 2+ TOC_mgL -1 C TOC_mgL -1 C THM -sum_ µgL -1 TCM_ µgL -1 BDCM_ µgL -1 DBCM_µgL -1 THM -sum_ µgL -1 TCM_ µgL -1 BDCM_ µgL -1 DBCM_µgL -1 TBM_µgL -1	0.43 79.0 130.0 1.8 75.0 42.0 F 78.0 o r 1.7 1.4 98.0 93.0 F 25 24 2.7 2.7 o r 2.69 ----P 0.09 77.0 87.0 1.5 99.0 28.0 1.8 79.0 23 2.5 3.5 ----e e r 0.03 71.0 87.0 1.4 95.0 28.0 2.5 59.0 20.0 2.73 2.82 9.5 6.8 2.0 0.7 9.2 5.1 11.2 -7 3.6 8.1 -P 1.7 1.1 2.4 -0.5 0.4 0.7 -0.05 0.05 0.05 e e r	0.03 85.0 1.4 93.0 28.0 2.82 9.2 6.5 2.0 0.7	0.03 70.0 87.0 1.6 93.0 28.0 1.8 66.0 19.0 2.37 2.78 9.5 6.8 2.0 0.7 6.6 4.8 1.3 0.5 0.05	0.02 86.0 87.0 1.4 93.0 28.0 1.6 96.0 28.0 3.99 2.71 3.7 2.3 0.9 0.5 4.8 3.5 0.9 0.4 0.05	87.0 0.04 85.0 1.5 105.0 1.3 99.0 28.0 2.86 28 2.8 9.2 5.9 2.1 0.7 5.1 3.7 1.0 0.4 0.05
	TBM_µgL -1 Zn_ µgL -1 Fe_µgL -1 Mn_ µgL -1 Zn_ µgL -1 Fe_µgL -1 Mn_ µgL -1 Al_µgL -1 Al_µgL -1 Cu_mgL -1 As_µg/L PO43-_mgl -1 Cu_mgL -1 As_µg/L PO43-_mgl -1 Cl -_mgl -1	-1 2,055.0 139 217 3 1.6 2.5 2.5 0.002 74.2 37.8 0.0546 0.104 62 3 1.6 2.5 22.5 22.7 10.0 10.0 19.4 19.7	-1 1,175.0 75.4 155 3 1.4 5.1 2.5 0.004 212.0 63.5 0.444 19.1 10.0 21.3	0.05 82.8 3 1.9 R e 0.05 75.1 8.0 2.7 v 1563.9 4275.1 70.5 3216 R e v 2.5 2.5 0.0135 0.0181 0.017 0.05 8.4 13.1 2.6 10.036 102.5 4.628 15.5 2.5 24.1 24.0 24.7 10.0 10.0 10.0 i e w 0.0052 0.00274 22.7 19.4 132 10 17.8 18.3 i e w	0.05 188.0 10.8 2.7 127.4 519.4 4.697 50.3 2.5 0.0072 23.6 10.0 0.00136 30.2 10 16.3	0.5 333 3 10.251 27.5 0.719 1.52 2.5 2.5 0.343 24.2 10.0 0.00044 30.2 10 17.3
	Cl -_mgl -1 SO 4 2-_ mgL -1	4	96.6		4	19.4 7.6	29.3 11.6	30.9	9.6	31.7	31.5 4.5	27.6	4
	SO 4 2-_ mgL -1 BrO3-_mgl -1	1	47.9		1	9 1	10.2 1.0	8.3		1.0	12	8.8 10.8	5.9	1.0
	BrO3-_mgl -1 MCAA_µgL -1	0.25		1		0.25	1 0.25	1 0.25	1		0.25	1	1 0.25	1	0.25
	MCAA_µgL -1 DCAA_µgL -1	4.4			-		4.3	-3.3	0.25 -	0.25	2.6	0.25	0.25 1.8	1.8	0.05
	DCAA_µgL -1 TCAA_µgL -1	5.6			-		3.5	-2.3	1.4 -	0.6		0.05	1.9	2 0.2	1.8	0.2
	TCAA_µgL -1 MBAA_µgL -1	3.3			-		0.2	-0.05	0.7 -	0.1		1.3	0.7	0.7 2.7	1	0.9
	MBAA_µgL -1 DBAA_µgL -1	0.05		-		0.05	-0.05	0.05 0.05	0.05	0.05 0.05	0.05 0.05	0.05 0.05
	DBAA_µgL -1 HAA-sum_µgL -1	13.3		-		8.5	-5.6	0.05 -	0.05	3.9	0.05	0.05 4.7	0.05	1.1
	HAA-sum_µgL -1 UV_254nm	0.029	-	0.008	-0.013	2.1 0.07212	0.7	2.6 0.07614	2.7 0.11898	4.5 0.09896
	UV_254nm SUVA	1.1	0.13		0.3	0.101 0.5	0.053 2.6	0.029	0.037 3.2	0.059 3.0	0.031 3.5
	SUVA ORP_mV ORP_mV T=temperature, TU=turbidity, pH=potential of hydrogen, EC = electrical conductivity, FreeCl 2 =free 4.8 2.9 1.9 1.0 1.3 2.2 588 601 430 452 457 452 462 1.1 142 123 421 419 420 404 411 residual chlorine, HCO 3 ¯=alkalinity, KMnO 4 =Oxidizability, NO 3-=nitrate,,Br¯=bromide, Ca=calcium,
	K=potassium,Na=sodium, Mg=magnesium, TOC=total organic carbon, THM sum= sum of
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  Figure S3. Comparation of average relative abundance of sampling points of nine bacteria on class level in A-DWDS. Remaining class did not grouped together as "Others" for better resolution. Empty cell have relative abundance <1%.

				FEMS Microbiology Ecology	Page 60 of 63
		100%
		90%
		80%
	Average relative abundance (%)	F o r P e e r 0% 10% 20% 30% 40% 50% 60% 70% A-T1 A-T2 A-T3 A-T4 4% 0% 0% 0% 2% 2% 3% 2% 2% 2% Gammaproteobacteria Planctomycetia Bacteroidia Clostridia 1% 2% 3% 10% 4C0d-2 9% 2% 14% 3% Alphaproteobacteria 22% 3% 19% 5% 20% 30% 40% 50% 60% F 10% o r 0% C-T C--WT Bacilli 4% 25% 42% Betaproteobacteria 27% 18% 15% 21% Actinobacteria 35% 66% 18% 10% A-DWDS Betaproteobacteria(c) 1% 2% P OD1(p) 1% 1% Entotheonellales(o) 2% 2% e Ellin6513(o) 3% 3% e Gallionellaceae(f) r Bacteria_unknown 2% 2%	A-T5 0% 1% 1% 52% 2% C-RW-Z8 A-T6 0% 2% 2% 42% 1% 38% 37% 5% 14% 2% 1% 3% 2% 6% 3%	Planctomycetia Bacteroidia Clostridia Gammaproteobacteria 4C0d-2 Alphaproteobacteria Bacilli Betaproteobacteria Actinobacteria
		Nitrospira OD1(p) Gallionella 4-29(f) C20(o) Cellvibrio Deltaproteobacteria© Sphingobium DTB120(o)	2% 2% 1% 2% 3% 4% 3% 5%	R e v i e w 1% 5% R 2% 5% 4% 4% e 2% 5% v 5% 1% i 8% e 4% 5% 12% w 5% 9%
		Comamonadaceae(f)	18%	4%
			ScholarOne Support 1-434/964-4100

  Figure S5. Average relative abundance at Phylum level (RA>1%) of sampling points in DWDS-B. Empty cell have relative abundance <1%.

	Page 63 of 63					FEMS Microbiology Ecology
	Relative average abundance (%)	Chloroflexi		1%	2%	2%	2%	2%	1%	2%	2%	1%		1%	B-H2-2
		Acidobacteria			3%	3%	2%	2%	1%	1%	2%	1%		1%	1%	1%
		k__Bacteria;p__ 3%	5%	1%	1%	1%	1%	1%	2%	2%	1%		2%
		Verrucomicrobia			1%	2%	2%	1%	2%	5%	5%	4%	3%	2%	2%	3%
		Planctomycetes			3%	2%	3%	2%	2%	2%	3%	2%	5%	3%	2%	5%
		Nitrospirae	2%	1%	2%	3%	4%	4%	4%	4%	2%	1%
		Elusimicrobia		1%	1%	2%	3%	3%	6%	8%	6%	1%			1%
		GN02	1%	1%	4%	5%	3%	4%	3%	1%	1%	1%	1%		5%	1%
		Actinobacteria	1%		1%	1%	1%	1%	3%	3%	4% 14% 5%	5%	2%	3%
		TM7											19% 33% 9%	3%
		OP3	11% 4%	3%	3%	4%	4%	5%	4%	5%	2%	2%	1%	1%
		Bacteroidetes	2%	3%	3%	3%	3%	3%	8% 16% 20% 32% 15% 16% 11% 15%
		OD1	23% 14% 20% 21% 22% 24% 22% 10% 13% 7% 17% 14% 16% 10%
		Proteobacteria	49% 63% 50% 46% 44% 43% 36% 36% 30% 31% 30% 20% 47% 52%
								DWDS-B, Phylum
					ScholarOne Support 1-434/964-4100

Richardson, S.D., 2011. Disinfection By-Products: Formation and Occurrence in Drinking

Acknowledgments

Declaration of interests 614

The authors declare that they have no known competing financial interests or personal 615 relationships that could have appeared to influence the work reported in this paper. 616 municipal drinking waters in the Ohio River basin, USA. PLoS One 11, 1-21. 839 https://doi.org/10.1371/journal.pone.0157966 840 Sun, H., Shi, B., Bai, Y., Wang, D., 2014 Table S1. Physico-chemical characteristics of tap water from different points tested as environmental variables in CCA analysis in A DWDS Table S2. Physico-chemical characteristics of raw, tap and water from hydrant from different points tested as environmental variables in CCA analysis in B DWDS

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text:
Jean-Baptiste Izard

Marc Gouttefarde

On the Cable Actuation of End-Effector Degrees of Freedom in Cable-Driven Parallel Robots

Keywords: Platform internal end-effector motion, static equilibrium, wrenchfeasible workspace analysis, available wrench set analysis

By routing the cables that support and drive the mobile platform of a Cable-Driven Parallel Robot (CDPR) to the moving parts of an end-effector (EE) installed on said mobile platform, it is possible with combined motions of the winches of the CDPR to drive this EE, such as the opening and closing of a gripper. Compared to CDPRs without internal EE, the cable tension distribution is then modified. In this paper, the static equilibrium of a CDPR platform with an internal EE driven by the supporting cables is analyzed, showing that an additional platform wrench needs to be balanced when force is applied on the EE. Both the magnitude of the wrench on the EE degrees of freedom and the way the cables generate motion at the EE influence this additional platform wrench. Considering a cable configuration and an external wrench applied on the EE, several EE designs are proposed where the EE motion is driven by one or two cables leading to different results in terms of workspace and wrench feasibility.

Introduction

Cable-Driven Parallel Robots (CDPRs) and Delta-type parallel robots share one design issue in industrial applications: Creating additional large-amplitude Degrees Of Freedom (DOFs) at the mobile platform leads in both cases to significant complexity. In the case of the Delta robot, the dynamic capabilities rely partly on having a lightweight mobile platform, but integrating an actuator in the platform adds weight. In addition, electric and pneumatic cables can be damaged by the numerous bending cycles and flutters caused by the rapid movements of the platform. In the case of CDPRs, most applications require motion at the end-effector (EE) mounted on the mobile platform: at least one DOF is generally required, frequently two, for instance generating a large rotation together with opening and closing a gripper. Space into the mobile platform and payload capabilities may not be an issue. However, when the workspace dimensions are large, it is complex to realize an electrical and/or pneumatic harness, connecting the platform to the robot base structure, which is reliable, cost-effective and does not hinder the platform movements.

In Delta robots, the problem has been circumvented by using one or more UPU kinematic chains in parallel to the three Delta kinematic chains. A notable design, proposed for the PAR4 and used in the Quattro robot marketed by Omron [START_REF] Nabat | Par4: very high speed parallel robot for pick-and-place[END_REF], consists in adding a fourth R-PAR actuated kinematic chain, identical to the original 3 Delta chains, and using the resulting actuation redundancy to generate a rotation created by means of an articulated mobile platform. This design has limited impact on the dynamics and workspace size of the robot. Other similar concepts have since emerged to add a DOF to the platform of a Delta-like robot, e.g. [START_REF] Krut | Heli4: a parallel robot for scara motions with a very compact traveling plate and a symmetrical design[END_REF][START_REF] Wu | Design analysis and dynamic modeling of a high-speed 3T1R pick-and-place parallel robot[END_REF], but none of them provides more than one DOF and all of them are based on rigid-link kinematic chains.

Rotational limitations in CDPRs have also led to design specific configurations that are able to generate a large rotation within the mobile platform. In [START_REF] Fortin-Côté | On the design of a novel cable-driven parallel robot capable of large rotation about one axis[END_REF], a rotational DOF within the platform is directly actuated by cables to generate rotations of large amplitudes. Another new type of spatial CDPR providing unlimited rotations about an axis is presented in [START_REF] Sun | Design of spatial adaptive cable-driven parallel robots with an unlimited rotation axis using the cable wrapping phenomenon[END_REF]. In this particular design, eight cables are wrapped around a cylindrical mobile platform. In [START_REF] Etienne | Design of a planar cable-driven parallel crane without parasitic tilt[END_REF], a planar CDPR with infinite platform rotation is introduced. It uses a mechanism embedded into the mobile platform actuated from the CDPR base by a cable loop and does not suffer from parasitic motions. Moreover, full unlimited 3-DOF rotations are obtained in [START_REF] Métillon | A cable-driven parallel robot with full-circle end-effector rotations[END_REF][START_REF] Métillon | Novel Design for A Cable-Driven Parallel Robot with Full-Circle End-Effector Rotations[END_REF] by using an embedded spherical wrist implemented with omni-wheels.

On the other hand, 6-DOF CDPRs are often redundantly actuated, typically using eight cables, in order to achieve large workspaces and/or to fully constrain the mobile platform. This actuation redundancy may also be used to drive additional motions inside the mobile platform. There are many ways to route cables inside a CDPR platform using pulleys and eyelets which direct them towards mobile elements to which they are attached [START_REF] Gosselin | Tight cable structure for e.g. haptic interface, has displacement unit displacing mobile member with respect to platform that is suspended inside framework by suspension cables, where unit is distinct to winders/unwinders of cables[END_REF]. This creates internal mobility within the platform, driven by the cables, but this also has an impact on the cable tensions. It is crucial for the operation of the CDPR that the introduction of this internal mobility does not hinder the static equilibrium of the platform.

The first contribution of this paper is the mathematical analysis of a CDPR with 𝑛 > 6 degrees of freedom and 𝑚 ≥ 𝑛 cables in order to determine the impact of introducing internal EE motions to an existing CDPR design, in particular in how it impacts the wrench applied to the platform.

Several ways to drive internal EE DOFs constructed on the base of an 8-cable suspended CDPR configuration are then proposed and analyzed to compare their performance in terms of wrench capabilities and workspace. This analysis considers one internal EE DOF but could be generalized to more DOFs in future works. From this perspective of wrench modification, designs suggested in [START_REF] Fortin-Côté | On the design of a novel cable-driven parallel robot capable of large rotation about one axis[END_REF][START_REF] Métillon | A cable-driven parallel robot with full-circle end-effector rotations[END_REF][START_REF] Métillon | Novel Design for A Cable-Driven Parallel Robot with Full-Circle End-Effector Rotations[END_REF] are discussed.

Wrench equations

Singularities set aside, a set of 𝑛 actuators can fully drive up to 𝑛 DOFs. A CDPR is redundantly actuated when there are more actuators than mobile platform DOFs: at a given platform pose, there exists infinitely many cable tension combinations that can balance a given wrench at the mobile platform. The same mechanism with less actuators than mobile platform DOFs is underactuated: at a given pose, only a limited set of wrenches can be balanced by cable tensions. Isostatic mechanisms have as many actuators as DOFs, and one combination of cable tensions corresponds to one platform wrench (and vice-versa).

The vector of cable forces 𝝉 is mapped into the wrench 𝒇 through the wrench matrix 𝑾 with 𝑛 rows and 𝑚 columns. The CDPR is fully actuated at the necessary condition that the rank of 𝑾 is equal to 𝑛. With forces and velocities being dually linked, 𝑾 can also be used to calculate the vector 𝒍 ̇ of cable length time derivatives (cable velocities) in function of the platform twist 𝒕.

𝒇 = 𝑾𝝉 ; 𝒍 ̇= -𝑾 𝑻 𝒕

(1)

The column vector 𝒘 𝒊 of 𝑾, of dimension 𝑛, represents the wrench generated by cable 𝑖 = 1. . 𝑚 at the level of the mobile platform for a unit cable force. In the case of a CDPR with massless cables, vectors 𝒘 𝒊 have a well-known expression given in Eq. ( 2), with 𝒃 𝒊 the vector representing the position vector of cable 𝑖 attachment point to the mobile platform in the platform reference frame, and 𝒖 𝒊 the unitary vector directed along the cable segment from the platform to the base.

𝑾 = [ 𝒘 𝟏 𝒘 𝟐 ⋯ 𝒘 𝒎] ; 𝒘 𝒊 = ( 𝒖 𝒊 𝒃 𝒊 × 𝒖 𝒊 ) (2)
Another description of 𝑾 is given in Eq. ( 3) where the row vectors 𝒗 𝒋 , 𝑗 = 1. . 𝑛, correspond to the rows of 𝑾;

𝑾 = [𝒗 𝟏 𝑇 𝒗 𝟐 𝑇 ⋯ 𝒗 𝒏 𝑇 ] 𝑇 (3)
Each 𝒗 𝒋 corresponds to the cable velocities that generate a movement on one of the translational or rotational degrees of freedom 𝑗 of the mobile platform.

Let us consider a CDPR with 𝑚 cables to which we add a supplementary cable 𝑚 + 1. The cable tension vector 𝝉 therefore gains an element, the cable tension 𝜏 𝑚+1 . Moreover, the wrench matrix 𝑾 gains a column 𝒘 𝒎+𝟏 representing the wrench generated by cable 𝑚 + 1 for a unit cable force. All platform DOFs being potentially impacted by this new cable, all components of 𝒘 𝒎+𝟏 are likely to be non-zero.

On the other hand, adding an internal EE motion in the mobile platform leads to adding an element to the wrench 𝒇, corresponding to the force or moment 𝑓 𝑛+1 to be generated on the EE DOF, and adding a row 𝒗 𝒎+𝟏 to 𝑾 built from the combination of cable velocities that generate a movement along this new DOF.

Let us now consider a CDPR with 𝑚 cables, 𝑛 degrees of freedom and 𝑘 ≤ 𝑚 -𝑛 internal EE DOFs driven by the cables. The wrench 𝒇 is then composed of the wrench 𝒇 𝒏 applied on the platform and of a set of forces/moments 𝒇 𝒌 , 𝑘 = 1 … 𝑚 -𝑛 on the platform internal EE DOFs. The wrench matrix 𝑾 can be decomposed in two submatrices: 𝑾 𝒏×𝒎 , the wrench matrix of the CDPR if there were no internal EE motion, as described in Eq. ( 2), and 𝑾 𝒌×𝒎 , the wrench matrix of the EE DOFs considered individually. These two matrices are considered to have full rank. Eq. ( 1) then becomes:

[ 𝒇 𝒏 𝒇 𝒌 ] = [ 𝑾 𝒏×𝒎 𝑾 𝒌×𝒎 ] 𝝉 (4)

Evaluation of static equilibrium

This section presents an analysis of the Constant-Orientation Wrench-Feasible Workspace (COWFW) and Available Wrench Set (COAWS) of various designs of CDPRs having internal EE DOFs. An example CDPR configuration is crafted taking inspiration from the CoGiRo CDPR configuration [START_REF] Gouttefarde | Geometry selection of a redundantly actuated cable-suspended parallel robot[END_REF]. It sits on a footprint measuring 12 x 8 x 6 m, with a box-shaped mobile platform frame measuring 1 x 1.4 x 1.5 m. The platform reference point is at the center of the pattern drawn by the cable fixing points on the lower side of the platform. The empty platform weighs 𝑚 0 =150 kg. The coordinates of the center of mass of the empty platform are (0,0,0.75) m. The resulting 6D wrench from gravity is denoted 𝒇 𝟔 . The cable diameter is 4 mm, weighing 65 g per meter.

Winches are designed with a safe working load of 2500 N (security factor of 4). The purpose of the CDPR is to carry a payload 𝑓 𝐸𝐸 =500 N that comes in addition to the platform weight. A vertical translational EE DOF is considered: the purpose of the EE is to move the payload up and down. We consider that 𝑓 𝐸𝐸 is applied vertically right below the platform reference point, therefore an additional platform wrench 𝒇 𝑬𝑬 = (0,0, 𝑓 𝐸𝐸 , 0,0,0) 𝑇 adds up to 𝒇 𝟔 when the load is carried. Different designs derived from this configuration will be evaluated in terms of COWFW and COAWS in sections 3.3 to 3.6. Details on how the COWFW and COAWS are calculated are given in sections 3.1 and 3.2, respectively.

Wrench-feasible workspace analysis

The COWFW is the set of positions of the CDPR mobile platform where a given external wrench or set of wrenches can be balanced by a set of cable tensions within a feasible tension set. The latter is defined as non-negative lower and upper bounds on the cable tensions. The lower bounds are defined for each cable as the positive cable tension required to have a length to sag ratio of 5 [START_REF] Gouttefarde | Simplified Analysis of Large-Dimension Parallel Cable-Driven Robots[END_REF]. The upper bound is defined by the safe working load of the winch. The method detailed in [START_REF] Gouttefarde | Geometry selection of a redundantly actuated cable-suspended parallel robot[END_REF] is used to test if a given pose belongs to the COWFW. Validity of a pose is also conditioned to cables not colliding with the platform. With the CoGiRo CDPR cable configuration, no collision occurs between the cables for any pose at reference orientation within the footprint. The search of the COWFW boundary is performed by testing a set of directions covering the space around the reference pose which is to be tested valid beforehand. Each direction is tested with a bisection algorithm and outputs a boundary position. All boundary positions are arranged to shape a boundary surface.

Available wrench set analysis

The COAWS can be defined as the set of wrenches that can be balanced by a cable tension set, with values within a feasible tension set, for all platform positions within a prescribed set, the platform orientation being fixed at the reference orientation. The tension feasible set is defined as in section 3.1. At each position, the COAWS is calculated using the hyperplane shifting method detailed in [START_REF] Bouchard | On the ability of a cable-driven robot to generate a prescribed set of wrenches[END_REF]. The COAWS for the given set of positions is the intersection of the COAWS of all individual positions. In the following analysis, the prescribed set of positions is a meshing of the constant orientation workspace representing 2/3 of the horizontal footprint dimensions and 1/2 of the vertical footprint dimension. This workspace corresponds to the prescribed one in the optimization of the CoGiRo CDPR configuration [START_REF] Gouttefarde | Geometry selection of a redundantly actuated cable-suspended parallel robot[END_REF], with the exclusion of rotations.

Once the COAWS is built, the intersection of the COAWS with a subspace of the wrench space, where some wrench components are fixed, can be computed. In the following, all platform wrench components are set at 0 except for the vertical force.

The wrench coordinate 𝑓 𝑍 along the vertical force direction is thereafter transformed into admissible additional mass to the platform 𝑚 𝑎𝑑𝑑 through the following equation:

𝑚 𝑎𝑑𝑑 = - 𝑓 𝑍 𝑔 0 -𝑚 0 (5)
where 𝑔 0 is the acceleration of gravity equal to 9.81 m/s, and 𝑚 𝑎𝑑𝑑 indicates the additional weight that the platform can be carry on top of platform empty weight.

In cases where the EE is driven by a combination of cables, another variable in the wrench is the force applied on the EE embedded in the platform. In the 2D space with coordinates in 𝑚 𝑎𝑑𝑑 and 𝑓 𝐸𝐸 , the COAWS is a convex polygon. Moreover, we consider that the force 𝑓 𝐸𝐸 drives the added mass 𝑚 𝑎𝑑𝑑 with a speed reduction ratio of 1/𝜂. A given value of 𝑚 𝑎𝑑𝑑 carried by 𝑓 𝐸𝐸 requires 𝜂 to be equal to 𝑚 𝑎𝑑𝑑 𝑔 0 /𝑓 𝐸𝐸 .

EE DOF driven independently

In a first design, the EE is driven independently from the cables. This is the reference design to which the designs discussed in the next sections will be compared. The effects of physically installing the EE actuators on board of the platform (transmission of energy and signal to the platform) are ignored.

Fig. 1 shows the corresponding COWFW, which size is evaluated at 9.6 x 6.3 x 4.0 m. As for the COAWS, the only variable left is 𝑚 𝑎𝑑𝑑 , valid from 0 to 261 kg.

Directly wiring a cable to the EE DOF

A direct solution to drive an internal EE DOF with a cable of a CDPR would be to draw one cable on the platform through an eyelet and then route this cable into the platform to actuate the internal EE DOF. Let us consider the example configuration defined at the beginning of Section 3. Instead of being fixed to the platform, cable 1 is run on an eyelet and routed to the payload through a block and pulley mechanism with 𝜂 cable leads routed on the block. This corresponds to a reduction ratio of 1/𝜂. With 𝒘 𝟏 . . 𝒘 𝟖 the columns of the wrench matrix of the configuration at the considered position without the driving of the EE DOF, Eq. ( 4

This equation corresponds to setting 𝜏 1 equal to 𝑓 𝐸𝐸 and thereafter solving the static equilibrium of the CDPR using only cables 2 to 8, with the gravity wrench 𝒇 𝟔 being incremented by -𝒘 𝟏 𝑓 𝐸𝐸 /𝜂, which is the effect of the cable 1 with tension 𝜏 1 . Considering all positions in the prescribed set leads to an empty COAWS, i.e. it is not possible to carry a payload of any magnitude this way in all positions. Four subsets of positions are then determined, which lead to different COAWS that do not intersect (Fig. 3).

Doubling a cable to drive a drum

The designs proposed in [START_REF] Métillon | A cable-driven parallel robot with full-circle end-effector rotations[END_REF][START_REF] Gosselin | Tight cable structure for e.g. haptic interface, has displacement unit displacing mobile member with respect to platform that is suspended inside framework by suspension cables, where unit is distinct to winders/unwinders of cables[END_REF] are meant to modify a CDPR cable configuration by selecting a cable which is doubled. Both cables from this doubling are driven by individual winches. Inside the platform, these cables are routed to a drum where they are wound in opposite directions. Running the winches of these two cables in opposite directions therefore actuate the internal EE DOF. Let us select cable 1 of a configuration and double it. The two corresponding cables are numbered 0 and 1, increasing the total number of cables to 𝑚 + 1. The fixing and drawing points of cables 0 and 1 are built from the former positions of the fixing and drawing point of cable 1: both are moved by a position vector denoted 𝒅 𝟎𝟏 in opposite directions in order to keep the unit vectors 𝒖 𝟎 and 𝒖 𝟏 directing cables 0 and 1 equal. Eq. ( 8) shows the wrench matrix for this configuration. Changing coordinates for 𝝉, replacing 𝜏 0 and 𝜏 1 by 𝜏 1 + = 𝜏 0 + 𝜏 1 and 𝜏 1 -= 𝜏 0 -𝜏 1 respectively, leads to:

[ 𝒇 𝟔 + 𝒇 𝑬𝑬 𝑓 𝐸𝐸 ] = [ ( 𝟎 𝟑 𝒅 𝟎𝟏 × 𝒖 𝟏 ) 𝒘 𝟏 𝒘 𝟐 . . 𝒘 𝒎 2𝜂 0 𝟎 𝟏×𝒎-𝟏 ] [𝜏 1 -𝜏 1 + 𝜏 2 . . 𝜏 𝑚 ] 𝑇 (9)
Which solves into the following set of equations:

𝜏 1 -= 𝑓 𝐸𝐸 /2𝜂 ; 𝒇 𝟔 + 𝒇 𝑬𝑬 -( 𝟎 𝟑 𝒅 𝟎𝟏 × 𝒖 𝟏 ) 𝑓 𝐸𝐸 /2𝜂 ⏟ 𝒇 𝟔 + = [𝒘 𝟏 . . 𝒘 𝒎 ][𝜏 1 + 𝝉 𝟐..𝒎 ] 𝑇 (10)
This set of equations corresponds to the static equilibrium of the CDPR without the internal EE, where the tension in former cable 1 is replaced by the sum 𝜏 1 + of the tensions in cables 0 and 1, and to which an extra wrench noted 𝒇 𝟔 + , with only torque values, adds to gravity wrench 𝒇 𝟔 + 𝒇 𝑬𝑬 . The magnitude of 𝒇 𝟔 + is proportional to 𝑓 𝐸𝐸 and to the norm of the cross product 𝒅 𝟎𝟏 × 𝒖 𝟏 . In order to evaluate this design, the example configuration is modified by doubling cable 1 with 𝑑 01 = [0 0 0.1𝑚] 𝑇 . 𝑓 𝐸𝐸 is once again set equal to 500 N and 𝜂 to 1. The COWFW and COAWS are shown in Fig. 4 and Fig. 5.

In the case of the robot presented in [START_REF] Métillon | A cable-driven parallel robot with full-circle end-effector rotations[END_REF][START_REF] Métillon | Novel Design for A Cable-Driven Parallel Robot with Full-Circle End-Effector Rotations[END_REF] with 6 degrees of freedom and 8 cables, cables are assembled by pairs which are routed towards 4 actuated drums installed in the platform. Middle points of fixing point pairs shape a square. 𝑓 𝐸𝐸 is replaced by a vector of torques (𝑓 𝐸𝐸 𝑖 )

𝑇 , 𝑖 = 1. .4, actuated on the drums, with 𝜂 matching the radius of the drums. Cable tensions are denoted 𝜏 𝑖𝑗 , 𝑖 = 1. . .4 and 𝑗 = 1. . .2, 𝜏 𝑖 + = 𝜏 𝑖1 + 𝜏 𝑖2

and 𝜏 𝑖 -= 𝜏 𝑖1 -𝜏 𝑖2 , Eq. ( 4) becomes the following equation:

[ 𝒇 𝟔 + 𝒇 𝑬𝑬 (𝑓 𝐸𝐸 1 . . 𝑓 𝐸𝐸 4 ) 𝑻 ] = ∑ [ ( 𝟎 𝟑 𝒅 𝒊 × 𝒖 𝒊 ) 𝒘 𝒊 2𝜂 𝟎 1×𝑚-1 ] 𝑖=1..4 [ 𝜏 𝑖 + 𝜏 𝑖 -] (11)
which solves into Eq. ( 12), corresponding to an underactuated CDPR with 4 cables attached to the platform at the vertices of a square, with the addition of a perturbation

With the CDPR being underactuated, this perturbating wrench leads to a perturbation of the position. This would explain why, in [START_REF] Métillon | Novel Design for A Cable-Driven Parallel Robot with Full-Circle End-Effector Rotations[END_REF], the platform shows erratic movements when EE motion is performed while the platform is suspended. Friction in the drums require torque from the EE DOFs, which turn into additional wrench on the platform. As the CDPR static equilibrium is equivalent to that an underactuated cable configuration, this wrench generates the shift in position and orientation visible in the video in [START_REF] Métillon | Novel Design for A Cable-Driven Parallel Robot with Full-Circle End-Effector Rotations[END_REF].

Running a pair of cables towards a drum carrying the load

The design proposed in [START_REF] Fortin-Côté | On the design of a novel cable-driven parallel robot capable of large rotation about one axis[END_REF] features a platform built with a vertical central rod to which 6 cables are attached in 2 clusters of 3 cables drawn upwards and downwards, which makes for an overconstrained CDPR with 5 DOF, excluding the rotation of the central rod. The central rod is then fixed by a drum coaxial to it to which two cables are attached, drawn from the base in a mostly horizontal direction, in order to pull on the drum in antagonistic directions. Similar constructions are also proposed in [START_REF] Gosselin | Tight cable structure for e.g. haptic interface, has displacement unit displacing mobile member with respect to platform that is suspended inside framework by suspension cables, where unit is distinct to winders/unwinders of cables[END_REF]. We therefore study in this section the design where two cables from the initial configuration are taken through an eyelet to an EE with a reduction ratio 1/𝜂. The number of cables is unchanged. The wrench matrix is augmented with a row vector with nonzero values for the cables driving the EE and 0 for the other cables. We choose to drive the EE with cables 1 and 2. With 𝜏

The corresponding COWFW (𝑓 𝐸𝐸 = 500 N, 𝜂 = 1) and COAWS are drawn in Fig. 6 and Fig. 7.

Discussion -Comparison of designs

A series of cable-driven configurations of an EE embedded in a CDPR mobile platform (internal EE), built from a CDPR configuration derived from the CoGiRo configuration detailed in Table 1, have been studied with respect to their workspace with a payload set at 𝑓 𝐸𝐸 = 500 N and payload capability within a designated workspace of 8 m x 5.3 m x 3 m. These results are compiled in Table 2. It is important to note that the capability in 𝑓 𝐸𝐸 depends on the mass carried by the platformwith the possibility of using 𝑓 𝐸𝐸 to carry such mass, and with possibility of integrating a speed reduction mechanism that amplifies force. The COAWS polygons as expressed in this paper, showing 𝑓 𝐸𝐸 in function of added mass to the platform 𝑚 𝑎𝑑𝑑 , help determining the reduction ratio in function of the desired load.

Applying speed reduction impacts two parameters. The first parameter is EE velocity, since it will be reduced from the cable speed by the same ratio. The second parameter is, for the same reason, stroke. The stroke at the EE divided by the reduction ratio goes on top of the stroke required for the movement of the platform in the workspace in order to get the actual stroke required from the drum.

Conclusion and perspectives

The work presented in this paper is an initial approach to the topic. Many cable combinations being possible, using different sets of cables, with various reduction ratios and directions of pull, it is possible that other combinations than the ones presented here would deliver larger forces across the prescribed set of positions and/or have more limited impact on the static equilibrium and on workspace size. In order for the idea of using cables for driving EE to deliver high impact results, we will work in the future on a theoretical approach to evaluate the impact on the platform static equilibrium of any combination of cables driving an EE. This will allow determining which of these combinations would lead to more favorable results. This theoretical approach should be generic so that it can be applied to any CDPR cable configuration and should exclude requiring additional winches. Besides, these developments are meant to lead towards the design of a 𝑚-cable CDPR with 𝑚 degrees of freedom including internal translational and/or rotational EE DOFs.

Fig. 1 .

 1 Fig. 1. COWFW of the example configuration with a translational EE DOF driven separately against 𝑓 𝐸𝐸 = 500 N. Coordinates are shown in m.

Fig. 2 .

 2 Fig. 2. COWFW of the example configuration where the translational EE is driven by cable 1 (blue cable) against 𝑓 𝐸𝐸 = 500 N with 𝜂 = 1. Coordinates are shown in m.

Fig. 3 .

 3 Fig. 3. AWS of the design featuring cable 1 driving the translational EE DOF. The AWS is shown on the left, and corresponding position sets are shown with the same colors on the right, where coordinates are shown in m.

Fig. 4 .

 4 Fig. 4. COWFW with the translational EE driven by a combination of cable 0 and cable 1 obtained by doubling original cable 1 against 𝑓 𝐸𝐸 = 500 N with 𝜂 = 1. Coordinates are in m.

Fig. 5 .

 5 Fig. 5. COAWS of the example configuration with cable 1 being doubled to drive a translational EE DOF. Limiting positions are shown on the right as blue crosses. Black dots show non-limiting feasible positions which are part of the prescribed set of positions.

Fig. 6 .

 6 Fig. 6. COWFW of the example configuration where the translational EE is driven by a combination of cable 1 (blue) and cable 2 (red) against 𝑓 𝐸𝐸 of 500 N with 𝜂 = 1. Coordinates are in m.

Fig. 7 . 1 𝑚

 71 Fig. 7. COAWSs of the configuration with cable 1 and 2 driving antagonistically a translational EE DOF. Limiting positions are shown on the right as blue crosses. Black dots show non-limiting feasible positions which are part of the prescribed set of positions.

Table 1 .

 1 Example configuration drawing and fixing point coordinates in m.

  wrench 𝒇 𝟔 + with non-zero values on torque directions proportional to values 𝑓 𝐸𝐸 𝑖 , which is applied in addition to 𝒇 𝟔 :

		𝜏 𝑖1 -𝜏 𝑖2 = 2𝑓 𝐸𝐸 𝑖 /𝜂 , 𝑖 = 1. .4
		𝒇 𝟔 + 𝒇 𝑬𝑬 -∑ 2 ( 𝒅 𝒊 × 𝒖 𝒊 𝟎 𝟑 𝑖=1..4 ⏟	) 𝑓 𝐸𝐸 𝑖	/𝜂	= [𝒘 𝟏 . . 𝒘 𝟒 ][𝜏 𝑖 + ] 𝑖=1..4
	{	𝒇 𝟔 +

  𝒘 𝟏 + 𝒘 𝟐 ) 𝒘 𝟏 . . 𝒘 𝒎 ][𝜏 12 + 𝜏 3 . . 𝜏 𝑚 ] 𝑇

	[	𝒇 𝟔 + 𝒇 𝑬𝑬 𝑓 𝐸𝐸	] = [	𝒘 𝟏 -𝒘 𝟐 𝒘 𝟏 + 𝒘 𝟐 𝒘 𝟑 . . 𝒘 𝒎 2𝜂 0 𝟎 1×𝑚-2	] [𝜏 12 -	𝜏 12 +	𝜏 3 . . 𝜏 𝑚 ] 𝑻	(13)
	which solves into Eq. (14), which corresponds to the static equilibrium of a modifica-
	tion of the original configuration where a cable runs from drawing point 1 to fixing
	point 1, then through eyelets and pulleys to fixing point 2, and then to drawing point 2;
	and to which an additional wrench 𝒇 𝟔 + is applied, proportional to 𝑓 𝐸𝐸 :
						𝜏 12 -=	𝑓 𝐸𝐸 2𝜂
		𝒇 𝟔 + 𝒇 𝑬𝑬 -(𝒘 𝟏 -𝒘 𝟐 ) ⏟	𝑓 𝐸𝐸 2𝜂	= [(
	{		𝒇 𝟔 +
						12 + = 𝜏 1 + 𝜏 2 and 𝜏 12 -= 𝜏 1 -𝜏 2 , Eq. (4) leads to:

Table 2 .

 2 Example configuration drawing and fixing point coordinates in m.

		Sec	Workspace size with	Payload capabil-
	EE drive	tion	𝑓 𝐸𝐸 = 500 N (m)	ity	Comments
	Independent	3.3	9.6 x 6.3 x 4.0	261 kg	Effects of umbilical are neglected
	One cable, di-rectly acting	3.4	(non-prismatic)	(217 to 261 kg)	Does not comply with the designated work-space
	One doubled ca-ble, both acting antagonistically	3.5	Z = 0: 6.1 x 3.9 (off-set on XY) Z = 4: 9.6 x 6.2	261 kg, with |𝑓 𝐸𝐸 | < 388 N	Requires an additional winch with respect to original configuration
	Two cables, act-ing antagonisti-cally	3.6	(non-prismatic -dra-matic effect at low Z)	245 kg, with 39 N<𝑓 𝐸𝐸 <284 N	Important impact on workspace; unidirec-tional 𝑓 𝐸𝐸 capacity