1

Microbial Corrosion in Heating & Cooling Water Loop Systems –

Findings from Bacterial Community Analysis

Marlies WIEGAND1, Oliver OPEL1, Karsten NEUMANN2

1Fachhochschule Westküste, Heide, Germany, wiegand@fh-westkueste.de ; opel@fh-westkueste.de

2Leuphana Universität, Lüneburg, Germany, kneumann@leuphana.de

Abstract The operation of heating and cooling water loop systems depends on a combination of physico-

chemical as well as biotic factors. Microorganisms present in water loop systems play an essential role in the

clogging of pipes and other components as well as in the degradation of metal materials. Some single

microorganism species have received significant attention in the context of microbially influenced corrosion

(MIC). However, it is clear that the most aggressive MIC occurs in complex microbial consortia and that

synthetic media with artificially composed groups of co-operative microorganisms fail to give a

representative situation. Thus, there is a lack of reports on observations in the bacterial community

composition and interpretation of the observed diversity patterns as a function of contextual environmental

parameters. This study describes for the first time similarity patterns in the microbiomes of existing heating

and cooling water loop systems in use based on 16S rRNA sequence analysis. Differences in the bacterial

communities and in diversity indices are discussed with regard to spacial (origin of fill water) and

operational factors (temperature).

Keywords building services; bacterial community; 16S rRNA gene sequencing; similarity; temperature

Introduction Water loop systems in buildings supply the human or industrial needs for thermal comfort and

thermoregulation while becoming increasingly energy efficient and complex [1]. Reliable operation

of heating and cooling systems is only possible if certain physical, hydro-chemical and biotic

conditions are met. Under non-optimum conditions, the synergistic effects of deposition and

corrosion may cause major problems [2]. Corrosion and scales on pipe surfaces may lead to

clogging of heat transfer equipment, loss of system efficiency, localized corrosion attacks, corrosion

damage of sensitive components or corrosion failure of the overall system which may lead to

unscheduled downtimes or shutdowns and respective maintenance costs [1,2].

The first understanding that microorganisms take part in the corrosion of metallic materials is dated

back to the year 1934 [3] or even to 1910 [4] [5]. Microbially influenced corrosion (MIC) is

described as a "consequence of coupled biological and abiotic electron-transfer reactions, i.e. redox

reactions of metals, enabled by microbial ecology" [6]. Numerous mechanisms by which

microorganisms affect the rates of corrosion have been described [7]. No single unified concept or

universal mechanism has yet been found [7]. The list of described causative microorganisms and

mechanisms is continuously growing [8]. Special attention is paid to sulfate reducing bacteria

(SRB) e.g. Desulfovibrio sp. [9] which corrode metals by cathodic polarization [3]. Microbial

sulfide production at the metal surface is known to be inhibited by the metabolic process of

denitrification [10]: addition of nitrate to the medium leads to lower sulfide concentrations which

are detected deeper within the biofilm [10]. However, nitrate reducing biofilm is also able to initiate and accelerate corrosion [11].

The secretion of slime by slime formers such as Pseudomonas sp. traps particulates and nutrients

and enables bacterial colonies to propagate [12]. A complex biofilm grows, possibly affecting heat

transfers [12]. This process is known as “biofouling” [12]. Biofilm formation may initiate corrosion,

change the mode of corrosion or the rate of corrosion attack [12]. Under biofilm, parameters such as

temperature, pressure, concentration of a solutes and the pH may differ considerably from those of

mailto:wiegand@fh-westkueste.demailto:opel@fh-westkueste.demailto:opel@fh-westkueste.demailto:kneumann@leuphana.de

2

the bulk water [12]. Apart from biofilms affecting the corrosion of metals, some mechanisms

involve corrosive, often acidic bacterial metabolites (e.g. elemental sulfur [13–16], sulfuric acid or

acetic acid), microbially generated hydrogen [3,7] or complexation of metals from passive,

protective oxide/hydroxide films on the metal surface by exopolymeric materials [3,6]. MIC

mechanisms may also be initiated by iron oxidizing bacteria e.g. Gallionella sp. [7,16].

Regardless of the proven contributions to corrosion by single causative organisms, it is established

that the most aggressive MIC takes place in the presence of microbial consortia in which many

physiological types of bacteria, including metal-oxidizing bacteria (MOB), SRB, acid-producing

bacteria (APB), and metal-reducing bacteria (MRB) interact in complex ways within the structure

of biofilms [17]. MIC found to be becoming more prevalent nowadays due to aging equipment and

increased awareness [5]. The inherent heterogeneity in water loop designs across different buildings

[18] provides a high diversity of microbial niches. Differences also arise from the quality of water

differing from one source to another: one water body may have clear, clean soft water that is mildly

corrosive, and yet another may have polluted hard water having scale-forming tendencies [12].

However, in drinking water (which serves in most cases as fill water for heating or cooling systems

[19]) patterns in spatial dynamics were found to be weaker than temporal trends i.e. seasonal

cycling, which is reproducible on an annual scale and correlates with temperature and source water

use patterns [20].

The microbiology of heating and cooling water loop systems was until now not investigated in other

studies. Experimental works using real field bacterial consortia for microbial corrosion assessment

take it e.g. from wastewater treatment plants [21] or oilfields [5]. No experimental works are known

which use consortia adapted to the highly specific conditions in closed heating or cooling water

loop systems characterized by nutritional stress, excess pressure and partly higher temperatures.

It is stated that modern methods in molecular biology should permit to understand more fully the

role and mechanisms of corrosion of metals and alloys [3]. In recent years, cultivation-independent

molecular techniques such as quantitative polymerase chain reaction (qPCR) of the 16S rRNA gene

have gained importance [17]. This is related to the limited fraction of bacteria which are cultivable

[17]. 16S rRNA analysis is applied in several neighboring research domains e.g. in quantification of

the copy numbers of total bacteria in hot water plumbing installations in buildings [18], in aquifer

thermal energy storage (ATES) [22] or in the production water of a petroleum reservoir, where the

linkage of temperature gradients, microbial community structure and biocorrosion of carbon steel

was studied [23]. However, there is a lack of reports on observations in the bacterial community

composition in closed water loop systems and interpretation of the observed diversity patterns as a

function of contextual environmental parameters.

Previous experiments in different lab-scale test systems [1] suggest that the biome in the well

performing system with low iron concentrations in the bulk fluid is more balanced compared to two

poorly performing test systems where single genera (e.g. Pseudomonas spp. and Acidovorax spp.)

dominated the community. Concerning hydrochemistry data, the worst conditions in heating and

cooling systems were found in those systems treated with chemicals which are meant to prevent

corrosion [19]. Here, we present for the first time findings of the bacterial genera and the bacterial

composition in the bulk fluid of heating and cooling systems i.a. from high-rise office buildings and

buildings with retail spaces and fill volumes >10 m³. Due to the focus on real operating systems, no

biofilms were investigated. This study is meant to contribute to the basic understanding in this

object of study.

3

Materials and Methods

In this investigation, a total of 35 samples from 34 fluids of closed heating and cooling water loop

systems in 21 buildings was characterized using ribosomal RNA (16S rRNA). Water samples and

field parameters were assessed as well. The closed heating and cooling water loop systems are

located i.a. in administrative buildings, high-rise office buildings, factories and buildings with retail

spaces. They differ in materials, fill-up waters and prevalent operation modes. For some circuits,

corrosion effects were known prior to analysis [1]. Among the systems are primary and secondary

circuits and most fill volumes were ˃ 10 m3. Three systems are combined heating/cooling systems.

Ten include corrosion inhibitors or other water treatment additives.

Prior to sampling, uninterrupted usual operation of the respective system for at least 24 hours was

arranged, as communicated to the building operators, to assure a representative sample constitution.

Spigots were fully opened and rinsed. For molecular biological analysis, samples of 800 mL were

collected into 1000 mL sterilized screwcapped PP flasks (sampling Kit "Blue BioSeq" by Blue

Biolabs, Berlin, Germany) and directly filled up with 200 mL denatured ethanol (sampling Kit

"Blue BioSeq"). Bacterial ribosomal RNA was extracted and purified. PCR amplification of 16S

rRNA fragments, sequencing, subsequent screening for chimeras and alignment search (with Basic

Local Alignment Search Tool (BLAST)) was assigned to an external service provider. Sequences

with no alignment were removed.

Samples for elemental composition were collected into two 10-mL polypropylene tubes (Sarstedt)

and stabilized with HNO3 (SupraPur, Merck). One of the two samples was filtered through 0.22 µm

cellulose acetate filters (Rotilabo®, C. Roth) immediately after sampling for determination of

ferrous iron. For determination of total organic carbon, samples were collected into 40-mL clean

dry glass vials.

Elemental compositions, anions, and TOC were determined in the laboratory while dissolved

oxygen, pH, temperature and conductivity were recorded on-site. Elemental analysis was done by

ICP-OES (Perkin Elmer 3300 RL; multi element standard CertiPUR® St. IV by Merck) and ICP-

MS (7500ce, Agilent). TOC (as NPOC) and IC were determined with a TOC-VCPN (Shimadzu

Corp). Nitrate, chloride and sulfate were determined using ion chromatography (Dionex Dx-120).

The field parameters redox potential, pH-value, temperature, dissolved oxygen and specific electric

conductivity were determined in a flow-through device with sensors by Hamilton® for redox

potential (EasyFerm Plus ORP Arc 120 Pt), pH-value/temperature (EasyFerm Plus PHI Arc 5120),

dissolve oxygen (VisiFerm DO Arc 120) and conductivity (Conducell 4 USF Arc 120). Calibration

standards for pH-sensors are from Xylem Analytics. Calibration of optical sensors for determination

of dissolved oxygen followed ISO 17289 using alkaline ascorbic acid solution [24]. Measurement

time was 45 ±15 min. Electrodes and probes were checked and calibrated before each measurement.

Statistical analyses were performed in R (v. 3.6.0, 2019-04-26, The R Foundation for Statistical

Computing Platform) using the package vegan [25]. From the “Environment for Tree Exploration”

(ETE) Toolkit, the “ncbi_taxonomy module” was used for fetching lineage track information

corresponding to taxIDs from the NCBI Taxonomy database [26]. For visualization of community

composition, Krona was used [27].

4

Results

Water Chemistry

Parameters relevant to microbial activity are shown in Table 1. Correspondence analysis (CA;

performed with function rda from package vegan, Figure 1) shows that some sites are chemically

more distinct than a bundled group of others. The combined heating and cooling systems differ

from the rest of the sites because of Mg, Ca and iron. One heating system at the bottom is controlled

by higher Mn-concentrations, one heating and one cooling system (from the same building, return

flow) are influenced mainly by Ni-concentrations. Two heating systems outrange mainly because of

K and one heating system because of Al and P.

Figure 1: Correspondence Analysis explaining 42% of total inertia. Note that “pH” was

transformed into protons concentration.

As shown in Table 1 it is remarkable that nitrate in the fill waters ranged from 2.1 to 16.8 mg L-1

while in the recirculation water, nitrate is depleted (except for a chemically conditioned water with

2.2 mg L-1 Nitrate). For sulfate, up to 90% of the initial concentration was depleted.

5

Table 1 : Water parameters relevant to microbial activity.

Fe(tot) Fe(II) Cu Zn Al Mn* Ni* Cr* Na K Mg Ca Mo P Cl- SO42- NO3- NPOC IC pH Eh

[mV]

O2

[µg/L]

Cond

[µS/cm]

Temp

°C

1 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA

2 0.05 0 0.00 0.04 0.00 14.90 1.90 2.14 11.50 0.90 4.1 4.61 0.10 0.04 11.77 0.50 0.20 4.80 11.40 8.50 -65.97 18.00 369.98 63.90

3 0.09 0 0.02 0.00 0.00 16.44 2.52 3.09 6.50 0.65 3.38 7.34 NA 0.00 7.98 0.19 0.19 2.50 13.30 8.40 -128.02 12.00 265.00 43.57

4 1.37 1.12 0.04 0.05 0.00 111.60 1.75 0.94 45.50 2.10 3.56 11.67 0.00 0.00 18.07 0.00 0.21 3.50 37.50 8.30 -360.53 23.00 326.61 10.51

5 0.07 0.01 0.04 0.00 0.09 83.68 1.14 1.56 19.70 0.74 3.67 5.11 0.00 0.02 1.53 0.19 0.18 3.60 17.50 8.50 -320.22 1.00 315.40 38.06

6 0.01 0 0.02 0.00 0.00 16.73 3.19 1.55 42.50 2.66 3.80 2.73 0.50 0.08 29.14 1.09 0.20 9.80 34.10 8.80 -76.92 17.00 537.91 39.62

7 0.43 0.38 0.04 0.00 0.26 70.00 1.55 1.18 77.00 2.53 7.00 3.60 22.60 6.50 29.35 19.77 0.53 8.00 24.50 9.60 -448.38 27.00 484.95 11.06

8 19.49 19.34 0.02 0.00 0.00 182.70 8.03 48.51 49.60 172.30 4.75 13.70 10.30 5.60 26.45 0.41 2.23 566.0 35.80 8.40 -510.40 8.00 2399.0 52.96

9 0 0 0.00 0.00 0.00 0.00 NA NA 7.80 0.72 1.44 14.84 NA 0.00 0.84 0.00 0.00 11.50 4.40 9.14 -91.98 17.00 202.02 48.30

10 0.3 0.09 0.00 0.00 0.00 0.00 NA NA 18.00 1.55 4.87 14.84 NA 0.00 12.25 0.44 0.00 14.13 6.30 9.27 -494.35 22.00 282.45 18.81

11 0 0 0.02 0.00 0.00 3.79 2.15 2.65 9.00 1.90 1.38 6.23 0.00 0.05 19.39 0.86 0.70 3.23 5.96 8.71 -19.90 14.00 305.31 62.75

12 0.22 0 0.03 0.00 0.00 17.77 3.32 2.24 10.21 0.24 0.18 2.31 0.00 0.15 3.00 0.24 0.00 3.67 8.48 10.10 -77.98 26.73 187.07 57.79

13 4.99 0.035 0.12 0.03 0.02 73.82 22.99 8.77 12.68 2.40 6.38 62.54 0.00 0.00 17.80 11.60 0.30 7.14 56.33 9.86 84.15 11530.0 430.37 9.49

14 2.185 1.89 0.00 0.00 0.00 175.90 1.14 1.23 8.18 1.68 5.28 22.30 0.00 0.13 17.75 11.60 0.28 6.92 26.46 7.96 -452.47 36.68 228.94 10.58

15 2.17 1.91 0.01 0.00 0.00 168.90 2.96 2.62 9.17 1.85 5.83 24.07 0.00 0.00 23.22 4.00 0.17 7.14 25.57 8.33 -450.79 42.60 211.10 8.42

16 0.23 0 0.04 0.01 0.04 14.64 5.15 1.26 9.47 0.15 0.23 2.58 0.00 0.00 0.79 0.23 0.00 4.92 8.72 9.87 -154.05 27.91 395.25 56.27

17 0.15 0.02 0.04 0.00 0.00 14.91 0.91 0.18 36.90 1.17 2.00 3.93 0.00 0.00 14.61 24.88 0.00 8.55 19.56 10.35 -363.07 42.00 251.97 9.04

18 0.07 0 0.04 0.00 0.00 13.59 0.85 0.29 30.30 1.93 6.70 8.10 0.00 0.05 21.90 2.80 0.97 122.60 30.93 9.29 -238.14 63.00 391.57 26.12

19 195.4 193.5 0.10 0.00 0.00 6600.0 1.36 0.91 6.67 1.10 1.57 2.60 0.00 1.60 NA NA NA 41.86 0.85 6.34 -516.00 6.59 2342.12 63.69

20 1.34 0.02 2.82 0.09 0.01 33.29 6.32 2.04 18.86 4.80 3.15 9.63 0.00 0.00 32.30 0.00 0.00 3.54 8.80 10.20 -342.32 35.67 263.04 14.47

21 1.08 0.96 2.07 0.04 2.30 20.74 3.38 2.13 154.32 11.38 9.00 100.9 158.6 9.84 NA NA NA 1389.0 153.20 7.33 -138.00 30.88 3286.16 37.16

22 0.04 0.03 0.02 0.00 0.04 21.94 0.37 0.25 12.26 2.52 2.73 6.28 0.02 0.00 21.30 0.00 0.00 5.11 15.89 9.30 -429.27 47.91 124.55 6.67

23 0.08 0.04 0.04 0.00 0.04 10.52 1.76 0.40 23.91 5.78 4.17 7.51 0.00 0.00 50.90 0.00 0.00 9.14 23.37 8.62 -386.28 23.72 492.53 41.09

24 131.6 122. 8 0.25 0.14 1.12 1060.0 6.09 0.41 183.08 4.04 3.36 27.80 76.62 4.34 NA NA NA 2952.0 37.06 6.44 -273.99 23.20 2055.75 32.84

25 112.7 105.8 0.00 0.10 0.86 950.00 0.83 0.22 171.59 4.12 3.31 27.21 61.61 3.12 NA NA NA 2968.0 32.40 6.90 -332.20 16.00 2168.90 37.80

26 1.38 0.89 0.01 0.00 2.79 68.98 0.33 0.08 158.73 6.19 4.50 20.74 192.6 10.50 NA NA NA 130.30 18.17 8.85 -422.34 34.82 1068.19 15.65

27 172.2 159.4 0.27 2.59 0.15 840.00 1047.0 14.21 112.65 3.67 1.92 13.70 8.90 0.40 NA NA NA 1251.0 10.55 5.47 -55.96 56.72 1440.73 29.49

28 1.54 0.01 2.22 1.52 0.24 630.00 914.3 0.43 70.99 16.62 15.03 40.38 16.25 1.21 NA NA NA 110.40 64.70 7.36 80.17 5462.61 1120.32 34.17

29 1.17 0.03 0.68 0.22 0.23 59.22 243.2 3.35 69.80 11.97 9.89 48.09 14.56 0.79 NA NA NA 9.37 66.71 8.19 64.16 6559.06 1175.75 34.93

30 0.18 0.13 0.00 0.00 0.02 20.14 1.46 0.40 8.97 1.82 5.00 8.36 0.00 0.00 17.90 0.00 0.00 4.96 11.60 9.09 -312.27 30.54 166.00 19.15

31 0.13 0 0.00 0.00 0.01 10.04 1.09 0.46 9.24 1.91 5.40 7.71 0.00 0.01 18.50 0.00 0.00 4.17 11.66 8.96 -154.47 24.96 226.27 32.69

32 0.1 0.07 0.00 0.00 1.89 6.06 1.46 1.12 152.92 3.74 0.37 2.19 0.00 0.00 97.20 0.40 0.50 115.90 55.16 8.59 -362.26 23.75 2381.50 50.39

33 1.01 0.98 0.00 0.00 0.01 38.58 3.54 2.32 61.90 5.45 6.56 13.47 0.00 0.03 98.30 4.90 0.40 6.45 34.67 8.91 -416.42 50.15 463.69 7.96

34 0.11 0 0.05 0.00 0.00 6.97 3.16 0.00 53.83 4.24 4.83 2.37 0.00 0.00 116.40 0.60 0.50 14.96 15.24 9.56 -250.15 27.40 477.35 22.81

41 0.27 0.02 0.04 0.00 0.00 4.15 2.05 1.40 25.49 3.82 3.79 7.95 NA 0.00 45.14 9.51 0.00 4.30 19.30 9.19 -112.05 34.62 297.72 18.28

Values in mg L-1 ; *µg L-1

6

Bacterial community composition

In the 16S rRNA analyses, a total of 529 different genera was identified. The archived sample ID

(VAMPS) is: EQM_Bacteria_Final_DataSet. Some genera were present in almost all the systems

(Figure 2).

The microbial community analysis suggests a high frequency of bacterial taxa associated with the

most abundant phylum Proteobacteria, followed by Actinobacteria in heating systems and

Firmicutes in cooling systems.

Table 1: Percentage of counts belonging to the most abundant Phyla.

Proteo-

bacteria

Actino-

bacteria Firmicutes

Bacteroi-

detes

Spiro-

chaetes Chloroflexi

Heating systems 57,2 15,1 14,2 5,8 2,8 2,7

Cooling systems 55,5 5,1 26,3 7,5 2,3 2,3

Figure 3 depicts the genera and higher operational taxonomic units (OTUs) of the respective

lineage. Compared to the following chart, Figure 4, fewer genera attain a proportion over 5% (only

Propionibacterium and Bradyrhizobium compared to the list of: Pseudomonas, Smithella,

Desulfotomaculum, Desulfosporosinus and Sediminibacterium in cooling systems). The proportion

Figure 2: Genera present in most systems and metabolic capacities.

7

of Deltaproteobacteria (including Smithella and SRB) is 7% in heating systems compared to 13% in

cooling systems.

Figure 4: Bacterial community composition of closed water loop cooling systems.

Figure 3: Bacterial community composition of closed water loop heating systems. Chart was

created using Krona.

8

Diversity Patterns

Genetic fingerprinting analyses reveals distinct microbial communities in the different systems.

Similar patterns were determined for systems with a geographical proximity, whereas systems with

similar temperatures showed significant differences.

Shannon index

The mean of Shannon index is 2.83 ± 0.39. Shannon values were observed to be highest in heating

systems (the ten highest Shannon values belong to 7 heating and 3 cooling systems.)

Regressing diversity (Shannon index) on temperature yields a significant linear model. However,

the amount of variance explained by the model is relatively small (13%).

Call: lm(formula = divers ~ siteschem$T) Residuals: Min 1Q Median 3Q Max -1.04988 -0.22606 0.07762 0.26461 0.56481 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 2.609865 0.123829 21.076

9

Concerning the genera richness, this study found an increase of richness from 103 to 241 genera in

the system which was analyzed twice. This is related to feeding-in of make-up water between the

two samplings.

Discussion

The microbiology of heating and cooling water loop systems was until now not investigated in other

studies. It is established that corrosion of carbon-steel is a complex process involving multiple

mechanisms influenced to varying degrees by the activity of bacteria [23]. This field study

comprises a case number of n = 35, which is relatively low compared to the estimated number of

influences. However, some indications for future research are given.

Water Chemistry

Element, anion and TOC concentrations as well as field parameters (pH, oxidation-reduction

potential, dissolved oxygen, conductivity and temperature) were measured (in total 23 variables).

These parameters (from Table 1) can only be punctually discussed with relevance to microbial

activity. Every system is unique and provides a high diversity of microbial niches. It is worth noting

that the systems have temperature gradients by design (lower return flow temperature). Obviously,

nitrate and sulfate were removed from the bulk water solution and possibly incorporated into the

biofilm (not investigated in this study) where these anions could have served as an energy source

for nitrate or sulfate reducing bacteria detected in molecular genetic analysis. Concerning NPOC,

other studies calculate that only a fraction of up to 9% is assimilable for growth [28]. However,

bacteria might adapt to the given Corg supply. Bacterial composition

The finding of higher proportions of Desulfo- genera in cooling compared to heating systems is

in accordance with the report by Li et al. who studied responses of microbial community

composition to temperature gradient [23]. They set incubation temperatures at 37, 55 and 65°C

to monitoring mesophilic, thermophilic and hyperthermophilic microorganisms associated with

anaerobic carbon steel corrosion. They detected some representatives of SRB within

Deltaproteobacteria such as Desulfovibrio, Desulfotignum, and Desulfobulbus genera only at 37°C. Thus, these genera appear to be more favored at mesophilic conditions, since at 55 or

65°C they were non-detectable [23]. In this study, the only non-cooling systems with higher

proportions (>10% of counts) of Desulfo- genera had operating temperatures of: 26, 33, 38 and

41°C at the time of sampling. This gives indication that corrosion problems related to SRB are

more likely in cooling than in heating systems.

Diversity

The finding that heating systems show a higher Shannon index needs to be further investigated with regard to MIC relevance. If one genus thrives specifically well at the sampling time, it

does not follow that MIC is favored. However, a smaller Shannon index might stand for a rather

deterministic (nichebased) process than a mere stochastic one. On the other hand, drinking water used for filling of a heating or cooling system may contain up to millions of microorganisms per

liter [20] so there is always a stochastic component, which bacteria get into the system. As the

case of the system which was investigated twice shows: several new genera were introduced by

a feeding- in of make-up water.

10

Conclusion

Based on 16S rRNA sequence analysis, this study describes for the first time similarity patterns

in the microbiomes of 34 operating heating and cooling water loop systems. Differences in the

bacterial communities and in diversity indices are partly related to the type of usage (heating,

cooling, combination) whereas temperature is an ambiguous estimator. We found higher

proportions of members of sulfate reducing bacteria (SRB) in cooling compared to heating

systems and as a tendency higher Shannon index values in heating systems. SRB are known to

assist microbially induced corrosion. A depletion of sulfate and nitrate from the bulk fluid was

confirmed. However, for addressing the question, how complex consortia interact in MIC processes and how much impact the origin of fill water exerts on the future community

composition, more statistical analyses of the data base are necessary.

Acknowledgments

This research has been funded by the German Federal Ministry of Economic Affairs and Energy

(BMWI) in the project EQM-Hydraulik (project no. 03ET1270B) which is gratefully

acknowledged. Additionally, we thank our coworkers in the field Dr. Tanja Eggerichs and Tobias

Otte for sampling and measurements as well as for their valuable input and we thank our partners:

SIZ energie+, Blue Biolabs, synavision, Union Investment, Wilo, IMI Hydronic Engineering.

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