Impact of different combinations of water treatment processes on the


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Impact of different combinations of water treatment processes on the concentration of disinfection byproducts and their precursors in swimming pool water Bertram Skibinski, Stephan Uhlig, Pascal Müller, Irene Slavik, and Wolfgang Uhl Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.9b00491 • Publication Date (Web): 10 Jun 2019 Downloaded from http://pubs.acs.org on June 10, 2019

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IMPACT OF DIFFERENT COMBINATIONS OF WATER TREATMENT PROCESSES

2

ON THE CONCENTRATION OF DISINFECTION BY-PRODUCTS AND THEIR

3

PRECURSORS IN SWIMMING POOL WATER

4 5

Bertram Skibinski,1,2* Stephan Uhlig,2 Pascal Müller,2 Irene Slavik,2,3 Wolfgang

6

Uhl,2,4,5,*

7

1

8

Germany.

9

2

Technische Universität Dresden, Chair of Water Supply Engineering, 01062 Dresden, Germany.

10

3

Wahnbachtalsperrenverband, 53721 Siegburg, Germany

11

4

Norwegian Institute for Water Research (NIVA), 0349 Oslo, Norway.

12

5

Norwegian University of Science and Technology (NTNU), Institute of Civil and Environmental

13

Technical University of Munich, Chair of Urban Water Systems Engineering, 85748 Garching,

Engineering, 7491 Trondheim, Norway.

14 15 16

* Corresponding authors. E-mail addresses:

17

[email protected] (Bertram Skibinski)

18

[email protected] (Wolfgang Uhl)

19 20 21 22 23 24 25 26 27 28 29 30 31

Keywords: swimming pool water treatment, disinfection by-products (DBPs),

32

activated carbon, ultrafiltration, UV radiation

33 34

Type of article: Research Paper

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ABSTRACT

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To mitigate microbial activity in swimming pools and to assure hygienic safety for

37

bathers, pool systems have a re-circulating water system ensuring continuous water

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treatment and disinfection by chlorination. A major drawback associated with the use

39

of chlorine as disinfectant is its potential to react with precursor substances present in

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pool water to form harmful disinfection by-products (DBPs). In this study, different

41

combinations of conventional and advanced treatment processes were applied to

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lower the concentration of DBPs and their precursors in pool water by using a pilot-

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scale swimming pool model operated under reproducible and fully-controlled

44

conditions. The quality of the pool water was determined after stationary

45

concentrations of dissolved organic carbon (DOC) were reached. The relative removal

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of DOC (Δc cin–1) across the considered treatment trains ranged between 0.1 ± 2.9%

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and 7.70 ± 4.5%, where conventional water treatment (coagulation and sand filtration

48

combined with granular activated carbon (GAC) filtration) was revealed to be the most

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effective. Microbial processes in the deeper, chlorine-free regions of the GAC filter

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have been found to play an important role in the degradation of organic substances.

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Almost all treatment combinations were capable of removing trihalomethanes to some

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degree, and trichloramine and dichloroacetonitrile almost completely. However, the

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results demonstrated that effective removal of DBPs across the treatment train does

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not necessarily result in low DBP concentrations in the basin of a pool. This raises the

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importance of the DBP formation potential of the organic precursors, which has been

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shown to depend strongly on the treatment concept applied. Irrespective of the filtration

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technique employed, treatment combinations employing UV irradiation as a second

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treatment step revealed higher concentrations of volatile DBPs in the pool compared

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to those employing GAC filtration as a second treatment step. In the particular case of

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trichloramine, results confirm that its removal across the treatment train is not a feasible

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mitigation strategy since it cannot compensate for the fast formation in the basin.

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1

Introduction

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By their nature, people release microorganisms as well as organic and inorganic matter

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into swimming pool water while bathing (1, 2). In order to avoid microbiological

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contamination and to prevent spreading of pathogenic diseases among bathers, most

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pool waters are disinfected using chlorine-based disinfectants (3). However, the

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pollutants released by the bathers, as well as natural organic matter (NOM) present in

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the filling water of swimming pools, react with chlorine, leading to the formation of a

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variety of harmful disinfection by-products (DBPs) that are characterised as eye- and

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skin-irritating, carcinogenic or as triggers of respiratory problems (4, 5). Many previous

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studies have dealt with the occurrence and formation of DBPs in swimming pool water

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(6, 7). More than 100 DBPs have been identified in swimming pool water (8), with

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trihalomethanes (THMs), halogenated acetic acids (HAAs), chloramines (CAs) and

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haloacetonitriles being the most studied examples (9, 10). To ensure efficient

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disinfection while having a minimal concentration of DBPs in the pool, swimming pool

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water is continuously circulated over a treatment train that consists of different

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consecutive treatment steps (3).

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Besides conventional water treatment processes, such as coagulation, sand filtration

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or granular activated carbon (GAC) filtration, advanced processes, such as membrane

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filtration (micro- (MF) or ultrafiltration (UF)) or UV irradiation are increasingly used for

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pool water treatment (9, 11, 12). Yet, only a few studies have determined the impact

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of membrane filtration and UV irradiation on the water quality in pools (9, 13-18). The

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few results reported are barely comparable with each other. Major concerns for

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comparability arise due to the different operating conditions of the swimming pool

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systems tested. This includes, for instance, differences in bather loading, filling water

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quality, concentration of free chlorine, pH, chlorine/precursor ratio and water

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temperature (1, 2, 7, 19). 4 ACS Paragon Plus Environment

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Only Judd et al. and Goeres et al. established a swimming pool system that allowed

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pool operations on a comparable basis (20, 21). However, Goeres et al. focused their

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work on biofilm formation and did not investigate further the removal efficiencies of

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different treatment processes. Judd et al. compared different swimming pool water

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treatment processes under controlled conditions using a pilot-scale swimming pool

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model, but the number of different treatment combinations that they compared was

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insufficient to get a full picture of the various treatment combinations typically used in

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swimming pools. Further, assessment of the water quality was limited to a small

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selection of DBPs (22), not taking into account other relevant nitrogenous DBPs such

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as trichloramine, dichloroacetonitrile and trichloronitromethane.

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Given the current state of knowledge, the main objective of this study was to assess

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and compare the impact of a high number of treatment combinations with regard to the

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concentration and composition of organic substances and DBPs present in swimming

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pool water. Treatment combinations included advanced processes such as

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coagulation, powdered activated carbon adsorption, ultrafiltration (UF) and UV

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irradiation as well as conventional processes such as coagulation, sand filtration and

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GAC filtration. In order to guarantee comparability of the results, the treatment

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combinations were assessed and compared under fully controlled and reproducible

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conditions using a pilot-scale swimming pool system.

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2

Materials and Methods

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2.1

Pilot-scale swimming pool model

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2.1.1 Components

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A pilot-scale swimming pool model (Figure 1) was established to assess the impact of

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different combinations of treatment processes on organic matter and DBPs present in

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pool water. Table 1 summarises the operating conditions of the pool model, which

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consisted of a basin with spillway and air space, a stirred (~200 rpm), double-walled

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and temperature-controlled splash water tank and a modular water treatment train. A

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pump P1 (CRNE1-7, Grundfos) took water from the splash water tank and fed it at a

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turnover water flow rate of 0.57 m³ h–1 over the treatment train back into the basin

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through 48 evenly distributed nozzles at the bottom of the pool.

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To assure a residence time in the basin of the swimming pool model that is equal to

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that of real-scale pools, the turnover water flow rate was calculated according to the

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German pool water standard DIN 19643 (assuming the pool being only used for

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swimming (e.g. no paddling of pool)) (3). The modular treatment train of the swimming

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pool model consisted of conventional and advanced treatment processes, which are

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described in detail in Section 2.1.3. Two ventilators (V1, V2) fed air into the air space

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above the basin over a perforated flow channel near the spillway. The ratio between

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recirculated air (V1) and fresh air (V2) was controlled by orifice plates to maintain a

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constant absolute humidity of 11–14 g kgair–1 1.5 m above the water level. The air was

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heated by two inline heating coils. All dimensions and flow rates of the pool model were

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chosen according to German swimming pool standards (3).

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Figure 1: Flow scheme of the swimming pool model. Red arrows indicate discontinuous sampling ports (SPs) and continuous sampling port (CSP).

131 132 133

Table 1:

Dimensions and operating conditions of the swimming pool model.

Parameter

Value

Volume of the basin (L x W x D: 1.6 x 0.8 x 2.1 m)

2.69 m³

Volume of the splash water tank

0.17 m³

Recirculation flow rate

0.57 m³ h–1

Fresh water input

8.5 L h–1

Hydraulic retention time in the basin in single pass operationa

4.7 h

Hydraulic retention time in the systemb

14.2 d

Free chlorine conc. (at 0.12 m depth)c

0.54 ± 0.13 mg L–1 (as Cl2)

pH (at 0.12 m depth)c

7.08 ± 0.14

Water temperature (at 0.12 m depth)c

28.0 ± 0.4 °C

Air temperature (1.5 m above the water level)c

27.2 ± 2.2 °C

Humidity in the air hut (1.5 m above the water level)

11 - 14 g kgair–1

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a respecting

a circulation flow of 0.57 m³ h-1 and a volume of the basin of 2.69 m³

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b respecting

a fresh water flow of 8.5 L h-1 and a total water volume in the pool system of ~2.9 m³

136

c

Errors represent the standard deviation ( n = 712). Data shown in Section A of the supplementary material (SM).

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2.1.2

Dosing of fresh water and bather load

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Fresh water was fed at 8.5 L h–1 into the splash water tank for the purpose of dilution.

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This amount was calculated using the maximum permitted bather loading of the pool

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model (~0.28 bather h–1) and a fresh water flow rate of 30 L per bather (3). A constant

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water volume was maintained by discharging the same amount of pool water by a

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peristaltic pump P2 (503S, Watson Marlow). To ensure fully controlled conditions, the

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local tap water used as fresh water was pre-treated by GAC filtration leading to a DOC

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of <0.1 mg L–1. After GAC filtration, a synthetic humic acid (HA) solution was added to

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the fresh water stream to reach a DOC concentration of 1 mg L–1 before dosing it into

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the splash water tank. The HA solution was prepared by dilution of a humic acid sodium

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salt (Sigma Aldrich) in deionised water and subsequent removal of the colloidal and

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particulate HA material by ultrafiltration (molecular weight cut off (MWCO) = 100 kDa).

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The input of soluble organic matter by the bather was simulated by dosing a body fluid

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analogue (BFA) (20) into the pool at four evenly distributed dosing points at 1.2 m water

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depth (~0.025 L h–1). The BFA stock solution was prepared by dilution of the solid

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chemicals (all purchased from VWR, Germany) in deionised water. Table 2

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summarises the composition and dosing rate of the BFA substances. Dosing of all

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chemicals was performed using peristaltic pumps (DULCO®flex DF4a, Prominent).

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Dosing of BFA and HA was performed for 24 h over 7 days per week to simulate the

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worst-case scenario for a swimming pool system.

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Table 2:

Composition of the BFA stock solution and dosing rates applied in the swimming pool model.

160

BFA substance

Ammonium chloride Urea Creatinine Uric acid Citric acid L-Histidine Hippuric acid 161

a

162 163 164 165

b The

Molecular weight

Concentration in the BFA mixturea

Dosing rate in the pool modelb

in Da

in gsubst L–1

in mgsubst h–1 (mgTN h–1)

53.49 60.06 113.11 168.11 192.12 155.15 179.18

1.21 8.95 1.11 0.30 0.39 0.74 1.06

30 (8.0) 224 (105.8) 27 (10.3) 7.4 (2.5) 9.7 (0) 18 (5.0) 26 (2.0)

As given by (20) dosing rate was calculated based on the amount of total nitrogen (TN) known to be introduced by bathers (~470 mgTN per bather). This amount includes initial bathing load (80 mgTN per bather), continual bathing load (120 mgTN per bather) and accidental bathing load (270 mgTN per bather) (23). It was assumed that ~0.28 bather h–1 entered the pool model.

166 167

2.1.3

Pool water treatment combinations

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Table 3 presents the treatment combinations tested in the swimming pool model

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(Figure 1). It is known from the literature, that operation conditions significantly

170

determine the overall removal efficiency of treatment steps towards NOM and DBPs

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(11). While many regulations with regard to the required pool water quality are known

172

(24, 25), only a few national standards give precise advice with regard to the design

173

and operation conditions of pool water treatment facilities (e.g. 26, 27, 3). As it was out

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of the scope of this study to determine optimal operation conditions, we followed

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recommendations of the German standard DIN 19643, as it provides a sound basis

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with regard to operation conditions for many of the single treatment steps used in this

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study (see Table 3). Where no requirements were given by DIN 19643, manufacturer

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recommendations were chosen. Each treatment combination included dosing of

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coagulants (polyaluminium chloride or pre-hydrolysed aluminium chlorohydrate),

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adjustment to pH 7 by dosing of concentrated H2SO4 and NaOCl. Dosing of all 9 ACS Paragon Plus Environment

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chemicals was performed using peristaltic pumps (DULCO®flex DF4a, Prominent).

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Table B.1 in the supplementary material (SM) summarises detailed information about

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the design and operating conditions of each treatment step. It has to be noted here

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that all treatment steps were operated in full-stream operation although many national

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standards also allow for side-stream operation. However, given the heavy loading of

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the swimming pool model with organic substances (see Section 2.1.2) we decided for

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the best-case scenario with regard to NOM and DBP removal for each treatment step,

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which is full-stream treatment.

189 190

Table 3:

Treatment combinations used for the pilot-scale experiments. Exp. No.

3 4 5 6 7 191

a Low-pressure

192 193

b

Sand filtration

GAC filtration

Sand filtration

UV irradiation (LP)a

Sand filtration

UV irradiation (MP)a

Ultrafiltration

UV irradiation (LP)a

Ultrafiltration

UV irradiation (MP)a

Adsorption by PACb

Ultrafiltration

Adsorption by PACb

Sand filtration

pH adjustment / chlorination

2

Coagulation/Flocculation

1

Treatment combination

UV lamp (LP); medium-pressure UV lamp (MP)

Powdered activated carbon (PAC) formed a pre-coat layer on the UF membrane and was rejected in the first ~20 cm of bed depth of the sand filter

194

195

2.1.4

Experimental procedure

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An individual experiment was performed for each treatment combination according to

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the following procedure: (i) filling the basin and the treatment train with DOC-free tap

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water (<0.1 mgDOC L–1), (ii) starting water circulation (experimental run time t = 0) and

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(iii) starting continued dosing of BFA, HA, fresh water, chlorine, pH chemicals and

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coagulant for 24 h per day until DOC concentrations reached stationary conditions.

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The continuous loading with BFA and HA for 24 h was chosen to simulate the worst10 ACS Paragon Plus Environment

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case scenario for a swimming pool system. Standard operating parameters, such as

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the concentration of free chlorine (cCl2), pH, temperature of pool water in the basin

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(Tbasin), air temperature (Tair) and humidity in the hut were maintained at constant levels

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during all experiments by feedback control.

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2.1.5

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A water stream of ~20 L h–1 was taken from the continuous sampling port in the basin

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and was led by gravity over a standard electrode system for feedback control of the

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concentration of free chlorine, combined chlorine, pH and temperature (see Section

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2.2) back into the splash water tank.

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Additionally, sampling ports (SPs) for discontinuous collection of water samples from

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the pilot-scale swimming pool model were placed before and after each treatment step,

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over the bed depth of the GAC filter and sand filter and in the basin (0.12 m below the

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water level). Discontinuous sampling at a flow rate of 20 L h–1 was performed

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automatically by consecutive opening of magnetic valves and feeding the sample for

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20 min by a gear pump (Type 1, Gather Industries) to a standard electrode system, an

217

online analyser for dissolved organic carbon (DOC) and a continuously operating

218

membrane inline mass spectrometer (MIMS) for automatic analysis of volatile DBPs.

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The water streams from the continuous and discontinuous sampling ports were led

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back into the splash water tank. Manual samples were taken sporadically to analyse

221

the composition of organic matter by size exclusion chromatography and the bacterial

222

cell count with flow cytometry.

Sampling procedures

223

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2.1.6

Simulation of the reference state

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For the purpose of comparison, the concentration of organic matter in the swimming

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pool model was simulated in the absence of water treatment (i.e. neglecting the 11 ACS Paragon Plus Environment

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removal of organic substances across the treatment train and transformation of organic

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substances by the reaction with chlorine). In the following, this state was denoted as

229

reference state. For simulation of the reference state, a simplified numerical model of

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the pilot-scale system was established using the simulation environment AQUASIM

231

(28). The numerical model is described in detail in Section C (SM).

232

For further discussions, it was necessary to compare concentrations of organic

233

substances from the AQUASIM model with the fractions of organic matter in the pilot-

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scale swimming pool model as measured by the LC-OCD method (see Section 2.2).

235

Therefore, a procedure was developed to allocate modelled concentrations of BFA

236

components to the organic matter fractions defined by the LC-OCD method (see

237

Section D (SM)).

238

239

2.2

Analytical methods

240

2.2.1

241

Standard process parameters (free chlorine, combined chlorine, pH and temperature)

242

were analysed by an electrode system (Meinsberg). The electrode system was

243

calibrated at least twice per week. Free and combined chlorine electrodes were

244

calibrated using the DPD colorimetric method (29). Organic carbon was measured

245

semi-continuously as non-purgeable organic carbon (NPOC) by the catalytic

246

combustion method using a TOC-VCSH analyser (Shimadzu). Correlation of NPOC

247

and DOC over all experiments performed in this study revealed that all organic carbon

248

in the swimming pool model was present as dissolved matter (Section E (SM)). Thus,

249

NPOC was denoted as DOC in the following.

Online measurement of relevant process parameters

250

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2.2.2

Composition of dissolved organic matter

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The composition of DOC was analysed using liquid size exclusion chromatography

253

with DOC, dissolved organic nitrogen (DON) and UV detection at 254 nm (UVD)

254

(LC-OCD-OND-UVD) as described by Huber et al. (30). After passing the size

255

exclusion column (TSK HW 50S, Toso, Japan), the complete eluent-flow (1 mL min-1,

256

phosphate buffer of pH 6.85) is led through the UV-detector while the eluent stream is

257

split before it is led to the OC- and OND-detector. The method allows subdividing DOC

258

and DON into fractions according to their apparent molecular weight (i.e. biopolymers,

259

humic substances (HSs), building blocks (BBs), low-molecular-weight acids (LMW-

260

acids) and low-molecular-weight neutrals (LMW-neutrals)). Following previous

261

recommendations, the fraction of LMW-acids was calculated without the correction that

262

accounts for the fraction of low-molecular-weight HAs (31).

263

Using the spectral absorption coefficient of ultraviolet light of the sample at a

264

wavelength of 254 nm (SAC254nm), the specific UV absorbance of the sample (SUVA)

265

was calculated by the ratio of SAC254nm to DOC. To allocate single BFA substances to

266

one of the molecular weight fractions, standards of each component of the BFA

267

substances (2.5 mg L–1 (as DOC)) and HAs (1 mg L–1 (as DOC) of Sigma Aldrich humic

268

acid) were prepared in ultrapure water and analysed with LC-OCD-OND-UVD.

269

Corresponding chromatograms are presented in Section F (SM).

270

Urea and ammonium were quantified using the LC-OCD method as proposed by Huber

271

et al. (32). A detailed description of the method is given in Section G (SM).

272

The sum of organic and inorganic nitrogen (TN) in water samples was analysed by

273

integrating the bypass signal of the nitrogen detector of the LC-OCD (30).

274 275

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2.2.3

Analysis of volatile DBPs

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Eleven volatile DBPs were measured by online MIMS as described previously (10). A

278

MIMS 2000 from Microlab (Aarhus, Denmark) with a Prisma QME 220 mass

279

spectrometer (Pfeiffer Vacuum, Germany) with electron ionization (at 60 eV) was used

280

for analysis. The sample flow rate was ~10 mL min–1 and the temperature of the

281

membrane inlet was fixed at 40 °C. For analysis, non-reinforced silicon membranes

282

with a thickness of either 0.125 mm or 0.25 mm were used (PERTHESE® LP 500-3 or

283

NP 500-1, Perouse Plastie). The selected ion monitoring mode of the mass

284

spectrometer was used for quantification of the DBPs. Signal intensities of the following

285

m/z values and respective fragments were used to quantify the volatile DBPs in purified

286

water samples: trichloramine (88, N37Cl2•+), trichloromethane (83, CH35Cl2•+),

287

tribromomethane

288

(208, CH79Br237Cl•+, CH79Br81Br35Cl•+), dichlorobromomethane (29, CH81Br35Cl•+,

289

CH79Br37Cl•+),

290

dichloroacetonitrile (76, CH37ClN•+). Further information on calibration and analysis

291

with MIMS is given in Section H (SM).

CH79Br81Br•+),

(173,

trichloronitromethane

(chloropicrin)

dibromochloromethane

(117,

C35Cl3•+)

and

292

293

2.2.4

Bacterial cell count

294

Total cell concentrations in water samples from the swimming pool model were

295

analysed by quantitative flow cytometry (Accuri C6, BD Bioscience) together with

296

fluorescence staining of microbial cells with the nucleic acid stain SYBRsGreen I

297

(1:100

298

Hammes et al. (33). Staining was performed prior to FCM analysis in the dark for at

299

least 13 min at 38 °C. SYBR Green I and propidium iodide (6 µM) were used in

300

combination according to Hammes et al. to differentiate between living and dead cells

301

(34).

dilution

in

DMSO)

according

to

the

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described

by

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302 303

2.2.5

Concentration of bromide ions and bromate

304

The concentration of bromide ions was determined in the BFA stock solution, the HAs,

305

and the filling water according to DIN 10304-1 (2009) using liquid ion chromatography

306

(35). No bromide was expected to be found in the NaOCl stock solution as it will be

307

oxidized forming bromate.

308

The concentration of bromate was determined in the BFA stock solution, the HAs, and

309

the filling water according to DIN 15061 (2001) (36). The concentration of bromate in

310

the NaOCl stock solution was analysed by iodometric titration of bromate after

311

reduction of the hypochlorite as described by Höll (1986) (37).

312

313

2.2.6

Water samples from a real swimming pool under heavy bather load

314

To compare the results gained with the pilot-scale swimming pool model with that of a

315

real pool, a pool water sample was taken from a heavily loaded full-scale swimming

316

pool facility, 30 cm below the water level near the middle of the short side of the basin.

317

Dimensions and operating conditions of the real-scale swimming pool before sampling

318

are summarised in Section K (SM).

319

320

2.2.7

Statistical analysis

321

Unless otherwise mentioned, concentrations are given as the mean value of three

322

repeated measurements and indicated errors represent the 95% confidence interval.

323

To determine whether any of the measured fractions of organic matter found in the

324

pool water samples could function as an indicator of DBP formation, correlation

325

coefficients between the concentrations of the volatile DBPs and those of the fractions

326

of organic matter were calculated according to Spearman’s rank method. Student’s t-

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tests were used for hypothesis testing on the basis of a difference between

328

concentrations of organic substances or DBPs between the different treatment

329

concepts. The null hypothesis that stated that no differences were observed was

330

rejected for a probability of p < 0.05.

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3

Results and Discussion

332

3.1

333

The standard process parameters (concentration of free chlorine (cCl2), pH, Tbasin and

334

Tair) of the swimming pool model were successfully maintained at a constant level over

335

the run time of the experiments (see Figure A.1 (SM)). The corresponding mean values

336

determined for Exp. 1 to 7 were as follows: cCl2 = 0.54 ± 0.13 mg L–1 (as Cl2),

337

pH = 7.08 ± 0.14, Tbasin = 28.0 ± 0.4 °C and Tair = 27.2 ± 2.2 °C (errors represent the

338

standard derivation, n = 172). As these parameters are known to influence the

339

formation and decay rates of DBPs (38, 39), their continuous control was of crucial

340

importance for fully reproducible process operation to allow comparison among the

341

experiments. The comparably high standard derivation for free chlorine is primarily

342

caused by the temporarily high free chlorine value (~0.6 – 1.0 mg L-1 (as Cl2)) of one

343

experiment (Exp.4) in the narrow time-interval from 2.5 – 4.5 days of run-time caused

344

by imperfect process control. A student t-test (α = 0.05) revealed no significant

345

differences between the mean value of 0.54 mg L-1 (as Cl2) and mean free chlorine

346

concentrations of the single experiments. Further, student t-tests (α = 0.05) revealed

347

that there is no significant difference between free chlorine concentrations in the pool

348

at the sampling time (see Figure 6(A)) among the different experiments.

349

The time-dependent concentration of DOC and TN in the swimming pool model for

350

different treatment combinations and the simulated reference state are shown in Figure

351

2. The DOC concentration increased steadily with time for all treatment combinations

352

until stationary concentrations were reached after ~24.9–27 days, which equals

353

approximately twice the hydraulic retention time in the system (~14.2 d) calculated

354

respecting the fresh-water input of 30 L per bather (see Table 1). When stationary

Process operation until stationary conditions were reached

17 ACS Paragon Plus Environment

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355

conditions were reached, DOC concentrations in the basin did not significantly differ

356

from each other over a period of at least 2.5 days (α = 0.05).

357

The only exception was Exp. 7 (PAC-Sand), which was started immediately after the

358

previous experiment and took ~11 d until stationary conditions were reached. In the

359

following, the pool water quality was only accessed when stationary conditions were

360

reached. Concentrations of TN, that have only been determined over the course of

361

Exps. 2 to 4, reached also stationary conditions until sampling was started. Assuming

362

the concentration of TN in the other experiments reaching stationary conditions as well,

363

supports the choice of the sampling interval used.

364

365 366

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Figure 2:

Time-dependent DOC concentration in the basin of Exp. 1 to 7 and

368

the simulated reference state (A) and time-dependent TN

369

concentration in the basin of Exp. 2 to 4 (B). Symbols with broken

370

lines at the end of Exps.1 and Exp. 7 represent data obtained after

371

sampling has been conducted. Broken vertical lines represent the

372

period when samples were taken to determine the water quality

373

under stationary conditions. The arrow indicates the time when

374

samples were taken in Exp. 7. The DOC concentrations were

375

smoothed by plotting average values (averaged over 24 h). The

376

concentrations of single BFA substances used to calculate the DOC

377

of the reference state are presented in Section D.1 (SM). TN

378

concentrations were derived from single LC-OCD measurements.

379

a

380

after the previous experiment without refilling the system with DOC

381

free water. This was done due to time constrains. Exp. 7 took ~11 d

382

until stationary conditions were reached.

Different from all other experiments, Exp. 7 was started immediately

383

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384 385

3.2

Comparing the organic matter composition of the swimming pool model with that of real swimming pool water

386

The DOC concentrations in the swimming pool model (Exps. 1 to 7) ranged between

387

2.99 ± 0.09 mg L–1 and 5.94 ± 0.11 mg L–1 (see Section 3.3.2). Only slightly lower DOC

388

concentrations have been reported previously for real pool water samples

389

(1.2 to ~4.2 mg L–1 (9, 11, 40, 41)). The concentration of DOC in the real pool water

390

sample taken from the highly loaded swimming pool (see Section 2.2.6) lay within this

391

range, at 2.01 mg L–1. The discrepancy between the pool model and the real pool can

392

be explained by the difference in pool attendance, which is typically ~12 h per day in

393

real pools (7) and was simulated at 24 h per day in the swimming pool model.

394

Figure 3 presents normalised LC-OCD chromatograms of water samples taken from

395

Exps. 1 to 7 and that of a heavily loaded real swimming pool. In general, the DOC

396

composition found under stationary conditions was comparable to that found in the

397

highly loaded real pool water sample. All samples had good comparability for humic

398

substances, a significant proportion of BBs and LMW-acids, and a dominating

399

proportion of LMW-neutrals. Some minor differences were found at detection times of

400

around 50 min and 60–65 min (LMW-neutrals). These differences could not be

401

explained in detail but were related to the limited diversity of substances of the BFA,

402

which did not fully represent the complexity of substances in real pool water

403

samples (42).

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404 405

Figure 3:

Comparison of LC-OCD chromatograms of water samples from

406

Exps. 1 to 7 and real swimming pool water normalised to a DOC of

407

1 mg L–1 (HS = humic substances, BBs = building blocks, LMWa =

408

low-molecular-weight acids, LMWn = low-molecular-weight neutrals).

409

Inset shows the chromatogram of low molecular weight substances

410

in more detail.

411 412

3.3

413

Impact of different combinations of pool water treatment processes on the concentration of dissolved organic matter

414 415

3.3.1

Dissolved organic matter under stationary conditions

416

The stationary concentration of DOC, the composition of DOC and the SUVA in the

417

basin of the swimming pool model for all considered treatment combinations are

418

presented in Table 4. The lowest DOC concentration in the basin among Exps. 1 to 7

419

was 2.99 ± 0.09 mg L–1 (Exp 5: UF-UV(MP)) while the highest was 5.94 ± 0.11 mg L–1

420

[Exp. 2: Sand-UV(LP)]. The DOC concentration of the reference state was significantly

421

higher at ~10 mg L–1. The following comparisons between the different treatment

422

concepts employed in this study are noteworthy:

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423

(i)

The amount of DOC found in the basin was significantly higher for

424

combinations with sand filtration compared to those with ultrafiltration (UF)

425

(comparing Exp. 2 with Exp. 4, Exp. 3 with Exp. 5 and Exp. 6 with Exp. 7).

426

(ii)

The amount of DOC found in the basin was significantly higher for

427

combinations with UV(LP) than with UV(MP) (comparing Exp. 2 with Exp. 3

428

and Exp. 4 with Exp. 5).

429

(iii)

Using powdered activated carbon (PAC) as a coating layer on sand filters

430

instead of a GAC filter resulted in higher DOC concentrations in the pool.

431

(comparing Exp. 1 with Exp. 7).

432

As the differences in removal between sand filter and UF (MWCO of 100 kDa) could

433

not be caused by physical sieving (see molecular weight of the BFA substances in

434

Table 2), the different nature of coagulants used was primarily responsible.

435

Polyaluminium chloride (PACl) used for sand filtration is known to perform differently

436

in pre-coagulation filtration hybrid processes compared to prehydrolysed aluminium

437

chlorohydrate (pACH) used for UF treatment (43). When comparing treatment

438

combinations with the same filtration step but different UV post-treatments (i.e. UV(LP)

439

vs UV(MP)), a significantly lower level of DOC was found for UV(MP) irradiation (p <

440

0.05). This could be explained by direct photolysis of organic molecules by UV(MP)

441

irradiation (13) or secondary reactions of the organic matter with highly reactive

442

intermediates (e.g., Cl•, OH•, singlet oxygen (1O2) and NH2•) (44-46) preferably formed

443

by combined UV(MP)/chlorine treatment (12, 43). Photolysis via UV(LP) irradiation is

444

suggested to be negligible (47). It is mainly the low amount of PAC used as a coating

445

layer on the sand filter that leads to the higher stationary DOC concentration in Exp. 7

446

compared to the same treatment concept but with GAC filter (Exp. 1).

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447

3.3.2

Dissolved organic matter composition under stationary conditions

448

The fraction of low-molecular-weight halogenated organic matter found in pool water

449

(<200 g mol–1) is associated with the highest genotoxicity (40). In this study, the

450

dominating proportion of this fraction (LMW-neutrals) in Exps. 1 to 7 contains mainly

451

urea (79.2–96.6%, [DOC]urea [DOC]neutrals–1). The concentration of LMW-neutrals in

452

Exps. 1 to 7 was lower compared to the reference state (without water treatment) by

453

41 to 74% while that of urea was only lower by 1.7 to 56.4 %. Thus, removal of urea is

454

assumed to be not as good compared to the remaining proportion of the LMW-neutrals

455

fraction. Treatment combinations that make use of ultrafiltration revealed generally

456

lower stationary concentrations of LMW-neutrals compared to those with sand filtration

457

(comparing Exp. 2 with Exp. 4, Exp. 3 with Exp. 5 and Exp. 6 with Exp. 7). The

458

stationary concentration of LMW-acids in the reference state was almost zero

459

(0.02 mg L–1), while 0.05–0.38 mg L–1 were found in Exps. 1 to 7. Thus, substances in

460

the LMW-acids fraction must have been formed, for instance, by oxidation of BFA

461

substances and HAs (48, 49). Treatment concepts with UV irradiation (LP or MP) as a

462

second treatment step had higher concentrations of LMW-acids compared to those

463

with activated carbon treatment as a second treatment step (comparing Exps. 2 to 5

464

with Exps. 1, 6 and 7). Previous results that found high concentrations of low-

465

molecular-weight organic matter due to chlorine-UV co-exposure support these

466

findings (17, 50). Higher-molecular-weight substances, such as humic substances, had

467

a comparably low stationary concentration in the basin in Exps. 1 to 7 compared to the

468

reference state, which indicates their effective removal by water treatment. With regard

469

to SUVA, a measure for the aromaticity and grade of saturation of organic substances

470

present in pool water (51), treatment combinations with UV irradiation (Exps. 2 to 5))

471

led to lower values (0.41–1.08 L mg–1 m–1) than without UV irradiation (0.83–

472

3.20 mg L–1 m–1). This effect could be explained by an enhanced opening of the ring 23 ACS Paragon Plus Environment

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473

structure of some of the BFA substances observed during chlorine/UV co-exposure

474

(52, 53).

475

Due to the high reaction rate of ammonia with hypochlorous acid, concentrations of

476

ammonia in the model pool under stationary conditions were found to be very low or

477

below the detection limit of 1 µg L-1, which is in agreement to previous studies

478

performed in real pool systems (54).

479

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Table 4:

Mean values of DOC, fractions of DOC, ammonia, SAC254 and SUVA in the basin of the swimming pool model at

481

stationary conditions for different treatment concepts. Values in brackets represent the relative proportion of each

482

DOC fraction in relation to the total DOC measured with the LC-OCD method (i.e. [DOC]fraction [DOC]total–1). Parameter

Unit

Reference statea

Exp. 1

Exp. 2

Exp. 3

Exp. 4

Exp. 5

Exp. 6

Exp. 7

SandGAC

SandUV(LP)

SandUV(MP)

UFUV(LP)

UFUV(MP)

PAC-UF

PAC-Sand

DOCb

mg L–1 (as C)

9.99

3.11 ± 0.01

5.94 ± 0.11

4.49 ± 0.23

4.98 ± 0.03

2.99 ± 0.09

4.06 ± 0.16

5.90 ± 0.06

DOCtotalc

mg L–1 (as C)

9.99

3.53

5.65

4.64

4.85

2.82

4.48

5.93

DOCBiopolymers

mg L–1 (as C)

0.00 (0.0)

0.02 (0.5)

0.00 (0.1)

0.00 (0.1)

0.00 (0.1)

0.00 (0.0)

0.01 (0.1)

0.00 (0.1)

DOCHumics

mg L–1 (as C)

0.60 (6.0)

0.10 (2.7)

0.08 (1.4)

0.19 (4.1)

0.14 (2.8)

0.13 (4.5)

0.21 (4.8)

0.17 (2.8)

DOCBuilding

mg L–1 (as C)

0.49 (4.9)

0.14 (3.9)

0.33 (5.8)

0.41 (8.9)

0.35 (7.3)

0.21 (7.4)

0.22 (5.0)

0.28 (4.6)

mg L–1 (as C)

0.02 (0.2)

0.05 (1.4)

0.38 (6.7)

0.28 (6.1)

0.28 (5.9)

0.18 (6.3)

0.09 (2.1)

0.25 (4.3)

8.88 (88.9)

3.23 (91.5)

4.86 (86.0)

3.75 (80.8)

4.07 (84.0)

2.30 (81.8)

3.94 (88.0)

5.23 (88.2)

Blocks

DOCLMW-acids + LMW HS

DOCLMW-neutrals mg L–1 (as C) (incl. urea)

483 484 485 486 487

Urea

mg L–1 (as C)

4.75 (47.5)

3.07 (87.0)

4.20 (74.4)

3.44 (74.1)

2.79 (57.5)

2.07 (73.5)

2.65 (59.1)

4.67 (78.8)

Ammoniad

µg L–1 (as N)

850

n.d.

n.a.

7.6

2.2

1.0

n.a.

n.d.

SAC254 nm

m–1

4.94

4.02

5.18

2.22

2.65

4.84

5.14

SUVA

L mg–1 m–1

1.59

0.68

1.15

0.45

0.89

1.19

0.87

a

LC-OCD chromatogram of the reference state is presented in Figure D.2 (SM) As measured by the thermal combustion method as NPOC, Errors represent the standard deviation c Calculated as the sum of all DOC fraction (measured by LC-OCD) d n.a. = sample not analysed, n.d. = sample analysed but compound not detected b

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488

3.3.3

Removal of dissolved organic matter across the treatment train

489

The concentration of DOC was measured over the treatment train of Exps. 1 to 7 when

490

stationary condition was reached (Figure I.1 (SM)). This data was used to calculate the

491

relative amount of DOC removed over the entire treatment train ([DOC]removed

492

[DOC]basin–1) drawn against the stationary concentrations of DOC in the basin of

493

Exps. 1 to 7 shown in Figure 4.

494

Depending on the treatment combination used, the amount of DOC removed across

495

the treatment train ranged between 0.01 and 0.23 mg L–1 (equal to 0.1 ± 2.9% to

496

7.70 ± 4.5%). The high level of the 95% confidence interval determined for the DOC

497

removal is caused mainly by the error in the DOC concentrations in conjunction with

498

the low amount of DOC removed. The DOC removal in Exps. 1 to 7 was similarly low

499

compared to previous data reported for pool water treatment combinations with UF

500

(~11%, ~9%, 18%) (11, 18, 55) or sand filtration (~10%) (55). From the data shown in

501

Figure 4, it becomes obvious that even a low removal of DOC in a single pass of the

502

treatment train results in a significant reduction in the stationary DOC concentration in

503

the basin. This can be explained by the fact that water in the basin is continuously re-

504

circulated and goes through the treatment train several times before stationary

505

conditions are reached. Thus, not only oxidation by chlorine (mineralisation and

506

volatilisation) (20, 56) but also a certain DOC removal across the treatment train leads

507

to low stationary DOC concentrations in the basin.

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508 509

Figure 4:

Relation between DOC removal across the treatment train of different

510

treatment combinations ([DOC]treated [DOC]basin–1) and the mean DOC

511

concentration in the basin when stationary conditions were reached.

512 513

Due to the limited resolution of the LC-OCD method, differences in the concentration

514

of organic matter fractions between in- and outflow of single treatment steps were often

515

not significant (see Figure I.1 (SM)). One of the exceptions was Exp. 1 (Sand-GAC)

516

where significant removals of BBs (~62%) and LMW-acids (~68%) were determined

517

across the bed depth of the GAC filter. Table B.1 (SM) summarizes the properties of

518

the used GAC.

519

As the trend in DOC change across the bed depth correlates with the decrease in

520

chlorine (i.e. increase in cell count) (see Figure 5) it is assumed that the high DOC

521

removal could be caused by microbial degradation in the deeper, chlorine free, regions

522

of the GAC filter (>20 cm bed depth). The chlorine reduction across the bed depth is

523

caused by the reaction of chlorine with the carbon surface (57).

524

It has to be mentioned here that previously to Exp. 1, a test-run of the swimming pool

525

model was performed under the same conditions as Exp. 1 (~26 days of run time). It

27 ACS Paragon Plus Environment

Environmental Science & Technology

526

is assumed that this operation time was sufficient to reach a breakthrough of THMs

527

under the conditions of this experiment (58). Thus, adsorptive processes that typically

528

control the removal of THMs in GAC filters (59, 60) were rated to be negligible which

529

leads to the low overall removal of THMs in the GAC filter.

530

Further, the previous operation time is considered to be sufficient to get the lower

531

regions of the GAC filter becoming microbiologically contaminated. It is important here

532

to note that in pool water treatment, microbiological contamination of GAC filters has

533

to be prevented to assure hygienic safety. However, previous studies that aimed to

534

remove microbial contamination in GAC filters by backwashing the filters with

535

chlorinated water found that at concentrations of free chlorine of up to 2.0 mg L-1

536

(as Cl2), chlorine-backwashing was not effective (61).

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537 538

Figure 5:

Profiles of stationary concentrations of free chlorine (A), total cell

539

count (TCC) (B) and DOC (C) across the bed depth of the GAC filter

540

(Exp. 1). Solid grey lines represent the boundary between the filter

541

layer of GAC and the supporting layer of coarse quartz grains.

542 543

29 ACS Paragon Plus Environment

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544

3.4

545

Impact of different combinations of pool water treatment processes on the concentration of volatile DBPs

546

3.4.1

Volatile DBPs under stationary conditions

547

Figure 6 illustrates the stationary concentrations of free chlorine, combined chlorine

548

and volatile DBPs in the basin of the swimming pool model for the different treatment

549

concepts employed (Exps. 1 to 7). The stationary concentration of combined chlorine,

550

which is a measure for the sum of inorganic as well as some organic chloramines (62)

551

was the lowest for the treatment combination Sand-GAC, slightly exceeded the

552

threshold of 0.2 mg L–1 as Cl2, given by DIN 19643 (3) for treatment combinations of

553

Sand-UV(MP) and UF-UV(LP) and significantly exceeded the threshold for both

554

treatment combinations employing PAC.

555

Previous studies by Soltermann et al. (63) showed that in a real pool with a comparable

556

free chlorine concentration of 0.45 mg L–1 (as Cl2), lower trichloramine concentrations

557

of 0.04 mg L–1 (as Cl2) compared to those determined in the swimming pool model

558

(0.11 to 0.24 mg L–1, as Cl2) could be found. The urea level in this study was 1 mg L-1

559

compared to the concentration found in Exp. 1 to Exp. 7 of 2.07 to 4.67 mg L-1. As

560

Soltermann et al. (63) claim that urea concentrations are not decisive for trichloramine

561

concentrations in swimming pools, the observed discrepancy was explained by

562

differences in the residence time in the basin, which was 4.7 h in this study (see Table

563

1) but as low as ~1 h in the study by Soltermann et al. (63). Further, we assume that

564

the low agitation of the water surface of the swimming pool model led to a low partition

565

of trichloramine to the air phase (64), which led to an accumulation of trichloramine in

566

the aqueous phase. Further, the high loading with urea in the pool model could be a

567

reason for the high NCl3 concentrations found. Schmalz et al (2015) shows that urea

568

is a precursor for trichloramine with high conversion compared to other nitrogen

569

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570

Variations of trichloramine concentrations between Exps. 1 to 7 were by a factor of ~2

571

(0.11 to 0.24 mg L–1, as Cl2). The low variations indicate that water treatment has only

572

minor influences on trichloramine levels and removal of trichloramine in the treatment

573

train does not compensate for the fast trichloramine formation occurring in the

574

swimming pool basin (63, 65). The high concentration of combined chlorine in Exps. 6

575

and 7 could be explained by the low removal of combined chlorine over the treatment

576

train (see Figure 7). The fact that the concentration of trichloramine in Exp. 2 is slightly

577

higher compared to the concentration of combined chlorine could not completely be

578

resolved in this study. Although the amperometric sensors for the detection of

579

combined chlorine were calibrated with the DPD-method on a regular basis (see

580

Section 2.2.1), short changes in pH might have led to false concentrations of combined

581

chlorine (66). In contrast to trichloramine, the stationary concentrations of other volatile

582

DBPs varied between Exps. 1 to 7 by a factor of >10 (TTHM: 11.0–48.2 µg L–1 as

583

CHCl3, dichloroacetonitrile: 4.2–62.6 µg L–1, chloropicrin: 0.2 (LOD)–1.2 µg L–1, see

584

Figure 6). In a survey of 11 real pool systems, Weaver et al. found similar variations

585

for volatile DBPs (10). In this study, the concentration of total THMs (TTHM) was

586

significantly higher for treatment combinations with UV irradiation (Exps. 2 to 5) than

587

without (Exps. 1, 6, 7). The higher TTHM levels could be explained by a UV-induced

588

increase of the reactivity of organic matter towards free chlorine (13). The contribution

589

of brominated THMs between treatment concepts with UV irradiation (17.8 ± 18.5%,

590

Exps. 2 to 5) and those without (1.7 ± 2.1%, Exps. 1, 6, 7) were likely to be different

591

(p < 0.33). The presence of brominated THMs in the swimming pool model under UV

592

irradiation can be explained by bromate found in the sodium hypochlorite stock solution

593

used (3.2 mg L–1 at a free chlorine concentration of 12 g L–1, as Cl2). No bromide was

594

found in the HA, BFA and fresh water sample. Under the presence of natural organic 31 ACS Paragon Plus Environment

Environmental Science & Technology

595

matter (e.g. humic acid), previous studies found that bromate is reduced to bromide

596

under UV-LP and UV-MP irradiation (67), which than might contribute to the formation

597

of brominated THMs.

598

In the literature, contrary data has been reported concerning the formation and removal

599

of brominated THMs under UV irradiation in pool water. While some studies found a

600

promoted accumulation of CHCl3 instead of brominated THMs in pool water during UV

601

irradiation (13, 17), other studies agree with the findings of this study by showing an

602

increased formation of brominated THMs when chlorinating samples after UV

603

treatment (17, 68). UV treatment can break bromine–carbon bonds, not only those in

604

brominated THMs but also in other molecules. The formed bromide is afterward

605

oxidized to BrOH by chlorine, contributing again to the formation of brominated THMs.

606

Thus, it is assumed that UV treatment does not remove brominated DBPs in a

607

recirculated system (69) unless they are stripped from the water.

608

With regard to dichloroacetonitrile, the concentration was significantly higher for

609

treatment combinations with UV irradiation (Exps. 2 to 5) than without UV irradiation

610

(Exps. 1, 6, 7). These findings are in accordance with previous data, where it was

611

shown that UV irradiation at 254 nm promotes the formation of dichloroacetonitrile by

612

chlorination of BFA substances, e.g., L-histidine (70).

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613

614 615

Figure 6:

Stationary concentrations of free chlorine, combined chlorine,

616

trichloramine (A) and chloroform, dichlorobromomethane,

617

dibromochloromethane, dichloroacetonitrile and chloropicrin (B) in

618

the basin of the swimming pool model for different treatment

619

combinations. Concentrations of chloropicrin are multiplied by a

620

factor of 10.

621

33 ACS Paragon Plus Environment

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622

Results of the pairwise correlation analysis between stationary concentrations of

623

individual DBPs and different fractions of organic matter are presented in

624

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625

Table 5. The concentration of TTHMs had a positive, statistically significant correlation

626

with the organic fraction of BBs (p < 0.01) which could be explained by the high THM

627

formation potential of citric acid (6) that is primarily assigned to the fraction of BBs (see

628

Section F (SM)). A highly significant correlation (p < 0.003) was found between the

629

stationary concentration of TTHMs and the fraction of LMW-acids, which partly

630

contained low-molecular-weight HAs. Previous studies agree that humic substances

631

are a major source for THM formation (6, 71).

632

No evident relationship was found in this study between the stationary concentrations

633

of trichloramine and urea (p = 0.88), which agrees with results of Soltermann et al.

634

(63).

635 636

35 ACS Paragon Plus Environment

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637

Table 5:

Page 36 of 47

Correlation analysis of stationary concentrations of DBPs and

638

different fractions of organic matter in the basin of the swimming pool

639

model (Exps. 1 to 7). The table shows Spearman’s correlation

640

coefficients and the corresponding p-values in brackets. DBP

DOC

Fraction of organic matter Humic acids (HAs)

Building blocks (BBs)

LMWacids (LMWa)

LMW neutals (LMWn)

Urea

Combined chlorine

0.11 (0.82)

0.82 (0.02)

0.07 (0.88)

–0.14 (0.76)

0.39 (0.38)

0.04 (0.94)

Trichloramine

0.07 (0.88)

0.50 (0.25)

–0.18 (0.70)

–0.11 (0.82)

0.29 (0.53)

–0.07 (0.88)

TTHMs

0.57 (0.18)

–0.21 (0.64)

0.86 (0.01)

0.93 (0.003)

0.29 (0.53)

0.29 (0.53)

Dichloroacetonitrile

0.18 (0.70)

0.14 (0.76)

0.89 (0.02)

0.68 (0.09)

–0.04 (0.94)

–0.04 (0.94)

641

642

3.4.2

Removal of volatile DBPs across the water treatment train

643

The concentration of free chlorine, combined chlorine and volatile DBPs was measured

644

over the treatment train of Exps. 1 to 7 when stationary condition was reached (Figure

645

J.1 (SM)). This data was used to calculate mean removal values for free chlorine,

646

combined chlorine and volatile DBPs for each single treatment step presented in

647

Figure 7 (calculated as Δc cin–1). The results indicate that evaporation of volatile DBPs

648

in the stirred splash water tank accounts for a loss of 5.4 ± 4.7–15.5 ± 6.3%, depending

649

on the type of DBP considered.

650

Sand filtration and UF showed only a small effect on the concentrations of volatile

651

DBPs (removal of 0.21 ± 11.1–10.2 ± 7.3%). No formation of DBPs across the bed

652

depth of the sand filter, as has been described previously for THMs (72) was observed,

653

while combined chlorine was partly formed across the ultrafiltration module. This could 36 ACS Paragon Plus Environment

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654

be explained by the retention of organic material on the membrane and its subsequent

655

reaction with free chlorine (14).

656

The layer of PAC on the UF or in the sand filter removed a significant amount of

657

combined chlorine (PAC-Sand: 28.2 ± 7.4% and PAC-UF: 38.6 ± 12.6%) but also free

658

chlorine (~100 %). Differences in the removal efficiencies between free chlorine and

659

combined chlorine could be explained by the different reaction rates reported for their

660

surface reaction with carbonaceous materials (73, 74).

661

UV(LP) irradiation at the doses applied in this study [Table B.1 (SM)] reduced less-

662

volatile DBPs and combined chlorine than did UV(MP) irradiation. These findings agree

663

with previous results, where it has been shown that concentration levels of THMs could

664

not be reduced by UV(LP) treatment (75), while a minor mitigation of THMs in pool

665

water has been observed for UV(MP) treatment (13). Although quantum yields for

666

trichloramine photolysis in purified water were found to be similar for UV(LP) and

667

UV(MP) irradiation (76), trichloramine removal was significantly higher in this study for

668

UV(MP) irradiation (91 ± 13%) as compared to that of UV(LP) irradiation (56 ± 34%).

669

The relatively high trichloramine removal by UV(MP) irradiation could be explained by

670

additional degradation caused by secondary reactions between trichloramine and free

671

radicals, e.g., OH• (13, 45, 76, 77).

672

GAC filtration showed the highest removal of volatile DBPs. Almost the entire amounts

673

of trichloramine, combined chlorine and dichloroacetonitrile were removed by GAC

674

filtration. However, the removal of TTHMs across the bed depth of the GAC filter was

675

very low at ~14%. As adsorptive processes control the removal of THMs in GAC filters

676

(59, 60) and the GAC filter of the swimming pool model was operated for ~26 days

677

before determining the THM removal, the low removal efficiency could be explained by

678

a breakthrough of THMs (58). 37 ACS Paragon Plus Environment

Environmental Science & Technology

679 680

Figure 7:

Mean removal values of free chlorine, combined chlorine and volatile

681

DBPs across single treatment steps of the swimming pool model

682

measured when stationary conditions were reached. Blue columns

683

for sand and UF treatment represent the removal values when

684

upstream PAC addition was employed (Exps. 6 and 7).

685

Although these results demonstrate that some treatment steps are effective in

686

removing DBPs, our results demonstrate that their removal does not necessarily result

687

in low DBP concentrations in the basin. These findings raise the importance of the DBP

688

formation potential and kinetics of the organic matter, which has been shown to depend

689

strongly on the treatment concept applied.

690

In the particular case of trichloramine, our results confirm that its removal across the

691

treatment train is not a feasible mitigation strategy since it cannot compensate for the

692

fast formation of trichloramine in the basin. As only weak correlations were revealed

693

between the concentration of trichloramine and nitrogenous precursors such as urea

694

in the pool water, our results support the important role of the level of free chlorine in

695

control of the concentration of trichloramine in pool water (78).

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696

Among the different treatment concepts examined in this study, the treatment

697

combination of Exp. 1, sand filtration with subsequent GAC filtration, revealed the

698

lowest stationary levels of both, organic precursors as well as DBPs that were

699

analysed. These findings indicate the lowest genotoxic potential for bathers for this

700

treatment concept. Conclusively, based on these findings and under the operation

701

conditions employed in this study, we recommend sand filtration with subsequent GAC

702

filtration but we also would like to highlight the potential of microbiological

703

contamination of GAC filters that has to be controlled over the long term to guaranty

704

hygienic safety of pool water.

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705

4

Acknowledgements

706 707

The authors gratefully acknowledge Heike Brückner, Heiko Herrlich, Andreas Kimmig

708

and Christoph Götze for their assistance in the laboratory during this work.

709

Dr. Christina Schmalz and Pascal Müller are acknowledged for their valuable input in

710

setting up the membrane inlet mass spectrometer. Dr. Fabian Soltermann is

711

acknowledged for fruitful discussions and proofreading of the manuscript. The authors

712

would like to thank Prof. Lutgarde Raskin and the anonymous reviewers for their helpful

713

and constructive comments that greatly contributed to improving the final version of

714

the paper. The authors also gratefully acknowledge the German Federal Ministry of

715

Education and Research (BMBF) for funding part of this research under grant

716

02WT1092 as part of the POOL project.

717 718

Supporting information. Operation conditions of the swimming pool model (A);

719

Operation conditions of the treatment processes (B); Numerical modelling the pool (C)

720

and simulated reference state (D); Additional information on analytical methods with

721

regard to DOC (E), LC-OCD (F, G) and MIMS (H); Organic matter across the treatment

722

trains (I); Volatile DBPs across the treatment trains (J); Additional information on the

723

real scale pool (K)

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724 725 726 727 728

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5

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