doi: 10.2965/jwet.20-139 Journal of Water and Environment Technology, Vol.19, No.4: 251–265, 2021

Original Article Fate Evaluation of CSO-derived PPCPs and Escherichia coli in Coastal Area after Rainfall Events by a Three-dimensional Water Quality Model

Chomphunut Poopipattana a, Hiroaki Furumai b

a Department of Urban Engineering, Graduate School of Engineering, The University of Tokyo, Tokyo, Japan b Research Center for Water Environment Technology, Graduate School of Engineering, The University of Tokyo, Tokyo, Japan

ABSTRACT This study developed a fate model of pharmaceuticals and personal care products (PPCPs), includ- ing acetaminophen, caffeine, and crotamiton, derived from combined sewer overflow (CSO) in the Tokyo coastal area. The target PPCPs were proposed as promising sewage markers having different persistency during treatment processes and in the environmental water. The PPCP model consists of hydrodynamic calculations and environmental kinetics by biodegradation and photodegradation. We considered inputs from pumping stations, sewage treatment plants, and urban rivers as CSO sources. We measured the PPCPs concentrations, Escherichia coli, and salinity in the collected surface water samples from several locations around Tokyo coastal area for consecutive days after rainfall events in October 2017 (113 mm), June 2018 (81 mm), and July 2018 (67 mm). We found high correspondence between simulation and monitoring results on E. coli and three PPCPs in the coastal locations for all events, suggesting that the model has the potential to quantitatively evaluate CSO-derived con- taminants in the Seaside Park and nearby locations. Simulation showed that acetaminophen concentration rapidly declined due to its susceptibility to sunlight and biodegradation. Caffeine andE. coli showed different attenuation rates, whereas crotamiton concentration did not change because of its comparable concentration level in CSO.

Keywords: Water quality model, combined sewer overflow (CSO), pharmaceuticals and personal care products (PPCPs), fecal bacteria, coastal area

INTRODUCTION ent. Thus, PPCPs have been proposed as chemical markers representing sewage contamination. They offer benefits over In many urban areas, combined sewer overflow (CSO) has microbial markers such as traditional fecal bacteria in sev- been recognized as the major pollutant source to the receiv- eral ways, including relatively shorter analysis time and high ing water. Untreated wastewater is discharged to surface specificity to human-source contaminants. Because of the water during heavy rainfall to prevent sewage treatment high susceptibility to treatment processes and natural attenu- plants (STPs); consequently, the pollutants were transported ation processes of PPCPs compounds such as acetaminophen to the overflows and deteriorated the surface water quality. and caffeine, they were suggested as labile markers 1–4[ ]. On In the last decade, researchers had gained interest in the the other hand, crotamiton and carbamazepine with a persis- compounds in the group of pharmaceuticals and personal tent nature represent contributions from treated wastewater care products (PPCPs) as emerging contaminants. The main and allow longer tracing periods [1,5,6]. sources of PPCPs entering the environmental water are It is a challenging task to predict and comprehend the from the discharged untreated raw sewage and treated efflu- fate of PPCPs after rainfall events because their behavior

Corresponding author: Chomphunut Poopipattana, E-mail: [email protected] Received: September 18, 2020, Accepted: June 2, 2021, Published online: August 10, 2021 Open Access This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial No Deriva- tives (CC BY-NC-ND) 4.0 License. http://creativecommons.org/licenses/by-nc-nd/4.0/

251 252 Journal of Water and Environment Technology, Vol. 19, No. 4, 2021 can be varied greatly by sources of pollutants, the effect of simulation model reproducing concentrations of microbial hydrological dynamics on their distribution, and biological and chemical markers simultaneously in coastal water. Even and physical processes that affect their degradation. Envi- though Pongmala et al. [23] simulated carbamazepine, total ronmental attenuation of PPCPs with different persistence suspended solids, and E. coli concentration after rainfall seems complex and thus should be further investigated. events, their study focused on simulating the dynamic of pol- Monitoring-based study alone could not provide good dis- lutants in the sewer system, and the field measurements were cussion. For more understanding of the situation, a numerical conducted in specific outfalls. Besides, carbamazepine is a simulation model is necessary. The usefulness of the math- compound with high persistency. Labile compounds were ematical model approach to comprehend situations on a great not included in the study. The objectives of this study were scale was demonstrated in the literature. Björlenius et al. [7] (i) to develop a fate model of PPCPs considering environ- predicted carbamazepine in the Baltic Sea region’s waters mental kinetics in coastal water, (ii) to evaluate the model by by considering inputs from watersheds. Oldenkamp et al. [8] comparing simulated and measured PPCPs concentrations, attempted to determine the aquatic risk of pharmaceuticals and (iii) to discuss the fate of PPCPs in the Tokyo coastal on a global scale. area after rainfall events and compare with the fate of E. coli. Regarding the fate model of CSO-derived pollutants in surface water, the fate model of Escherichia coli and total MATERIALS AND METHODS coliform bacteria were well established in different coastal waters [9–11] and Tokyo coastal area [12] because of their Study area and coastal water sampling direct relationship with human health. Even though simula- The Tokyo coastal area is situated at the uppermost part tion-based studies on PPCPs behavior were conducted in dif- of (Fig. 1), which usually receives CSO-derived ferent freshwater sources, including rivers [13,14], streams, pollutants from potential pollution sources. Major urban riv- and lakes [15–18], studies in the coastal water system were ers in Tokyo, such as Sumida River, Furu River, and Meguro found to be scarce. More particularly, studies conducted in Japan were focused on rivers [19–21]. Fonseca et al. [22] modeled the pathways of CSO-derived pharmaceuticals from the releasing wastewater outfalls to the dispersion in Tagus estuary, Portugal. However, the model did not incorporate stormwater runoff calculation; thus, only treated effluent from STPs and input from rivers were considered the main sources of the pharmaceuticals. In this study, we developed a fate model of PPCPs coupled with a three-dimensional hydrodynamic model to discuss pollutants’ distribution and transportation after rainfall in the Tokyo coastal area. The behaviors of the PPCPs were modeled by considering environmental kinetics, including biodegradation and photodegradation. To obtain the model parameters, we conducted biodegradation and photodeg- radation tests using samples collected from the study area, ensuring the representativeness of degradation kinetics. For comparison, we also simulated the fate of E. coli represent- ing microbial marker. We selected the areas that usually suf- fer from discharges from many potential pollution sources, including overflow chambers, pumping stations, and STPs after rainfall events. A large number of CSO releasing points complicate the situation. To verify simulation results, we carried out field measurements through water sampling Fig. 1 Map of the study area. campaigns after several rainfall events. (Base map GIS data from the Geospatial Information Au- Among a limited study, this study creates and utilizes a thority of Japan [GSI]). Journal of Water and Environment Technology, Vol. 19, No. 4, 2021 253

pollutant load. STP1 (capacity: 70.5 × 104 m3/d) is located upstream of the Sumida River, discharging effluent into the Sumida River. STP2 (4.6 × 104 m3/d) and STP3 (4.5 × 105 m3/d) are located upstream of the . STP4 (7.0 × 105 m3/d) and STP5 (3.5 × 105 m3/d) discharge effluent into the Sumida River’s up- and middle-stream areas, whereas STP6 treats secondary effluent from the STP4 for water rec- lamation purpose. STP7 (8.3 × 105 m3/d) and STP8 (3.0 × 104 m3/d) discharge effluent to the coastal area directly; however, STP8 only receives sewage from a separate sewer system. We collected surface water samples (0.5-m depth) after heavy rainfall events and under dry weather conditions in lo- cations shown in Fig. 2. Table 1 summarizes the information of target rainfalls and sampling date and time. We performed sampling campaigns for several consecutive days after rain- fall events, as previously described in the literature [24,25]. We also collected samples on October 18, 2018, under dry weather condition. We obtained the precipitation and tidal level data from the Japan Meteorological Agency (JMA) at Otemachi Weather Station and Harumi Tidal Observation Fig. 2 Map of the sampling locations. Station, respectively. (Base map GIS data from the Geospatial Information Au- thority of Japan [GSI]). Microbial analysis We collected the samples with a stainless bucket and then River, also receive and transport the pollutants to the coastal dispensed them in pre-sterilized polyethylene bags or steril- area. The Kanda River is one of the Sumida River’s tribu- ized Corning centrifuge tubes (50 mL; Corning Inc., New taries that receives many pollutants from many overflow York, USA). The samples were then transported in cooler chambers and pumping stations along the river under wet bags with ice packs to the laboratory and kept at 4°C until weather conditions. The pumping stations are installed in the analysis, which was performed within 24 h after sampling. drainage area with low elevation to convey CSO to STPs and All analysis was carried out in duplicate; 1 mL and 5 mL direct discharge to the surface water under heavy rainfall of the sample were used for high- and low-concentration events. The total base flow rate from the three main rivers is samples, respectively. estimated to be 9.3 × 104 m3/d. We determined the E. coli using a Chromocult Coliform Under wet weather conditions, discharged primary effluent Agar (Merck, Darmstadt, Germany) and performed the from several STPs in the area contributes to the additional analysis following the manufacturer’s instructions. After

Table 1 Information of the target rainfall events. Sampling Sampling date (days after Maximum rainfall Time Total precipitation (mm) event the rainfall events) intensity (mm/h) October 2017 October 30 (day 1) 09:55–12:18 113 (October 28–29) 16.0 November 1 (day 3) 09:36–12:10 duration: 31 h November 3 (day 5) 09:20–11:35 June 2018 June 12 (day 1) 10:11–14:04 81 (June 10–11) 7.0 June 14 (day 3) 09:15–12:00 12.5 (June 15) June 16 (day 5) 09:32–11:51 duration: 34 h July 2018 July 30 (day 1) 09:40–13:00 67 (July 28–29) 15.0 August 1 (day 3) 09:25–12.26 duration: 29 h August 3 (day 5) 09:14–12:10 254 Journal of Water and Environment Technology, Vol. 19, No. 4, 2021 overnight incubation at 37°C, we counted the dark blue-to- Laboratory experiment to determine environmental violet colonies as E. coli. kinetics To obtain the kinetic constants of PPCPs, we conducted a PPCPs analysis biodegradation test. In the experiment, the bacteria commu- Three PPCP compounds were targeted: acetaminophen nity from raw sewage was considered a major role in PPCPs (ACE) and caffeine (CAF) as labile compounds and crotami- biodegradation after CSO events. Bacteria inoculum was ton (CTMT) as conservative compounds. The collected sur- prepared from the raw sewage sample collected on March 17, face water samples (2 L) were dispensed in pre-combusted 2020. Figure S1 shows the inoculum preparation procedure. glass bottles and added with 1-g/L ascorbic acid for sample First, the sample was subjected to sonication at 50% ampli- preservation. The samples were transported under cool con- tude for 45 s in three cycles. Between each cycle, the sample ditions to a laboratory and filtered through a glass fiber filter was cooled for 15 s. Next, the sample was filtered through a (GF/F, 0.7 µm). Then, the samples were kept in a refrigerator 3-µm polycarbonate membrane to remove particulate mat- at 4°C. ters, considering the size of coliform bacteria. Then, the fil- For sample preparation, the samples were injected with a trate was filtered through a 0.2-µm polycarbonate membrane mixture of internal standards at concentrations of 100 ng/L to collect bacteria cells. After that, the membrane filters were for CAF and 40 ng/L for other PPCPs, before they were ap- cut into small pieces and resuspended in the 0.2-µm filtrate plied to solid-phase extraction procedure with Oasis HLB of the raw sewage. Finally, bacteria cells were detached by cartridge (6 cc; Waters Co., Milford, USA) within 48 h. continuous vortex for 2 min. LC-MS/MS analysis was carried out using Q Exactive We conducted a biodegradation experiment in duplicates Hybrid Quadrupole-Orbitrap Mass Spectrometer (Thermo- using 500-mL glass bottles in the dark. Figure S2 shows Fisher Scientific, Waltham, USA) for the detection and the experiment setting. To imitate the situation after CSO quantification of PPCPs. Analytes were separated in Accela events, we considered 10-fold dilution; 50-mL inoculum was high-performance liquid chromatographic system (Thermo- mixed with tap water. Live cell abundance was controlled Fisher Scientific) equipped with Hypersil GOLD column at 106–107 cells/mL using the flow cytometry technique. (150 × 2.1 mm; Thermo-Fisher Scientific) with 5-μm particle Fluorescent dyes (SYBR Green I + propidium iodide) were size. Every compound was detected in positive ion mode. used to discriminate cell viability. To completely remove the The MS instrumental setting and gradient elution program bacteria cells, we prepared a biostatic control by filtration is set following the literature [26] with some modifications. three times through the 0.2-µm membrane. Mixed PPCPs Data were acquired in the selected reaction monitoring mode standard solution was spiked. The final concentrations were on precursor ions, followed by data-dependent MS/MS scans 15–30 µg/L. All units were covered in aluminum foil and for fragment ions. Tables S1 and S2 show the analytical incubated in a dark temperature-controlled room at 25°C. information of each compound and the internal standards, The samples were continuously mixed by magnetic stirrers respectively. for 8 days to maintain sufficient dissolved oxygen and ho- The detection and confirmation of target compounds were mogenous condition. The samples were collected on day 0, based on their mass-to-charge ratio (m/z) and retention time 0.5, 1, 2, 4, 6, and 8 during incubation. with criteria of 5-ppm mass tolerance and 0.3-min retention We conducted a photodegradation test in the light simu- time window. Table S3 shows the analytical performance, lator chamber at the National Institute for Environmental including limit of detection, limit of quantification, linearity, Studies, as shown in Fig. S3. Environmental water samples and recovery. used in the experiments were collected at Suidobashi bridge of Kanda River and mouth. Similarly, 10-fold Raw sewage and secondary effluent samples collection dilution was conducted in filtered raw sewage with river sam- The 24-h composite samples of influent and secondary ef- ples. We measured the salinity level, in which we observed fluent from five STPs were collected under dry weather con- low salinity in the Kanda River sample (2.1 ppt) and high ditions in May, July, and December 2017 and January 2018 (n salinity (20.7 ppt) in the Meguro River sample. Before using = 9) to investigate PPCPs concentrations. Repeated sample them in the experiment, the samples were filtered through collections were conducted in one of the STPs, considering GF/F glass fiber filters (0.7 μm). Mixed PPCPs standard the seasonal variation of the concentrations. Tables S4 and solution was spiked. The final concentrations were 15–30 S5 summarize the measured concentrations. µg/L. Samples were filled in 500-mL beakers. Beakers were Journal of Water and Environment Technology, Vol. 19, No. 4, 2021 255 wrapped with Al-foil on the side to prevent light exposure Sumida River. The expanded model includes 29 pumping to the sample’s side; hence, samples were exposed to the stations, 6 STPs, and 6 inflow rivers as sources of CSO, as light only from the top part. A control unit was included, in well as hundreds of overflow chambers in the 23 wards of which it was covered with Al-foil in all parts. The samples Tokyo, which are accounted for in the calculation. The fol- were exposed to an average light intensity of 315.8 W/m2 lowing briefly discusses the estimation method. (400–700 nm) continuously for 58 h. Magnetic stirrers were The river flow is given as boundary input data to the hy- used to ascertain homogenous and similar light exposure to drodynamic model. CSO discharge from overflow chambers all units. Temperature and humidity were controlled at 25°C (gravitational flow system) and pumping stations is estimated and 80% in a light simulator chamber. A water bath was used in the model. In brief, the dry weather flow is estimated on to prevent samples from overheating. the basis of population and wastewater generation flow per capita. The drainage runoff is calculated using the synthetic Hydrodynamic model rational method. The calculation of travel time was based Here, we used the previously validated hydrological on the drainage area. The total CSO can then be estimated simulation program written in FORTRAN. The whole model by adding drainage runoff to the dry weather flow and system consists of two nested computational models, includ- subtracting with intercepting flow, which is three times the ing the Tokyo Bay model and the Odaiba model, covering hourly maximum dry weather flow in Tokyo (Fig. S4). Under the coastal region located in the Tokyo Bay. The 3D hydro- wet weather conditions, sewage exceeding the intercepting dynamic model was originally developed by Koibuchi and capacity is directly discharged to the receiving water. The Sato [27]. The Navier–Stokes and continuous equations were intercepted sewage entered STPs and underwent a primary solved under hydrostatic and Boussinesq approximations to treatment process. However, primary effluent without bio- express the Tokyo Bay flow dynamics considering density logical treatment is also discharged from STPs as CSO. The effect by temperature and salinity. The Tokyo Bay model model calculates the pollutant loads both from the direct has a 2-km mesh system consisting of horizontal and verti- discharge and primary effluent. CSO discharge is estimated cal (25 × 33) grids. Then, another 3D hydrodynamic model separately by sources using drainage area-based calculation. was developed by Onozawa et al. [28] for the Odaiba coastal Tables S6 and S7 show the detailed information of the runoff area, which was then modified by Shibata et al. [12] for an calculation method and the equations. expanded study area. The Odaiba model was expressed by a 100-m mesh system consisting of 60 × 290 grids, emphasiz- Water quality model for E. coli and PPCPs consider- ing reproduction of the hydrodynamic field with consider- ing their environmental kinetics ation of the Sumida River flow receiving urban runoff and In the Odaiba model, E. coli and PPCPs are incorporated CSO discharge from sewage districts. The sigma-coordinate as fecal indicators and sewage makers to discuss their fate in vertical grid system was also used, which is widely applied the coastal area. Regarding E. coli, their settling and inacti- for the computational model in meteorology and oceanogra- vation by salinity were considered. In addition, Nakajima et phy. The nested grids make possible a representation of the al. [30] had added effects of inactivation by ultraviolet light stratification effect. This grid system flats out the variable in the fate model. The model solves the mass conservation bottoms; thus, the number of vertical grids is ten for the equation and advective diffusion equations, as shown in whole area. equation (1).

River system and CSO discharge in the Odaiba ∂CCC ∂ ∂ ∂ C ∂22 C ∂ C ∂∂ C coastal area +u + v + w = K + K +() K +− SC kC ∂∂∂t xy ∂ zxy∂∂xy22∂∂z z z There are six urban rivers, many overflow chambers, and (1) pumping stations in the Odaiba coastal area and the Sumida River system. Kojima et al. [29] elaborated the expression C: concentration (mL−3) of CSO discharge from overflow chambers and pump sta- t: time (T) tions and calculated the flow rates from the Sumida River u, v, and w: flow velocities in x, y, and z axes (LT−1) and its tributaries. For estimating the accurate inflow from Kx, Ky, Kz: turbulent diffusion coefficients in x, y, and z the Sumida River, it was necessary to determine the diver- axes (L2T−1) sion flow from Ara River at the most upstream point of the 256 Journal of Water and Environment Technology, Vol. 19, No. 4, 2021

S: sedimentation rate (LT−1) only for E. coli [S = 0 for For the simulation of the fates of E. coli and PPCPs, the PPCPs] meteorological data and tidal levels at the Tokyo reference k: inactivation of E. coli or degradation coefficient of point were the input data. Meteorological data include pre- PPCPs (T−1) cipitation, temperature, humidity, wind speed, wind direc- To determine the fate of E. coli in the surface water, we tion, cloud, solar radiation, vapor pressure, and sea air pres- considered and included several factors in the model. The sure. The data were obtained from JMA and TMG. The tidal inactivation rate of E. coli was determined, including basic level, temperature, and salinity were given as open boundary decay rate (kb), inactivation rate by salinity (ks), and inactiva- conditions in the Odaiba model. tion rate by sunlight (kI), as shown in equation (2). RESULTS AND DISCUSSION k=++ k k C k Iexp( − ah) (2) b ss I Monitored concentrations of E. coli and PPCPs after −6 −1 Kb: basic inactivation coefficient = 1.88 × 10 (s ) rainfall events Ks: inactivation coefficient depending on salinity = 0.0032 Figure 3 shows the monitored surface concentrations of E. −1 −1 (day ppt ) coli and three PPCPs on the first day after rainfall events (day Cs: salinity level (ppt) 1) and under dry weather conditions in the coastal area. The 2 −1 −1 KI: inactivation factor by sunlight = 0.069 (m day W ) figure was drawn using the previously published data 25[ ]. −2 I: solar irradiance (Wm ) Compared with those under dry weather, elevated concentra- −1 a: extinction coefficient = 0.1 (m ) tions were observed in all events for E. coli and labile PPCPs h: water depth (m) of ACE and CAF. Results showed that E. coli contaminated In the simulation model, the average influent concentration the area in a wide region, especially after the heaviest rain- 6 of E. coli was 5.0 × 10 CFU/100 mL. We used E. coli and the fall event in October 2017. ACE and CAF shared a similar total coliforms for technical standards of effluent quality of trend with E. coli, suggesting that they have the same major the STP. Therefore, the total coliform count was converted to source: raw sewage. The characteristic suggested their utility an order of magnitude lower of E. coli concentration, taking to trace recent fecal contamination. into account their high correlation [31]. The E. coli concentra- On the other hand, CTMT, which is a conservative com- tion in the primary effluent was set to 300,000 CFU/100 mL, pound, showed less elevated concentrations after the rainfall 3 according to the effluent standard (3,000 coliforms/cm ). The events. In October 2017, surface concentrations were lower E. coli concentrations in the secondary effluent from STPs than those observed under dry weather. Its surface concen- were set to 30–2,240 CFU/100 mL, depending on coliforms tration tended to be less affected by CSO discharges and can values at different STPs, as shown in the annual report of the be highly diluted under an extreme rainfall event. Following Bureau of Sewerage Works, Tokyo Metropolitan Govern- the observation data, E. coli and two types of PPCPs, includ- ment (TMG) [32]. The boundary condition of E. coli was 100 ing labile and conservative compounds, showed independent CFU/100 mL in the Ara River and coastal water from Tokyo behavior. Bay. On the other hand, the fates of PPCPs were determined Environmental degradation of PPCPs in coastal water considering the degradation coefficient following equation We investigated the environmental kinetics of PPCPs (3). Biodegradation and photodegradation were considered by biodegradation and photodegradation. We applied the as the main mechanisms for their attenuation. pseudo-first-order reaction rate to estimate the kinetic pa- rameter values based on batch experiments (Fig. 4 and 5). k=+− k k Iexp( ah) (3) bio photo CTMT was persisted to biodegradation. On the other hand, ACE was found to be most sensitive to sunlight and readily k: degradation coefficient (day−1) biodegradable among three PPCPs. k : biodegradation rate constant (day−1) bio Biodegradation kinetic constants that were derived from k : photodegradation rate constant [m2 (W·day) −1] photo the experiments were adjusted for application as model pa- I: solar irradiance (Wm−2) rameters. Biomass concentration was considered using cell a: extinction coefficient = 0.1 (m−1) counts. In the batch experiment, live cells were controlled h: water depth (m) at 106–107 cells/mL. However, the number of live cells is Journal of Water and Environment Technology, Vol. 19, No. 4, 2021 257

Fig. 3 Monitored concentrations of E. coli and PPCPs on day 1 after rainfall events in October 2017, June 2018, and July 2018, compared to range of concentration under dry weather observed in coastal locations (n = 5). (Data from the literature [25] were used for illustration.)

Fig. 4 PPCPs biodegradation kinetics at salinity of 0.1 ppt and 20 ppt. *↓= Below LOD. Half of LOD was used for kinetic constants calculation. **Concentration change less than 20% was con- sidered as no decrease (lag phase). Lag phase and degradation phase was considered separately. 258 Journal of Water and Environment Technology, Vol. 19, No. 4, 2021

Fig. 5 PPCPs photodegradation kinetics in Kanda and Meguro river samples.

Table 2 Environmental kinetic parameters of the PPCPs obtained from experiments. Biodegradation rate (day−1) Photodegradation rate (day−1) Compound Kanda River Meguro River mouth 0.1 ppt 20 ppt (2.1 ppt) (20.7 ppt) CTMT 0 0 0.14 0.14 CAF 0.10 0.044 0.05 0.05 ACE 0.174 0.419 2.86 0.94 expected to be 10-fold diluted under actual coastal condi- C: PPCPs concentrations (ng L−1) tions. (Live cells in raw sewage and coastal water are 107 and Apart from biomass concentration, other factors possibly 106 cells/mL, respectively.) Thus, the kinetic constants were influenced the biodegradation rate. However, we considered divided by ten for application following equation (4). Table 2 the biomass concentration to be the most critical factor. Be- summarizes the first-order-reaction rate constants applied in sides, high resolution and complicated numerical calculation the PPCPs fate model. are not the main targets in this study. The same biodegradation rate was applied similarly to the Biodegradation rate= − kXC (4) whole study area. The salinity effect on the biodegradation rate was also considered by assuming a linear relationship k: specific biodegradation rate constant [day−1 · (cells mL–1­ )−1] between salinity and kinetic constant. Kinetic constants at X: biomass concentration (cells mL−1) Journal of Water and Environment Technology, Vol. 19, No. 4, 2021 259

Table 3 Boundary conditions for the river discharge. Concentration Model parameter ng/L CFU/100 mL CTMT CAF ACE E. coli Urban river discharge 120 100 20 100 Diverted flow from Ara River 10 200 30 100 Note: The values were decided on the basis of the data from the literature [25].

Table 4 Influent, primary effluent, and secondary effluent concentrations of target compounds andE. coli were given as boundary conditions. Concentration ng/L CFU/100 mL Source CTMT CAF ACE E. coli River area Coastal area River area Coastal area River area Coastal area All areas Influent 840 530 51,500 91,000 24,400 35,400 5.0 × 106 Primary effluent 880 440 41,200 72,800 12,200 17,700 3.0 × 105 Secondary effluent 880 440 70 70 30 30 30–2,240a aEscherichia coli concentrations in the secondary effluent were set differently depending on each STP based on the report of the Bureau of Sewerage Works, TMG [32]. The data were collected in this study. salinity lower than 0.1 ppt were assumed to be similar to those effect on rate constants was considered as well in this rela- observed at salinity lower than 0.1 ppt. A similar assumption tionship. Regarding salinity outside the experimented range, was made for constant at salinity higher than 20 ppt. a similar concept to biodegradation rate was considered. No From Table 2, ACE was the most readily biodegradable DOM effect was observed for CAF and CTMT; thus, it was among PPCPs. The highest degradation rate was estimated at not considered for these compounds. Besides, photodegrada- 0.419 day−1. On the other hand, CAF was also biodegradable, tion rates were adjusted to sunlight irradiation from the input but CTMT was persisted to biodegradation. Results also meteorological data. showed a different degradation rate under different salinity levels. Settings of boundary conditions on PPCPs based on As for photodegradation rate, previous studies illustrated monitoring data the influence of DOM presence on the enhancement of indi- River discharge rect photodegradation rate through the production of reactive Concentrations were given as listed in Table 3. Concentra- intermediates such as hydroxyl radical (•OH) and singlet tions in urban river discharge, including , 1 oxygen ( O2) [33]. Thus, we considered the effect of DOM on Kanda River, Shakuji River, Furu River, Meguro River, and the photodegradation rate. Nomi River, were decided on the basis of the monitoring Salinity level was used to estimate the dilution of DOM data at Kanda River mouth on dry days (Table S8). As for derived from urban rivers by coastal water because the the Ara River, the concentrations observed at the Iwabuchi amount of DOM was unknown. As shown in Table 2, ACE Watergate were given. Figure 1 shows the locations of the was highly sensitive to light and either salinity or DOM. The Kanda River mouth and Iwabuchi Watergate. relationship between ACE rate constants and salinity was considered using experimental results observed in the Kanda Influent, primary effluent, and secondary effluent (2.1 ppt) and Meguro River samples (20.7 ppt). The Kanda concentrations River sample represents freshwater having a high DOM Table 4 summarizes the input concentrations. The STPs amount because it was collected upstream. On the other were separated into two groups because of a significant hand, the Meguro River sample was collected at the river variation in the influent concentration, especially ACE and mouth representing surface water with a high contribution CAF between STPs located in river and coastal areas. STP1, of coastal water and diluted DOM amount. Possible salinity STP3, STP4, and STP5 were considered those in the river 260 Journal of Water and Environment Technology, Vol. 19, No. 4, 2021 area, whereas STP7 was categorized in the coastal area. In Overall, the correspondence among simulated and moni- secondary effluent concentration, it was not much - differ tored concentrations was high. Concentration levels were ent for ACE and CAF; thus, average values from all STPs higher at the Rainbow Bridge because of its proximity from were used. Regarding CTMT, it is hardly removed by the sources such as STP7 and pumping stations. On the other treatment process; thus, effluent concentration is given cor- hand, Odaiba’s temporal change tendency has fluctuated respondingly to the concentrations observed in the STPs in less because it is a protected area with less influence from each area. contamination. Different concentrations were given to the two effluent For E. coli and labile PPCPs, the concentrations gradually types because the model calculates loads from both direct increased during the precipitation period, followed by the discharge and primary effluent as CSO. For CTMT, primary decrease due to tidal mixing and environmental degrada- and secondary effluents were equally assigned considering tion. Focusing on the tidal level change, we suggested a high their persistent behavior. On the other hand, the removal degree of dispersion under a great tidal range (1.76 m) in this of CAF and ACE was decided on the basis of previous event. On the other hand, the concentration of CTMT was literature. Primary effluent underwent treatment processes, found to decrease during intense precipitation. The trend including sedimentation and chlorination. According to pre- was highly correlated to rainfall intensity. vious research, Kumar et al. [34] reported < 1%–8% removal for CAF and < 1%–7% removal for ACE during the primary Different spatial–temporal distribution of PPCPs sedimentation. On the other hand, Yang et al. [35] found that and E. coli (July 2018 event) <10% of CAF was removed during chlorination, and up to Figure 7 shows the simulated spatial-temporal distribu- 42% was found for ACE. Therefore, 20% of removal was tions of CTMT, ACE, CAF, and E. coli in the July 2018 event. assumed for CAF and 50% for ACE after primary treatment. Comparing the trend among three PPCPs, the concentration of CTMT rarely changed after the rainfall (Fig. 7a). The Initial condition setting compound shared a similar concentration in influent and sec- To start the simulation after rainfall events, we required a ondary effluent (Table 4). Therefore, less influence of CSO start-up running period to stabilize and achieve reasonable on its concentration change was observed in this area where a surface concentration ranges under dry weather. The initial significant amount of secondary effluent is discharged daily. concentrations of three target compounds were selected The impact of CSO on the concentration of CAF and ACE on the basis of minimum concentrations observed in the was distinct (Fig. 7b, 7c) because of their high concentration coastal area under dry weather (Table S8). Concentrations levels in influent but low in secondary effluent. Under dry of ACE and CAF were set at 30 and 220 ng/L, respectively. weather conditions, the logarithmic concentration of CAF In CTMT, it is presented similarly to the concentration in was approximately two in the coastal area, and it increased influent and effluent; thus, a higher level of concentration up to four in the Sumida River, Meguro River mouth, and was observed in the upstream and river mouth area than the near STPs in coastal area 24 h after rainfall. After a few days, coastal area. Therefore, the initial concentration of CTMT the overall concentration decreased because of degradation was given according to the location of the grids. Grids lo- and tidal mixing. The concentration levels were back to dry cated higher than Sumida River mouth are accounted for the weather level within 5–7 days after the rainfall. upstream area and assigned a value of 90 ng/L, whereas the CAF and ACE shared a similar spatial distribution pattern. coastal area located downstream was assigned at 50 ng/L. Elevated concentrations were observed in the similar area After a start-up running period of 4 days or more, the desired and gradually expanded to the whole area. Thus, CAF was surface concentration range can be achieved. selected as a representative to compare with E. coli (Fig. 7d). E. coli shared a similar spatial–temporal distribution Time-based simulated concentration changes (July pattern with CAF, but it was different for CTMT. Especially, 2018 event) the canal area was found to be highly contaminated by CAF Simulated concentrations of E. coli and PPCPs after rain- and E. coli after rainfall event because of the discharge from fall in the July 2018 event are shown on an hourly basis (Fig. Meguro River. As well as Meguro River, the primary effluent 6a, 6b), at the Rainbow Bridge and Odaiba, respectively. of STP7 was expected to contribute to the high-level concen- The monitored concentrations are included for verification tration because of their proximity. Even though CAF and E. purposes. Precipitation and tidal level are also shown. coli shared a similar tendency, E. coli was inactivated faster. Journal of Water and Environment Technology, Vol. 19, No. 4, 2021 261

Fig. 6 Time-based comparison of simulated surface concentration of PPCPs and E. coli and monitored dry concentration at the (a) Rainbow Bridge and (b) Odaiba after the rainfall event in July 2018.

A rapid decrease in concentration was observed within 3 sources. During rainfall events, the main source of E. coli is days because of their susceptibility to high salinity and sun- the CSO discharge. On the other hand, CTMT comes from light inactivation. CSO with diluted concentration and secondary effluent with The different trend observed among CAF and E. coli comparable concentrations to raw sewage, as mentioned might also cause by their different removal efficiency before above. primary effluent discharge. Even though the pollutant loads from primary effluent discharge is expected to be much Comparison between simulated and monitored lesser than those from directly discharged CSO without coastal water concentrations treatment, considering that they were partially removed Table S8 summarizes the monitored concentrations of during the primary process, it could potentially influence PPCPs. The comparison was carried out on the basis of the water quality because of the proximity of discharge loca- five locations in the coastal area (Fig. 2). The monitoring tion of STP7 and pumping stations to the Odaiba area. This data after the three target rainfall events were used. Figure implies that it is useful to conduct a model evaluation on the 8 compares the simulated concentrations of CTMT, CAF, effectiveness of disinfection schemes during CSO events at ACE, and salinity with the monitored concentrations. Plots STP7 and pumping stations to protect the Odaiba Seaside are shown in different colors, indicating sample collection Park’s water quality. days after rainfall events. As shown in Fig. 7a and 7d, CTMT and E. coli are much High correspondence between simulated and monitored different because of their contrastive feature in the main concentrations was observed in most cases. However, over- 262 Journal of Water and Environment Technology, Vol. 19, No. 4, 2021

Fig. 7 Simulated spatial–temporal distribution of (a) CTMT, (b) CAF, (c) ACE, and (d) E. coli after the rainfall event in July 2018 (Dark blue color represents land area).

estimation can be observed on day 1 (first day after the rain- water quality in Odaiba Seaside Park is directly influenced fall event) when the CSO pollutants were freshly discharged, by the coastal water around Rainbow Bridge and Tokyo especially in October 2017. On the other hand, salinity shows Bay tunnel, which receive pollutant discharge from Meguro high correspondence, suggesting high reproducibility of hy- River, STP7, and pumping stations. drodynamic calculation. Even though PPCPs concentrations in raw sewage were given separately in different regions, the CONCLUSIONS concentrations’ seasonal and time variation may influence the overestimation. The degree of model overestimation was In this study, we developed a fate model of the three PPCPs higher for ACE and CAF because of their high raw sewage with different persistency. Boundary and initial concentra- concentration, supporting the assumption. tions were carefully selected on the basis of the monitoring The model was found to produce consistent results in data. Laboratory studies were also conducted to determine the coastal locations, including Rainbow Bridge, Odaiba, biodegradation and photodegradation kinetic constants, and Tokyo Bay tunnel. Considering that this study’s main which were provided as model parameters. The experiments focus was the water quality in the coastal area, especially were designed to imitate real situation after rainfall events the Odaiba Seaside Park, the model’s reproducibility is good using environmental water and raw sewage. ACE was found enough to discuss the results in these locations. Besides, the Journal of Water and Environment Technology, Vol. 19, No. 4, 2021 263

Fig. 8 Comparison between the simulated salinity, concentration of CTMT, CAF, ACE, and E. coli and the monitored con- centrations (n = 5) after rainfall events.

to be sensitive to sunlight and was readily biodegradation. ness of the primary treatment schemes during CSO events CAF was quite persisted to sunlight degradation but was bio- to prevent water quality degradation in the Odaiba Seaside degradable. On the other hand, CTMT was highly persisted. Park caused by pollutants with different persistency. The results from the simulation model were compared In future work, sensitivity analysis is necessary to evaluate with the monitoring data from three different rainfall events. the model parameters and to determine the importance of Simulation results well explained the monitoring data in the applying the parameters such as photodegradation kinetics. coastal area. Therefore, the model was useful for evaluat- Moreover, additional experiments are crucial to establish ing fecal contamination in Odaiba Seaside Park and nearby appropriate calculations considering the salinity effect on influential locations, such as the Rainbow Bridge and Tokyo biodegradation kinetics and DOM effect on indirect photo- Bay tunnel. However, there was overestimation of ACE and degradation kinetics in the model. CAF observed on Day 1 when CSO freshly discharged. The model overestimation might be caused by seasonal and time SUPPLEMENTARY MATERIALS variation of ACE and CAF concentrations in raw sewage, and the temporal variation of labile PPCPs should be consid- Analytical information, performance of PPCPs analysis, ered to improve model accuracy. and CSO discharge estimation method can be found in the We simulated and compared the spatial–temporal distri- supplementary materials. The results from laboratory stud- bution of PPCPs and E. coli. The distribution patterns were ies and monitored data of PPCPs concentration, E. coli, and similar among labile PPCPs and E. coli. On the other hand, salinity after the three rainfall events and under dry weather CTMT was less affected by the CSO event because of its per- were also included in the supplementary materials. sistence in wastewater treatment. Modeling multiple markers Supplementary Materials file for this article is available at increased the confidence for evaluating fecal contamination the link below. in the Tokyo coastal area. Also, modeling markers with dif- https://www.jstage.jst.go.jp/article/jwet/19/4/19_20-139/_ ferent removal efficiencies could help evaluate the effective- supplement/_download/19_20-139_1.pdf 264 Journal of Water and Environment Technology, Vol. 19, No. 4, 2021

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