Globally applicable water quality simulation model for river basin chemical risk assessment

Journal of Cleaner Production 239 (2019) 118027

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Journal of Cleaner Production journal homepage: www.elsevier.com/locate/jclepro

Globally applicable water quality simulation model for river basin chemical risk assessment Yuriko Ishikawa a, *, Michihiro Murata b, Tomoya Kawaguchi b a b

National Institute of Advanced Industrial Science and Technology (AIST), 16-1 Onogawa, Tsukuba, Ibaraki, 305-8569, Japan Nihon Suido Consultants Co., Ltd (NSC), 22-1 Nishi-Shinjuku 6-Chome, Shinjuku-ku, Tokyo, 163-1122, Japan

a r t i c l e i n f o

a b s t r a c t

Article history: Received 27 December 2018 Received in revised form 5 July 2019 Accepted 12 August 2019 Available online 14 August 2019

Many companies around the world need to assess water environmental risks in accordance with sustainable development goals (SDGs) set by the United Nations, environmental, social, and governance (ESG) investment, the Carbon Disclosure Project (CDP) water program, and also their corporate social responsibility (CSR) activities. Although companies have been requested to voluntarily evaluate their water risks and provide information thereon, the water quality risk in river basins is difficult to assess owing to insufficient monitoring information. This paper proposes the National Institute of Advanced Industrial Science and Technology - Standardized Hydrology-based AssessmeNt tool for chemical Exposure Load (AIST-SHANEL) model as a river water quality simulation model that can be employed for chemical risk assessment and evaluation of the effects of use of a company's consumer products on river basins. AIST-SHANEL is the only unsteady analysis model that estimates spatial and temporal chemical concentrations and assesses water quality within river basins by point/non-point emission of chemical substances. To evaluate the efficacy of AIST-SHANEL, a case study focused on the surfactant linear alkylbenzene sulfonate (LAS), found in the detergents of widely used health care products, was conducted. The water risk assessment was performed in three Japanese river basins with different watershed characteristics: Tama River in urban Tokyo with high sewerage penetration rate, Nikko River in urban Nagoya with a low sewerage penetration rate, and the nonurban None River with no sewerage. In Tama River, the LAS river water concentrations were found to be strongly influenced by seasonal variations as well as the flow rate associated with dilution and temperature-induced biodegradation. Their concentrations in Nikko River were strongly affected by flooding, indicating that the influence on their transport through the drainage channel was due more to low sewage levels than temperature-induced biodegradation. In None River, the LAS river water concentrations were only slightly affected by dilution. The river sediment concentrations of LAS in the three river basins showed mild seasonal variations. The 95th percentile of river water concentrations, predicted environmental concentration (PEC), at the most downstream and maximum concentration grids in the three river basins were 4.5 and 51.2 mg/m3 for Tama River, 30 and 58.6 mg/m3 for Nikko River, and 0.15 mg/m3 for None River. Neither of the PECs nor maximum values at these grids exceeded the predicted no-effect concentration (PNEC) of LAS (270 mg/ m3), suggesting minimal ecological risk in these river basins over the year for all target river basins due to LAS. Furthermore, we analyzed a scenario in which LAS was substituted with the highly biodegradable alcohol ethoxylates (AE) for corporate self-management measures in Nikko River with only a few sewers, and therefore only a low risk. The AIST-SHANEL model has useful and widespread application with no need for monitoring by companies; as long as the relevant watershed characteristics can be obtained, water risk assessments of chemical substances can be performed for any river basin in the world. © 2019 Elsevier Ltd. All rights reserved.

Handling Editor: Hua Cai Keywords: River water and sediment concentration Chemical substances Spatial and temporal estimation Unsteady analysis model Water risk assessment Carbon disclosure project (CDP) water program

1. Introduction

* Corresponding author. E-mail address: [email protected] (Y. Ishikawa). https://doi.org/10.1016/j.jclepro.2019.118027 0959-6526/© 2019 Elsevier Ltd. All rights reserved.

Business practices are increasingly conforming to the sustainable development goals (SDGs) set in 2015 by the United Nations and environmental, social, and governance (ESG) investment. In

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Y. Ishikawa et al. / Journal of Cleaner Production 239 (2019) 118027

order to promote a sustainable water environment, the Carbon Disclosure Project (CDP) water program, which is an information disclosure program for corporate water risks run by the international nonprofit organization CDP, was initiated in 2015. According to the program (CDP, 2017a), water risks related to business must be explicitly stated, including physical risks such as shortage/excess of water volume and water pollution, regulation risks related to water use imposed by governments, and reputation risks arising from water-related issues. Companies around the world have been requested to voluntarily evaluate their water risks, concur with the results, and provide information on these risks, as well as their own corporate social responsibility (CSR) activities. Recently, many companies have decided to reduce their impact on water resources by reducing water consumption and intake according to quantitative targets. In Japan, 90% of companies consider their employees, communities, and regulators in water risk assessments. However, only approximately 35% of these companies request that their suppliers report their water risks and water intake. Water risk assessment and management in many countries and river basin areas are important for overseas business expansion and globalization of the supply chain. The effects of chemical discharge on river water quality can vary depending on the river basin characteristics such as land-use, population, industrialization, and wastewater treatment conditions. Therefore, water risk assessment in any river basin should consider these regional characteristics. Typically, the water quality in river basins is assessed by water quality measurements; however, considerable effort and costs are involved when monitoring multiple sites, study periods, and chemical substances. The CDP water program has a comprehensive method for water risk assessment and most companies employ their own knowledge, regional government databases, water risk assessment tools, and life cycle assessment (CDP, 2017a, 2017b, 2016). The most widely used water risk assessment tool in the CDP water program is a mapping tool for identifying water risks, developed by the World Resource Institute (WRI), called WRI Aqueduct (WRI, 2017). This tool has 12 indicators for three risks and contains information and data on 15,000 river basins around the world. In addition, the WBCSD Global Water (WBCSD, 2018) and WWF - DEG Water Risk Filter (WWF Germany, 2018) tools can rapidly evaluate the water risks in watersheds. Although these tools are convenient, they are mainly used for water quantity risk assessment in terms of water consumption, mainly as supplier and factory initiatives of enterprises. Companies also need models to evaluate water quality risks with water use. Several of the water quality risk-related tools mentioned above have two limitations in terms of water quality risk assessment as their consumer product initiatives. The first limitation is that the data and evaluation ranges of water risks provided by these tools are too rough to consider the actual site conditions and properties of the relevant chemical substances. It is necessary to consider the discharge through domestic drainage and sewage treatment in water quality risk assessment of consumer products. For areas such as Asia, which experience significant precipitation and temperature changes, it is necessary to understand the effect of river flow and fluctuation in biodegradation, in order to understand the concentration fluctuations associated with spatiotemporal changes in the river basin. The second limitation is that these tools cannot evaluate countermeasure effects at a site. Thus, a new simulation tool is required that enables companies to evaluate river water quality and countermeasure effects using actual regional information. Several simulation models that predict river water quality related to chemical substances currently exist. In Europe, the most famous simulation tool is GREAT-ER, published by the University of Osnabrück (Cefic-LRI, 2011; University of Osnabrück; Kehrein et al.,

2015; Price et al., 2009; Koormann et al., 2006), which estimates the river water concentration of chemical substances across countries in Europe. The US EPA has also published tools, such as BASINS (US EPA, 2015) and WASP (US EPA, 2017) for predicting water quality in river basins in the United States. In Japan, two simulation models are available. One is G-CIEMS, developed at the National Institute for Environmental Studies (NIES) (NIES, 2008; Suzuki et al., 2004) to estimate the environmental concentrations of air, water, and sediment. River water and sediment concentrations are calculated for each river with an average length of 5.7 km. All of the models cited above can estimate the spatial chemical concentration at a steady state, such as the annual chemical concentrations, without considering the sewerage situations and treatments in the river basin. The other model in Japan is the National Institute of Advanced Industrial Science and Technology - Standardized Hydrology-based AssessmeNt tool for chemical Exposure Load (AIST-SHANEL) model, which was previously developed by us at the National Institute of Advanced Industrial Science and Technology (AIST, 2015; Ishikawa and Tokai, 2006; Ishikawa et al., 2012, 2017). Nishioka et al. (2018, 2019) applied the model to several surfactants in Japan, and discovered that the estimated surfactant concentrations in river water impose ecological risk. This model is a spatiotemporal simulation model (developed on a PC) that includes essential material dynamic parameters as inputs, such as advection diffusion, settling, resuspension, and biodegradation, for the entire river basin; however, it does not take the atmospheric and oceanic parameters into account. It has more detailed spatiotemporal resolution than any of the models mentioned above, and can estimate the concentrations of chemical substances in river water and sediments, as well as sewage. Domestic and industrial wastewater within sewer service areas flow into sewage treatment plants, where the majority of the chemical substances are removed from the wastewater during the treatment process; however, the remainder is discharged into rivers. This model, which quantitatively estimates the influence of these point and non-point sources on the river water quality, is the only model available to date that can estimate the spatiotemporal changes of chemical substances in river basins experiencing sewage discharge. AIST-SHANEL enables companies to evaluate river water quality and the countermeasures that can be taken to effectively combat water pollution using actual regional information. In this paper, we focus on the surfactant linear alkylbenzene sulphonate (LAS), present in the detergents of health care products, and present water ecological risk assessment case studies conducted for three river basins in Japan with different land-use, population, and sewage characteristics. In addition, we analyze a scenario wherein a company evaluated its water quality risk for a case of chemical substance substitution using the AIST-SHANEL model. The results indicate that this model can be applied to any river basin in the world for water risk assessment. Moreover, it provides useful information for understanding the time and area of the occurrence of the concerned risk, and the subsequent measures that need to be undertaken. The model may also be applicable in the CDP water program. 2. Materials and methodology 2.1. Model description The river model AIST-SHANEL, available since 2015, is an unsteady state analysis model that estimates river flow rate and chemical concentrations in the water and sediment for any river basin in Japan with a gridded spatial resolution of 250 m and a daily temporal resolution. For each 250-m grid, eight water flow directions are defined using the global surface flow direction data of

Y. Ishikawa et al. / Journal of Cleaner Production 239 (2019) 118027

GDBD published by NIES (2007) and Masutomi et al. (2009). First, the model estimates the river flow rate for each grid in the target river basin. Second, it estimates the emission of chemical substances in each grid then their concentrations in the river water and sediment using estimated river flow and emission data, as shown in Fig. 1. The input data for the model are meteorological data such as precipitation and temperature, land-use, population, sewer service area, locations and discharge amounts of sewage treatment plants in the target river basin, and the physical properties and removal rates of the chemical substance in sewage treatment plants. The distribution maps of river flow and chemical concentration are depicted using the free map software Google Earth and ArcGIS Desktop in kml format and the temporal variations are displayed in csv format. This model is applicable to any river basin in the world as long as the necessary watershed data can be obtained. 2.2. Analysis method 2.2.1. Environmental media of the model The model assumes that every grid it contains has the environmental media of the air, river water, river sediment (liquid phase, solid phase), soil (gas phase, liquid phase, solid phase), and drainage channel within one grid, as shown in Fig. 2. It should be noted that the grids may cover any river compartment media, irrespective of whether the actual rivers exist, because this model needs to calculate the continuous upstream to downstream river flow in the river basin. However, the grids characterized by the presence of actual rivers should only be used for risk assessment in the river basin. The drainage channel is a virtual waterway discharged from an emission source into the river with a liquid phase and a solid phase. The soil is divided into four vertical layers, AeD, from the surface layer, with thicknesses of 0.3 m, 1.0 m, 2.5 m, and 10.0 m, respectively. The calculation results of river flow rates and river water and sediment concentrations are outputted for all grids throughout each target river basin, even if no actual river exists. We then obtain the results for target grids containing rivers. 2.2.2. River flow model The river flow model is a one-dimensional hydraulic model that consists of two sub-models describing evapotranspiration, snow accumulation, melting processes, and runoff processes for each grid according to the method of Kawaguchi and Kojiri (2007) and Ishikawa and Tokai (2006).

3

2.2.2.1. Heat balance. Evapotranspiration, snow accumulation, and melting is applied using the numerical energy budget model for heat balance. The energy balance equation for the surface of the Earth is given by equation (1):

RY þ Qa ＝sT 4s þ H þ lE þ G

l ¼ 2:50  106  2400Ta

(1)

where RY is the net radiation, J/s/m2; Q a is the artificial heat release, J/s/m2; s is the Stefan-Boltzmann constant, J/s/m2/K4; Ts is the ground surface temperature, K; H is the sensible heat flux, J/s/ m2; lE is the latent heat flux, J/s/m2; l is the latent heat of vaporization of water, J/kg; E is the amount of evaporation, kg/s/m2; G is the soil heat flux at the surface, J/s/m2; Ts is the air temperature, K. 2.2.2.2. Runoff. As shown in Fig. 3, a kinematic wave model including intermediate flow is applied for the water surface and Alayer using equation (2). The linear storage model is also applied to the B-, C-, and D-layers using equation (3). The kinematic wave model is also applied to river flow:

vh vq þ ¼ r,f vt vx 0

1 a ðhal  da Þmb þ a,hal A @ qal ¼ a,hal

da ¼ la ,Da

a¼

k, sin q

l

a¼

pffiffiffiffiffiffiffiffiffiffi sin q n

ðDarcy TypeÞ;

0

1

 da A when @ < da

ðManning typeÞ;

mb ¼

5 3

(2)

where h is the water depth, m; q is the discharge per unit width, m2/s; r is the precipitation intensity, m/s; f is the runoff coefficient; sin q is the gradient of slope; n is the equivalent roughness of slope, m1/3s; k is the hydraulic conductivity, m/s, l is the porosity; D is the layer thickness, m; d is the maximum water depth, m; subscripts a and b indicate the A- and B-layers; subscript l indicates land-use type of urban areas, field and forest.

dh ¼I  q dt

q ¼ qh þ qv

I ¼ qv þ u

qh ¼ kh,maxðh  Z; 0Þ qv ¼ kv,h 0

Z ¼ Dðl  lw Þ 1

h  dA u¼@ 0

0

1

 0A when h  d @ ; <0

d ¼ l,D

(3)

where I is the inflow, m/s; q is the outflow, m/s; u is the return flow, m/s; kh and kv are the horizontal and vertical related coefficients of target soil, 1/s. lw is the porosity related to runoff.

Fig. 1. Model calculation flowchart.

2.2.3. Emission analysis Discharge flow rates and emission amounts of chemical substances due to human activities in each grid can be estimated using grid data including population and land-use information considering the sewerage penetration rate. The discharge and emission amounts from sewerage area grid are allocated and summed up for each grid containing the municipal sewage treatment plants related to the area. Those in grids with no sewerage are regarded as

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Y. Ishikawa et al. / Journal of Cleaner Production 239 (2019) 118027

Fig. 2. Mechanisms of chemical movement between compartments of each model grid.

Fig. 3. Schematic of the river flow rate calculation in each grid.

onsite discharge and emission amounts for direct input into the river. Point-source emission chemical data are input to the grid corresponding location; i.e. the latitude and longitude of point sources. Non-point-source emission data are calculated using population and land-use grid data. The residual amount of chemicals after removal processes at the sewage treatment plants; i.e. that discharged to the river, is calculated using the removal rate factor. The model estimates onsite emission amounts from no-sewerage areas and from sewage treatment plants after chemical removal processes. 2.2.4. Chemical concentration analysis Chemical concentrations in river water and sediment are then calculated using the estimated river flow rates and emission

amounts. The calculation includes the dynamic mechanisms of advection, diffusion, adsorption/desorption with suspended matter (SS), settling and resuspension with SS, mass transfer between river water and sediment, and biodegradation to predict the dissolved and suspended chemical concentrations in the river and in liquid and solid sediment. Assumed drainage channels in the model are divided into water and sediment for the dynamic analysis, which includes partition equilibrium, biodegradation, and bed-load transport during intense precipitation. The physical parameters for analysis are the vapor pressure, molecular weight, water solubility, organic carbon water partition coefficient (Koc), degradation rate estimated by the halflife of the chemical substance, and sewage treatment removal rate. Wet and dry deposition from the atmosphere can also be included.

Y. Ishikawa et al. / Journal of Cleaner Production 239 (2019) 118027

For grids with no sewerage, onsite emission amounts are inputted into each drainage channel. For sewerage areas, emission amounts from sewage treatment plants are directly inputted into the river. The basic formula for the physical dynamics of the model is shown in equation (4).

  v v v vCw Ar Ar ðAr , Cw Þ þ ðAr , Ur , Cw Þ ¼ Ar , Dr þ Wr þ Gr vt vx vx vx Hr R þ D  K,Ar ,Cw þ f (4) Dr ¼ 5:93Hr ,u* where Ar is the cross section of river water, m2; Cw is the concentration of the chemical substance in the river water, mg/m3; Ur is the cross-sectional mean flow velocity, m/s; Dr is the diffusion coefficient, m2/s; D is the amount of mass transfer between the river water and sediment, mg/m/s; Wr is the settling flux, mg/m2/s; Gr is the resuspension flux, mg/m2/s; Hr is the mean water depth, m; R is the hydraulic radius, m; K is the degradation rate coefficient, 1/s; f is the inflow load to river, mg/m/s; and u* is the friction speed, m/s. The biodegradation rate coefficient, K, is estimated based on the Arrhenius equation considering temperature dependence. The amount of mass transfer between the river water and sediment, D, can be calculated using equation (5):

Ar D ¼  Uw;sdw Hr

Hr Csdw Cw  R Hsdw;w

(5)

Hsdw;w ¼ koc,Msd,rsdw 1 koc,Msd,rsdw

where U is the mass transfer velocity, m/s; Csdw is the chemical concentration in the river sediment, mg/m3; u is the transfer coefficient between compartments, m/s; H is the partition equilibrium coefficient, -; Koc is the organic carbon water partition coefficient, m3/g; Msd is the organic carbon content rate, -; rsdw is the density of the liquid phase in the river sediment, g/m3; w is the index for river water; and sdw is the index for the liquid phase in the river sediment. The material balance of suspended solids (SS) that can adsorb chemical substances is also included in the calculation. The settling flux and resuspension flux of SS, Wss and Gss, are determined by equation (6).

0

1

1

0 0

C B  *2 mss C @ Wss A ¼ B C B u Gss A @ ass 1 u*2 c 1 0 1 w W ,C ss ss ss A¼@ @ A Gss 0

when

u*  u*c

(6)

0

u* ¼

when

friction speed, m/s; uc* is the limiting friction speed, m/s; g is the gravitational acceleration, m/s2; Ie is the hydraulic gradient, -;ass is the resuspension rate coefficient from river sediment, g/m2/s; mss is the resuspension constant from river sediment, and -; wss is the settling rate of SS in river water, m/s. For a drainage channel, partition equilibrium between dissolved and suspended chemical substances, biodegradation, outflow, and sedimentation process of wastewater are considered in the calculation. In the sedimentation process, it is assumed that chemical substances in the sediment are transported with flooding; thus, loading amounts from the drainage channel to the river are calculated by equation (7).

frnp ¼ kwnp Snp

qal A

(7)

 dSnp  ¼ 1  fp xmp  frnp  Knp Snp dt where fp is the partition equilibrium coefficient between water and sediment in the drainage channel, -; xmp is the onsite emission amount, mg/s; frnp is the flow loading amount transported with flooding from the drainage channel to the river, mg/s; Snp is the sedimentation load, mg; kwnp is the traction coefficient (the bedload transport coefficient), 1/m; qal is the flow rate, m3/s; A is the catchment area, m2; and Knp is the biodegradation rate, 1/s.

!

Hw;sdw 1 1 ¼ þ Uw;sdw uw usdw

Hw;sdw ¼

5

u*  u*c

pffiffiffiffiffiffiffiffiffiffiffiffiffiffi g,R,Ie

where Css is the SS concentration in river water, g/m3; u* is the

2.3. Validation and quality control The validity of AIST-SHANEL has already been verified by Ishikawa and Tokai (2006) and Ishikawa et al. (2012, 2017) using the rivers in Japan. They indicated that the accuracy of this model depends on the accuracy of the amount of emissions, chemical behavior of sewage during degradation and adsorption processes, and sewage treatment removal rate. At present, these input data and parameters can only be determined from the information available in the related research papers, thus causing uncertainty in their estimation. Sewage treatment removal rate generally differs depending on the treatment method adopted at each sewage treatment plant. For example, the estimated concentrations of LAS in rivers differed by more than an order of magnitude when Japan's sewage treatment removal rates were varied from 90% to 99.9%. In this study, we evaluated the validity of this model with more recent data. For 2015, more data could be obtained than that reported in previous studies on the river flow and the LAS concentration in the water from Tama River, a typical urban river in Japan. By comparing their estimated values with the corresponding observed values on a daily basis, we derived a criterion for the validity of this model. Based on this criterion, if the ratio of the estimated value to the observed value lay between 0.1 and 10 (factor 10), the estimated value would be considered reliable. Fig. 4 compares the daily river flow values calculated by the AIST-SHANEL model in 2015 and the corresponding observations made at three observation sites on Tama River (Hino Bridge, Houon Bridge, and Ishihara) by MLIT (2015). For more than 95% of the total of 1095 samples, the ratios of the estimated values to the observed values were within a factor of 10. Therefore, we concluded that the estimated river flow rates were valid. Fig. 5 compares the daily river water concentrations of LAS estimated by the model with those of the observed values once per month or once every two or 3 months at seven monitoring sites on the main river and 17 sites on the tributaries of Tama River in 2015 (Bureau of Environment Tokyo Metropolitan Government, 2015). For 86% of the total of 202 samples, the ratios of the estimated values to the observed values were within a factor of 10, suggesting

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Y. Ishikawa et al. / Journal of Cleaner Production 239 (2019) 118027

3. Case study

Fig. 4. Comparison of river flow rates calculated by the AIST-SHANEL model and observations made by MLIT (2015) at the three observation sites on Tama River.

In this section, we present a case study that demonstrates the applicability of the AIST-SHANEL model for water risk assessment of chemical substances. More specifically, the model was used to obtain information on various river basins for assessing the chemical ecological risk in the use of consumer products. We selected three river basins in Japan with different population, landuse, and sewerage characteristics to assess the ecological risk of LAS as an indicator of consumer products. LAS is a typical surfactant contained in household and industrial cleaning agents and is widely used in a variety of detergent formulations, including laundry powers and liquids, dishwashing liquids, car washes, and hard-surface cleaners. LAS can affect aquatic life; in Japan, this fact is also stated in the environmental standards for conservation of aquatic life, determined by Japanese basic environmental law in 2013. LAS is a water-soluble, organic, and easily biodegradable chemical substance that can advect and diffuse in river water. We calculated the river flow rates and LAS river and sediment concentrations as a case study in 2011; the average annual temperature was normal, and the annual rainfall was relatively normal in the east and high in the west of Japan. 3.1. Study area

Fig. 5. Comparison of LAS river water concentrations calculated by the AIST-SHANEL model and observations at seven sites on the main river and 17 sites on tributaries of Tama River in 2015 by Bureau of Environment Tokyo Metropolitan Government (2015). Observed data below the detection limit of 0.006 mg/L of LAS are shown assuming their half value, 0.003 mg/L.

that the estimated concentrations of LAS in the river were valid. These validities were evaluated in the absence of any precipitation, because the samplings were performed on a sunny day. The AISTSHANEL model can estimate the chemical concentrations even during rainfall; however, the estimated concentrations vary significantly because of precipitation, depending on the river flow. We cannot verify those estimated concentrations, because at present, the amount of available data collected on rainy days is small. We did not verify the river sediment concentrations estimated by this model in this study, because we did not verify its validity owing to a lack of observation data. Although the accuracy of the estimated river sediment concentrations cannot be verified, we will use the LAS sediment concentrations estimated in this case study to discuss the variations of LAS concentration in river water.

Three river basins were selected: Tama River, Nikko River, and None River (Fig. 6), each with different population, land-use, and sewage coverage characteristics. Fig. 7 shows the land-use area ratio of the three river basins. As no data were available for the sewage coverage by river basin, we estimated the sewage data for each grid using the sewage treatment population by municipality, assuming that a larger population would indicate more sewers. We regarded the onsite sewage discharge rate and sewage discharge data included in the model as the sewage coverage (Table 1). Tama River has a catchment area of 1,240 km2, which includes Tokyo, Kanagawa, and Yamanashi prefectures. This river is a very famous urban river with over 4 million people living in the basin; 60.6% of the basin is forest area located in the upper stream area, 33.2% is urban land, with large populations and factories in the middle and downstream areas, and 3.5% is field land. The sewage coverage is almost 60% for the entire basin and nearly 100% in the downstream area. Nikko River is located in Aichi prefecture with a catchment area of 295 km2. The basin area had a population of approximately 830,000 people in 2008 and many manufacturing factories. The sewage coverage is very low across the basin at only 2%; therefore, the majority of drainage in the area flows directly into the river without passing through a sewer. 56% of the river basin is urban land with factory zones and 33% is paddy field land. None River has a basin area in Tokushima prefecture 94 km2, with a population of thousands of people. Approximately 95% of the basin is occupied by forest land. The city area is less than 1% and the sewage coverage is approximately zero over the entire river basin. 3.2. Emission amounts and chemical properties of LAS This case study assumed that LAS would be used mainly as a household detergent and an industrial detergent for washing the interiors of buildings. The emission amounts of LAS were assumed to be equal to the non-point-source emission data of LAS for detergents obtained by the PRTR system in Japan in 2011 (MOE, 2013). Table 2 shows these non-point-source emission data from households, buildings, and inflow amounts to sewer systems for each municipality related to the three river basinseTokyo, Kanagawa, and Yamanashi prefectures in Tama River; Aichi prefecture in Nikko

Y. Ishikawa et al. / Journal of Cleaner Production 239 (2019) 118027

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Fig. 6. The three river basins selected for the case study.

Fig. 7. Land-use area ratios of the target river basins.

Table 1 Onsite discharge, sewage discharge, and sewage discharge ratios for the three river basins.

Tama River Nikko River None River

Onsite discharge (A) (m3/day)

Sewage discharge (B) (m3/day)

B/(AþB) ratio

749,427 3,689,356 3,760

1,076,290 91,861 0

0.59 0.02 0.00

River; and Tokushima prefecture in None River. The LAS non-point emission amounts from household detergents were allocated in proportion to the related population, without sewerage area grid data included in the model. Those from the industrial detergents in the building sites were allocated in proportion to the related building site, without sewerage area grid data. The LAS inflow amounts to the sewerage system were allocated in proportion to the grid related to the municipal sewage treatment plants according to the treated water volume, considering the sewage coverage. Table 3 shows the onsite emission amounts, amounts transferred to the sewer system, amounts discharged from sewage

treatment plants to rivers considering a removal rate of 99% in Table 4, and the total amount of LAS discharged to the river estimated by this model. The total emission amounts of LAS were 646 ton/year in Tama River, 2,033 ton/year for Nikko River, and 3.5 ton/ year for None River. LAS emission was largest in Nikko River and predominantly comprised onsite emissions. In None River, emissions were 2e3 orders of magnitude smaller than in the other two rivers. As shown in Fig. 8, Tama River had five sewage treatment plants discharging more than 1,000 kg/year directly into Tama River from midstream to downstream. The sewerage penetration rate was higher (100%) in the downstream region of Tama River according to the sewerage statistics for Japan in FY 2011. However, onsite emissions of over 1,000 kg/year, that reached the river by means other than the sewer, existed in the midstream area of Tama River. In Nikko River, onsite emissions of over 100 kg/year were distributed throughout the basin. Three sewage treatment plants discharged directly into Nikko River, from a maximum of 349 kg/ year at the furthest upstream region to 55 kg/year in the midstream region. None River had no sewerage and all onsite emissions were less than 100 kg/year in the downstream area. The chemical properties of LAS used as input parameters are summarized in Table 4. The half-life in river water is 0.75 days, which indicates that it is easily degradable. The half-life in river sediment and soil is 22 days, indicating that LAS is more persistent in these media. The organic carbon water partition coefficient (Koc) is 2,500 (L/kg), which indicates that LAS is slightly more absorbable in sediment and soil. 3.3. Results and discussion 3.3.1. LAS river concentrations Fig. 9 shows the distribution map of LAS concentration in the

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Table 2 LAS non-point-source emission data by prefecture in target river basins according to its different uses. River

Prefecture

Use of consumer products Household detergent

Business detergent,

Inflow amounts to sewer system

Allocation indicator Population

Building site

Sewage treatment plant

Tama River

Tokyo Kanagawa Yamanashi

73,438 146,789 89,245

14,383 16,952 13,255

5,641,124 3,131,397 154,787

Nikko River None River

Aichi Tokushima

532,892 141,343

92,437 18,387

1,705,333 30,634

Unit: kg/year.

Table 3 Emission amounts of LAS for the three river basins in this case study.

Tama River Nikko River None River

Onsite emission

Inflow amounts to sewer system

Emission amounts from sewage treatment plants to the river

Total emission amounts into the river

633,796 2,032,193 3,493

1,214,997 69,380 0

12,150 694 0

645,946 2,032,886 3,493

Unit: kg/year.

low flow state in the three river basins, as well as the daily variation of LAS in the river water and solid sediment volume-based concentration and river flow rate for the most downstream grid and the grid with the maximum estimated LAS concentration. The corresponding daily temperatures necessary for consideration in Tokyo (for Tama River), Nagoya (for Nikko River), and Tokushima (for None River) are shown in Fig. 10. In Tama River, the concentration was higher in the midstream than in the downstream area, where the sewage coverage was higher, resulting in smaller onsite emissions of LAS discharged into the river. Therefore, the chemical substances discharged to Tama River from the midstream were diluted with increasing flow rate downstream. The daily variations of LAS river concentration in the most downstream grid indicated that the river water concentration varied from 2.0 to 4.0 mg/m3 in January, February, and December (winter in Japan) and was less than 1.0 mg/m3 in June, July, and August (summer in Japan). The solid sediment concentration of LAS varied in the range 60e100 mg/m3 in winter and 5e10 mg/m3 in summer, with mild seasonal variation. The river concentration at the grid with the maximum estimated LAS concentration in the flow state varied from 25.0 to 60.0 mg/m3 in winter and was less than 20.0 mg/m3 in summer. The solid sediment concentration of LAS varied in the range 1,000e2,000 mg/m3 in winter and 200e300 mg/m3 in summer, with mild seasonal variation. The differences in LAS river concentration between the two sites were mainly attributed to dilution related to the river flow rate. At the most downstream grid, the flow rates were less than 10 m3/s in winter, with occasional peaks of more than 500 m3/s during the

Table 4 Chemical parameters of LAS inputted into the model (Ishikawa et al., 2012). Parameter

Value

Vapor pressure Molecular weight Water solubility Koc Half-life in surface water Half-life in sediment Half-life in soil Removal rate of sewage

3.05  1013 342.4 2.5  105 2,500 0.75 22 14 0.99

Pa g/mol g/m3 l/kg day day day e

rainy season in summer and a typhoon in September. In contrast, at the grid with the maximum LAS concentration, the rates were less than 1 m3/s in winter, with several peaks of over 5 m3/s in summer and September. Another reason for this seasonal variation was biodegradation linked to river water temperature, which was estimated by the model using a correlation equation between air and river water temperature. In Tokyo, the air temperature was 0e10  C in winter and 20e30  C in summer. In Nikko River, the concentration distribution of LAS was approximately uniform and higher throughout the basin than in Tama River. These concentrations were attributed to the onsite emission amounts due to a very low sewage coverage rate. The daily variations of LAS river concentration at the most downstream grid varied from 10.0 to 50.0 mg/m3 in winter and approximately 5.0e20.0 mg/m3 in summer. The solid sediment concentration of LAS varied in the range 2,500e5,000 mg/m3 in winter and 1,000e2,000 mg/m3 in summer, with mild seasonal variation. There was also a frequent decrease in the concentration at a high flow rate, which was probably due to the resuspension of SS with flooding. The LAS river concentration at the maximum concentration grid was in the range 20.0e100.0 mg/m3 in winter and 10.0e40.0 mg/m3 in summer. The solid sediment concentration of LAS varied in the range 5,000e10,000 mg/m3 in winter and 2,000e5,000 mg/m3 in summer with mild seasonal variation. As before, there was a frequent decrease in the concentration at a high flow rate. Again, differences between the two sites were mainly attributed to dilution. At the most downstream grid, the flow rates were less than 10 m3/s in winter, with occasional peaks of more than 200 m3/s in summer and September, whereas at the grid with the maximum LAS concentration, the rates were less than 1 m3/s in winter, with some peaks of over 5 m3/s in summer and September. The LAS river water concentrations exhibited no clear seasonal variations, unlike Tama River, despite the air temperature being less than 5  C in winter and 20e30  C in summer. The peaks of LAS river water concentration appear to agree with the flow rates at both sites, contrary to Tama River. This means that LAS concentration in the river water increased as the chemical settled in the drainage channel around onsite emission points flowing into the river with rainfall. None River is located in mountainous areas with no sewerage.

Y. Ishikawa et al. / Journal of Cleaner Production 239 (2019) 118027

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Fig. 8. Distribution map of LAS emissions in (a) Tama, (b) Nikko, and (c) None Rivers. Colors on the map represent onsite emission levels and red marks with white numbers represent emissions from sewage treatment plants after removal. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

The LAS river concentrations were approximately 1/10th and 1/ 100th those of Tama River and Nikko River, respectively. The concentration distribution map of LAS was similar to the distribution of emissions from residential and building areas along None River (Fig. 8). The LAS river water concentration was highest at the most downstream grid throughout the river basin. The daily variations of LAS river water concentration for the most downstream grid ranged from 0.1 to 0.2 mg/m3 in winter and approximately 0.05e0.1 mg/m3 in summer. The solid sediment concentration of LAS varied in the range 20e35 mg/m3 in winter and 10e20 mg/m3 in summer, with mild seasonal variation, and there was a slight decrease in the concentration at a high flow rate. Although the flow rates were less than 10 m3/s in winter, with occasional peaks of more than 100 m3/s in summer and September and the air temperature were less than 5  C in winter and 20e30  C in summer, the LAS river water concentrations exhibited minimal seasonal variation, similar to Nikko River. The effects of dilution and biodegradation on LAS river concentration were minimal. Fig. 11 shows the changes in river water concentration of LAS from the most upstream to the most downstream area in each of the three rivers in their low flow state. In each figure, rapid increases in LAS concentrations on each main river are numbered one to five, which corresponds to their location in Fig. 9. Essentially, LAS concentration in river water is determined by dilution plus discharge and emission amounts into rivers. If emission amounts were approximately the same, river water concentrations would decrease toward the downstream region due to dilution. In the

main Tama River, LAS concentration twice increased rapidly (1,2) immediately after joining the tributaries that discharged more onsite LAS then decreased gradually toward the downstream. In the main Nikko River, as onsite discharge amounts were distributed throughout the river basin with minimal variation, a rapid increase in LAS concentration was observed near the upstream area (3) before gradually decreasing due to dilution. In None River, two rapid increases (4,5) in LAS concentration occurred around the downstream area, which had higher onsite emissions than the upstream area (Fig. 9). 3.3.2. Risk assessment for aquatic organisms in the river basins In this study, we performed general environmental risk assessment in river basins for down-the-drain chemicals (Grill et al., 2016; Zhang et al., 2015). Predicted no-effect concentration (PNEC) is the concentration of any substance in an environment below which adverse effects are unlikely to occur for either longterm or short-term exposure. It was compared to the 95th percentile of the concentration of pollutant in river water, predicted environmental concentration (PEC), to determine whether the risk of that pollutant is acceptable; for PEC/PNECs<1, the risk is acceptable. Several studies on the ecological risk assessment of LAS (HERA, 2013; Kilgallon et al., 2017; Cowan-Ellsberry et al., 2014; Hampel et al., 2012; McDonough et al., 2016; Sakai et al., 2017) have been conducted worldwide. Ecological risk was evaluated according to the influence on aquatic organisms using the results of river water concentration

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Fig. 9. River water concentration distribution maps for (a) Tama, (b) Nikko, and (c) None River basins in their low flow state. The daily variations of LAS river water, sediment concentrations, and river flow rate at the most downstream grid and the maximum LAS concentration grid are shown in red, purple, and blue, respectively. White numbers represent the position of sharp increases in LAS concentration in Fig. 11. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Y. Ishikawa et al. / Journal of Cleaner Production 239 (2019) 118027

Fig. 9. (continued).

11

12

Y. Ishikawa et al. / Journal of Cleaner Production 239 (2019) 118027

Fig. 9. (continued).

estimated by AIST-SHANEL. The PNEC of LAS below which no adverse effects on aquatic organisms are expected was assumed to be 270 mg/m3 (HERA, 2013). If the PEC exceeded this PNEC at the evaluation site in each river, we judged that there was an ecological risk. Fig. 12 shows the range of LAS river water concentrations at the most downstream grid and the maximum concentration grid in the low flow state estimated by the model using box plots to show the minimum, 5th percentile, median, 95th percentile, and maximum values. The PEC values at the most downstream and maximum concentration grids in the three river basins were 4.5 and 51.2 mg/ m3 for Tama River, 30 and 58.6 mg/m3 for Nikko River, and 0.15 mg/ m3 for None River. Neither of the PECs nor maximum values at these grids exceeded the PNEC, indicating that there was no ecological risk over the year due to LAS for any of the target river basins. 3.3.3. Scenario analysis Nikko River was found to have the highest concentration of LAS

in its waters (see Section 3.3.2). As a scenario analysis for corporate self-management measures, we analyzed the AE concentrations in river water using the AIST-SHANEL model, and estimated the ecological risk with PEC/PNEC ratio this river when all the LAS of the surfactant contained in the detergent was replaced with highly biodegradable AE; the latter is a relatively recent commonly used non-ionic surfactant that is used as a cleaning agent. The chemical parameters of AE were as follows: vapor pressure, 4.40  1014 Pa; molecular weight, 582.8 g/mol; water solubility, 1.6  103 g/m3; Koc, 6,570 l/kg; half-life in surface water 0.3 days; removal rate in sewage treatment plant, 0.998 (Nishioka et al., 2018). AE has a much higher biodegradation rate in river water, over two times that of sewage and approximately five times that of LAS. The PEC values of AE in river water were 0.38 and 0.86 at the most downstream and maximum concentration grids, respectively, in Nikko River in this scenario. For ecological risk assessment, PEC/PNEC ratios of LAS at the most downstream grid and the maximum concentration grid were

Y. Ishikawa et al. / Journal of Cleaner Production 239 (2019) 118027

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Fig. 10. Temporal variations of air temperature (JMA, 2011). (Shown in blue for Tama River, Tokyo; orange for Nikko River, Nagoya; and green for None River, Tokushima.). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

compared with those of AE using the PNEC value of 110 mg/m3 (JSIA) (Fig. 13). The PEC/PNEC ratios of LAS were 0.11 and 0.22 for the most downstream and the maximum concentration grids, respectively; while for AE, they were 0.003 and 0.008, respectively, which suggested that the ecological risk was considerably lowered when LAS was replaced with AE as a surfactant in consumer detergents. 4. Conclusion The AIST-SHANEL model, which was developed by us in a previous study, is applicable to chemical risk assessment in any river basin in the world. The model is an unsteady analysis model that assesses the influence of point/non-point emission of chemical substances on river water quality for water risk assessment. The model can estimate spatial and temporal river flow rates and chemical concentrations throughout the target basin by using watershed characteristic data such as meteorological, land-use, population, industry, and sewerage data. In this study, three case studies were conducted. They involved estimating river flow rates and concentrations of the surfactant LAS, then performing an ecological risk assessment according to the PNEC in three river basins in Japan with different watershed characteristics; namely, Tama River, an urban area with a large sewer area, Nikko River for urban area with less sewer area, and None River for nonurban area with no sewers. The results obtained suggest that the LAS river concentrations depend on variations in their emission amounts, sewerage, river flow rates, and biodegradation. In Tama River, the LAS river water concentrations were strongly affected by seasonal variations in temperature and flow rate associated with dilution. Their concentrations in Nikko River, located in a smaller sewage area, were strongly affected by transport from the drainage channel with flooding and showed little seasonal variations due to temperaturerelated biodegradation. In None River, with no sewage area, the LAS river concentrations remained practically unaffected by dilution and seasonal changes in temperature. The river sediment concentrations of LAS showed seasonal variations. Watershed characteristics also influenced the river concentrations of LAS; therefore, companies need to assess both the spatial and temporal risks posed by these chemicals in river basins using the AIST-SHANEL model. In this study, we showed that by applying the developed model to various river basins, we could not only estimate the spatiotemporal concentrations of chemical substances for ecological risk

Fig. 11. River water concentration changes of LAS from the most upstream to the most downstream area in (a) Tama, (b) Nikko, and (c) None Rivers in their low flow state.

assessment, but also analyze new findings showing that the behaviors of chemical substances differ depending on weather conditions and sewage coverage. Although the present study targeted river basins in Japan, AIST-SHANEL can be applied to any river basin in the world using global watershed data such as that of population, land-use, sewage coverage, and meteorological data. Recently, global companies are being required to perform water risk assessment for international environmental awareness and their CSR. When a health care or daily-use consumer products manufacturing company is expanding locally and internationally, the impact of chemical substances of concern for toxicity contained in household wastewater on a target river basin should be assessed and a guarantee that the risk would be low across river basins ascertained. However, lack of exposure data of river water and sediment chemical concentrations in many unfamiliar watersheds where the company's product(s) would be used may make such risk assessment difficult. In such cases, this model can be used to predict the chemical concentrations on various river basins regardless of

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the concept of water risk assessment or context-based water targets (CBWTs) discussed by CEO Water Mandate (2017) considering natural and social features. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Acknowledgements We would like to offer our special thanks to Dr. Higashino. Without his encouragement and support, this paper would not have materialized. Fig. 12. Estimated range of river water LAS concentrations at the most downstream and highest LAS concentration grids for the three rivers. The PNEC of LAS is indicated by the red line. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 13. Estimated range of river water LAS and AE concentrations at the most downstream and highest LAS concentration grids for the three rivers. The PNEC of both compounds are shown by the red line. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

country. This facilitates evaluation of the future impacts of chemicals due to changes in physical properties for alternative or new chemical substances, climate change, emission reductions, water saving measures, and improvements to wastewater treatment facilities, including sewerage. In addition, this model will help manufacturers of such consumer products to show spatiotemporal changes in chemical concentrations across the watershed estimated by this model and the risk of their own product(s) to residents, easily and conveniently for risk communication. In conclusion, we proved that using the AIST-SHANEL model, consumer product manufacturing companies can evaluate the environmental risk in any river basin for present as well as future scenarios. The model can also be applied when companies plan to expand their business into other areas and overseas that have different watershed characteristics. This would lead to risk reduction in the environment, due to the lower concentrations of chemicals discharged into the rivers and/or the use of alternative chemical substance(s) and by undertaking their own wastewater treatment measures and/or administrative measures of wastewater treatment with regard to sewage area and removal if necessary. This suggests that such a risk assessment tool will be useful in addressing the CSR. Overall, AIST-SHANEL is expected to be a useful evaluation tool for overseas business development and water risk assessment and management programs such as the CDP water program. The targets of future studies will be determined based on

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