Risk assessment framework for nitrate contamination in groundwater for regional management

Science of the Total Environment 697 (2019) 134102

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Risk assessment framework for nitrate contamination in groundwater for regional management YanGuo Teng a, Rui Zuo a, Yanna Xiong b, Jin Wu c,d,⁎, YuanZheng Zhai a, Jie Su a a

College of Water Sciences, Beijing Normal University, Beijing 100875, China Solid Waste and Chemicals Management Center, Ministry of Ecology and Environment of the People's Republic of China, Beijing 100029, China College of Architecture and Civil Engineering, Beijing University of Technology, 100124, China d Beijing Key Laboratory of Urban Hydrological Cycle and Sponge City Technology, Beijing 100875, China b c

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T

• A new framework for groundwater risk screening and assessment was proposed. • The optimal method for groundwater risk assessment was discussed. • Risk screening was applied for groundwater management at regional scale in China. • Combining different risk assessment methods was a useful approach for systematic management of groundwater quantity.

a r t i c l e

i n f o

Article history: Received 10 June 2019 Received in revised form 23 August 2019 Accepted 24 August 2019 Available online 29 August 2019 Keywords: Groundwater Risk assessment Nitrate, risk screening and assessment China

a b s t r a c t Nitrate pollution in groundwater is now one of the most important environmental problems all over the world. For this purpose, a new framework for risk screening and assessment of groundwater nitrate was proposed according to source-pathway-receptor-response model to provide basic for defining environmental management strategies. The framework is composed of groundwater relative risk model (RRM), groundwater contamination risk assessment (CRA), and human health risk assessment (HHRA). The framework is applied in the lower Liaohe river basin plain, northeast of China. The results showed that the priority area with high groundwater relative risk in study area was successfully screened by RRM. Furthermore, the sites with high human health risk for public by groundwater nitrate were selected as hazardous areas. This framework promotes systematic integration of risk assessment of groundwater nitrate and expands traditional research on groundwater management from a scale-based approach to crucial insights into pollution. © 2019 Elsevier B.V. All rights reserved.

1. Introduction

⁎ Corresponding author at: College of Architecture and Civil Engineering, Beijing University of Technology, 100124, China. E-mail address: [email protected] (J. Wu).

https://doi.org/10.1016/j.scitotenv.2019.134102 0048-9697/© 2019 Elsevier B.V. All rights reserved.

Groundwater is a valuable resource for the existence of mankind as people across the globe use it for various activities like consumption, irrigation and industrial use (Fang et al., 2018; Huan et al., 2018). Its contamination has always been a big concern among the researchers,

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government agencies and environmental organizations in the recent years (Wang et al., 2018). Groundwater contamination adversely affects the human health, as well as threatens ecosystems through energy and material cycling (Qiu et al., 2018). Groundwater pollution risk assessment, which refers to the process of determining the potential impacts of any pollutant, is an effective tool for designing efficient groundwater management and protection strategies (Shrestha et al., 2016; Wang et al., 2019; Ali et al., 2019). Groundwater provides about 20% of total water's supplies for China, and 50–80% of water in water-scarce north and northwest regions of the country (Qiu, 2011). According to the latest round of groundwater investigation by China's Ministry of Land and Resources, N55% of samples were classified as category IV or V (on a scale of I to V from the best to poorest quality) (Zheng and Liu 2013). It is important to evaluate groundwater risk for drinking water and human health (Li et al., 2018a; Li et al., 2018b). The research on groundwater vulnerability and contamination risk (GCVR) in China began in mid-1990s (Zeng et al., 1998). During this period, most of groundwater vulnerability research were limited in intrinsic vulnerability of water source area, and mainly referred to foreign studies (Aller et al., 1987; Evans and Myers, 1990; Villeneuve et al., 1990; Merchant, 1994). The representative researches areas of groundwater vulnerability assessment includes Hebei plain, Daqing city, Tangshan city. In the last 10 years, with rapid development of 3S technology (Remote Sensing, RS; Geographical information System, GIS; Global Positioning System, GPS), the groundwater pollution risk assessment can be calculated and presented by GIS. Many researches about groundwater contamination risk assessment and human health risk assessment have been carried out in China (Liu et al., 2016; Xiao et al., 2019). During this period, China has been catching-up the practice of GCVR compared to some other countries. Most of studies focused on (1) nitrate pollution from agricultural activities; (2) urban area impacted by anthropogenic activities; (3) hazardous solid waste landfills; and (4) contaminated sites by industrial activities. As a part of this process, the implementation strategies of GCVR have been established and have since become a practical tool for Chinese environmental authorities. In spite of a large number of groundwater pollution investigation and evaluation in China were conducted (Qiu, 2011), the systematic theory and method of groundwater risk assessment have not been established. In order for GCVR to reach its full potential, significant work remains to be completed. The most immediate challenge facing GCVR in China involves optimization of assessment method but also effective combination of vulnerability risk and contamination risk. In this case, a framework that can be adjusted based on different management goals for groundwater risk assessment needs to be developed in China. These recognize the important role played by GCVR in providing safe and sustainable groundwater. It is necessary and significant to establish the framework of groundwater risk assessment to obtain risk identifying, screening, assessing, classifying, and ranking for groundwater management in China Therefore, the objective of this study was to develop groundwater risk screening and assessing method from basin to site scale in China.

2. Materials and methods

assess risks at different scale, which meet the requirements of different scales of risk management (Fig. 1). 2.2. Risk screening procedure based assessment According to the framework, the procedure of the groundwater risk screening and assessment characterized stepwise screening, grading and classification evaluation. The screening accuracy of this procedure is from extensive to elaborate process, and the assessment scale of this procedure is from large to small. At the large scale the environmental risk assessment of groundwater is suitable. Aim to the high-risk areas delineated by environmental risk assessment of groundwater, the groundwater contamination risk assessment can be used to monitor and regulate potential contamination sources (CMEP, 2011) and to protect water source area. Object to the potential pollution sources identified by groundwater contamination risk assessment, the human health risk assessment can be used at the small scale. 2.3. Risk screening and assessment method 2.3.1. Groundwater relative risk assessment at basin scale The relative risk model (RRM) was developed in order to integrate the impacts due to a variety of contamination at a regional scale, and it has been successful in multiple diverse settings, including marine ecosystems and inland watersheds. When the RRM model is used to evaluate the groundwater relative risk, sources of risk in the region and receptors should be included in the pressure assessment and analysis (Lannuzzi et al., 2010). It is important to focus on the water environment at the basin scales because many cumulative effects are especially apparent at this scale (Faggiano et al., 2010). In addition, interpretation should be based on the following assumptions (Chen et al., 2012; Landis and Wieger, 1997; Landis, 2004; Han et al., 2018): (1) The sensitivity of risk receptors to stressors varies with receptor type, and the receptor sensitivity is positively correlated with the response to stress; (2) There is a positive correlation between frequency of the risk source and the release pressure; a higher density of Receptors linked to the endpoint will result in a greater likelihood of exposure to pressure; (3) Effects on groundwater risk endpoint and effect in the same area of the multiple risk pressure can be superimposed according to its relative level of risk. The RRM model was used to evaluate the regional groundwater relative risk by using the following formula (Eqs. (1) and (2)). Rv ¼

X

Si  SSCin  DI  Rm  SECml

RRv ¼ Rv =Rvmax

ð1Þ ð2Þ

here, Rv is the risk value; RRv is the relative risk value; i is the source series; n is the stressors series; m represents the receptor series; l represents the endpoint series; Rm is the receptor rank in sub-regions; Si is the relative risk source value in sub-regions; DI is the relative groundwater vulnerability index; SSCn is the source–stressor–receptor exposure coefficient in sub-regions; SECml is the stressor-endpoint response coefficient in sub-regions. DI, the relative GVI is:

2.1. Framework of groundwater risk screening and assessment DI ¼ GVI=GVImax A framework for groundwater risk screening and assessment was proposed according to a source-pathway-receptor-response for the description of regional, local or site groundwater contamination processes. This groundwater risk screening and assessing framework consists of three levels (Fig. S1): (1) environmental risk assessment at basin or regional scale; (2) contaminated risk assessment at urban or industrial park scale; and (3) human health risk assessment at site scale. This framework can screen risks from large scale to site scale gradually and

ð3Þ

where groundwater vulnerability index (GVI) is the final assessment result. where GVI is the vulnerability index based on DRASTIC model. The DRASTIC model is composed of seven hydrogeologic indexes: depth to water table (D), aquifer net recharge (R), aquifer media (A), soil type (S), topography (T), impact of the vadose zone (I), and hydraulic conductivity (C). Each of these hydrogeologic factors is assigned a rating

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(1−10) depending on its important and a weighting (1–5) depending on its influence on vulnerability. The DRASTIC index is calculated applying a linear combination of all the factors as follows (Chitsazan and Akhtari, 2009): GVI ¼ Dr Dw þ Rr Rw þ Ar Aw þ Sr Sw þ Tr Tw þ Ir Iw þ Cr Cw

3

this manner, the variables are in effect being multiplied, as is the calculation of human health risk assessments. Risk is calculated as follows (U.S. EPA, 1989): L ¼ L1 þ L2

ð5Þ

S¼Q þAþT

ð6Þ

R ¼LþS

ð7Þ

ð4Þ

The higher the GVI number, greater is the groundwater vulnerability. Groundwater vulnerability mapping can be graded into several classifications indicating comparable pollution possibility of groundwater. The Relative Risk Value (RRv) is normalized by dividing by the maximum value. The groundwater risk was divided into five ranks from Relative Risk Rank (RRR) I to V according to the RRV [0–0.2], [0.2–0.4], [0.4–0.6], [0.6–0.8], [0.8–1], these different ranks correspond to lowest, low, medium, high, and highest risk levels. The Analytic Hierarchy Process (AHP) developed by Wind and Saaty (1980) was used to compute the ratings and weight of all parameters used in the modified DRASTIC method. The pairwise-comparison matrix was prepared for the six parameters used in the modified DRASTIC method and new rating coefficients were calculated for each parameter. The weights of specific criteria are established by ranking their importance and suitability (Vasiljević et al., 2012). Factor weights are determined by a pair-wise comparison matrix (Saaty, 2001). In construction of a pair-wise comparison matrix, each factor is rated against every other factor by assigning a relative importance value between 1 and 9 to the intersecting cell. When the factor on the vertical axis is more important than the factor on the horizontal axis, this value varies between 1 and 9. Conversely, the value varies between 1/2 and 1/9. 2.3.2. Groundwater contamination risk assessment at urban scale The groundwater contamination risk assessment is based on the origin-pathway-target model, which has an origin equivalent to the potential source of contamination resulting from human activities primarily taking place at the land surface (EPA, 1991). The usual index method of groundwater pollution risk assessment integrates the mapping of intrinsic vulnerability and hazards by virtue of overlay method, such as cross-table, matrix relationship and hazard ranking system. Besides, researchers have added other factors, such as groundwater value, well capture zone and specific geological settings into the risk assessment methods based on different perspectives (Harman et al., 2001). In general, groundwater contamination assessment combines the contamination source load, intrinsic vulnerability, and the groundwater value to determine risk at a regional scale. (1) Contamination source load: The contaminant sources could include leakage of toxic and hazardous substances, production plants, equipment, surface water bodies, and soil cover that could threaten the groundwater safety. The characteristics, positions, contents, and retention periods of the sources will affect the level of groundwater contamination risk. Because one contamination source could release more than one contaminant simultaneously and may contain many types of contaminants, it is difficult to quantify and compare the risk between different contamination sources. The U.S. EPA Priority Setting Approach is a risk screening tool designed to help local officials assess and rank the relative threats to groundwater. Potential contamination sources (PCSs) can be divided into the following major source categories: agrichemical applications, container storage and material transfer, injection wells, land treatment, landfills, material transport, pipelines, septic tank systems, storage piles, surface impoundments, and tanks (Harman et al. Saaty, 2001). This method is established for wellhead protection areas (WHPA). The risk score is calculated for each PCS within a WHPA by multiplying two risk components, (1) the likelihood (L) of the well becoming contaminated and (2) the severity (S) of well contamination. In the EPA methodology, the natural numbers in the scoring system are converted to decimal logarithms to allow calculation of Risk values (R) through summation. Therefore, when users are adding variables in

L1 = likelihood of a contaminant release from the source L2 = likelihood that the contaminant reaches the well Q = quantity released from the source A = attenuation of the contaminant due to transport T = toxicity of the contaminant The priority setting approach is an integral “origin-pathway-target” model of risk assessment used for wells. This study uses the model of risk assessment for the whole regional groundwater system. The objects and targets will be changed when the scale of risk assessment changed. It is observed that main meaning of parameter L2 and A were on account of certain well. In addition, it is difficult to collect such representative parameter values in regional. Therefore, a modified priority setting approach in which only the characteristics of contamination (T and Q) and the protective measures for potential sources (L1) were considered was applied to quantify the subsurface contamination load in the studied area. The detailed values calculation of L1, Q, T were described in Harman et al. (2001). (2) Intrinsic vulnerability: Based on the origin-pathway-target model, the groundwater intrinsic vulnerability is equivalent to the pathway, which describes the process of contamination from the surface to the groundwater level. Intrinsic vulnerability deals with pollution possibilities without considering any specific pollutants (Mimi et al., 2012; Pathak and Hiratsuka, 2011) and depends on the aquifer properties and the associated water sources and stresses for the system. Due to its ease to use, minimum data requirement, and clear explanation, DRASTIC model is the most widely used method of groundwater vulnerability evaluation. A modified DRASTIC model was employed to assess the groundwater vulnerability. The modification was based on improvement of practicability and accuracy of intrinsic vulnerability assessment. Aquifer susceptibility combines the intrinsic susceptibility of the physical system with anthropogenic features that locally increase susceptibility. Due to the average ground elevation between 35–50 m for the whole study area, have no obvious difference between the entire study area, the topography factor is removed. Soil media influence the aquifer susceptibility mainly rely on the soil structure, structure, thickness and organic matter, clay content and soil water content, etc. Organic matter may enhance microbial activity by providing a substrate for the microbes. An increasing organic matter in aquifer media may decrease the concentration of pollutants in the groundwater environment by selective sorption (Su et al., 2015). Impact of the vadose zone refers to the unsaturated zone that modulates vertical migration of pollutants, and the vertical hydraulic conductivity coefficient (Ky) can be used in the assessment of groundwater pollution. An increasing Ky may reduce the residence time of pollutants in the unsaturated zone and enhance the concentration of pollutants in groundwater. Based on the hydrogeological conditions and characteristics of Hun River alluvial fan, select depth to the groundwater depth (D), recharge (R), aquifer medium (A), soil organic matter content (O), impact of the vadose zone (I), permeability coefficient (K), these 6 factors as the intrinsic vulnerability of groundwater index. Optimizing the index weights using of the analytic hierarchy process (AHP) method. Boreholes into the subsurface provide a pathway through which a contaminant can move directly into a deep aquifer, increasing the intrinsic susceptibility of an aquifer at the scale of the wellhead. Using the presence of a well, effective surface seal, well cap/cover, likelihood of ponding around the wellhead, abandoned these five factors to assess the conduits risk

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(C) (Simpson et al., 2014). The shallow groundwater vulnerability characteristics in Hunhe River alluvial fan were calculated according to the following equation: SA ¼ SI þ C

ð8Þ

SI ¼ DR DW þ RR RW þ AR AW þ OR OW þ IR IW þ KR KW

ð9Þ

where SA is the groundwater vulnerability index; SI is the relative groundwater vulnerability index; D, R, A, O, I, and K are acronyms of the six parameters and the subscripts R and W indicate the corresponding ratings and weights. The recharge and aquifer medium have no significant difference in the study area, while the depth to the groundwater level have obvious spatial difference and play an important role in aquifer susceptibility, so the weights of D is the maximum value in the six factors. The parameter R was calculated based on precipitation data from Dai et al. (2016). The parameter A and I were obtained from boreholes (Fig. 2). The groundwater depth was detected in 2010 in Hunhe River alluvial fan, the permeability coefficient was detected by pumping test, the soil organic matter content was analyzed according to Jones and Willett (2006). (See Fig. 1.) (3) Groundwater value: Groundwater value is used to make decision makers realize the present and future value of exploiting groundwater resources.. Valuing groundwater is critical to groundwater development, protection, and remediation projects (Ducci, 1999; Mays, 2013). Usually, groundwater value is evaluated using indicators based on groundwater quantity and quality. A region with abundant groundwater storage that can be represented by a specific well yield and high groundwater quality will have a higher groundwater value. In the present study, groundwater value (GV) was evaluated according to: GV ¼ G  Y

ð10Þ

G is the grade of groundwater quality; Y is a specific well yield.

(4) Risk characterization: There were three types of maps prepared in this study, a hazard map, intrinsic vulnerability map, and groundwater value map. Each thematic map of each variable was prepared as a GIS data layer in the form of a 500 × 500 m grid size raster format. The basic risk map and the value-weighted map were conducted using the raster calculator of the spatial analyst tool in the Arc GIS9.3 software. 2.3.3. Human health risk assessment of groundwater contamination Since the United States Environmental Protection Agency (U.S. EPA) promulgated the Interim Procedures and Guidelines for Health Risk and Economic Impact Assessments of Suspected Carcinogens in 1976, strict health and economic impact risk assessments have been an important part of the regulatory process. Human health risk assessment involves the evaluation and quantification of potential health hazards to humans from exposure to chemicals of concern (Bień et al., 2005). At present, human health risk assessment includes four steps (U.S. EPA, 2005): (1) hazard identification; (2) dose-response assessment; (3) exposure assessment; and (4) risk characterization. 2.3.3.1. Hazard identification. The hazard identification is the first step of risk analysis and requires qualitative and quantitative representation of the contamination situation and data necessary for modelling the fate and transport (Bień et al., 2005). For this step, site investigation and description is the basic requirement. Some of the most commonly data set related to hazard identification were: (1) hydrogeological condition (groundwater depth, net recharge, soil media, aquifer thickness, vadose zone media and groundwater velocity); (2) pollution sources inventory (refuse landfill, sewage irrigation areas and sewage draining system); (3) land use status (industrial land, agricultural land, cultural entertainment land and residence land); (4) groundwater resource utilization (exploitable groundwater and purpose); (5) toxicology and epidemiological data. Finally, based on the date sets above-mentioned, the weight of evidence regarding a chemical's potential to cause adverse human health effects can be evaluated.

Fig. 1. Location of study areas with the two hydrogeological cross sections in the topographic.

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2.3.3.2. Dose-response assessment. The purpose of this step is to document the relationship between dose and toxic effect. The dose–effect relationship can be expressed using the reference dose (RfD) method for risk assessment. The reference dose (RfD) is an oral or dermal dose derived from the no observed adverse effect level (NOAEL), Lowestobserved-adverse-effect level (LOAEL) or Benchmark Dose Lowerconfidence Limit (BMDL) by application of generally order-of-magnitude uncertainty factors (UFs). The RfD is determined by use of the following (U.S. EPA, 2005): RfD ¼

NOAELðLOAEL or BMDLÞ UFs

ð11Þ

A similar term, known as reference concentration (RfC), is used to assess inhalation risks, where concentration refers to levels in the air. EPA's Integrated Risk Information System (IRIS) is a human health assessment database that evaluates information on health effects that may result from exposure to environmental contaminants (U.S. EPA, 2005). When the native data is limited to express dose-effect relationship, IRIS database can be used as reference. 2.3.3.3. Exposure assessment. This step is the process of measuring or estimating the magnitude, frequency, and duration of human exposure to an agent in the environment, or estimating future exposures for an agent that has not yet been released. Exposure assessment considers both the exposure pathway as well as the exposure route. Generally, the ingestion and the dermal absorption of water are expressed by the processes of human daily water drinking and daily showering, respectively. Considering the two pathways mentioned above, the doses received through the individual pathway considered are determined using Eqs. (12) and (13) (U.S. EPA, 2005):

RfD ¼

RfDing ABSGI

 n  HI ¼ ∑i¼1 HQ ing þ HQ derm

5

ð15Þ

ð16Þ

where HQing/derm is the hazard quotient through ingestion or dermal absorption of water (unitless); HI is the hazard index (unitless), which is the sum of the HQs from all the applicable pathways (the pathways classified into two pathways of the ingestion and the dermal absorption in this study); i is the kind of the trace element. ABSGI is the gastrointestinal absorption factor, originated from researches by the US Environmental Protection Agency (U.S. EPA, 2009). Carcinogenic risk is defined as the incremental probability that an individual is developing any type of cancer during a lifetime due to chemical exposure under specific scenarios. Cancer risks (CR) were assessed by multiplying the predicted slope factor. The slope factor (SF) is composed of oral slope factors for ingestion, dermal contact (SFO ABSGI) and inhalation unit risk (IUR). Inhalation risks were calculated based on the most updated US EPA RAGS methodology for this pathway (U.S. EPA, 2009). The carcinogenic risks were only evaluated for those elements whose slope factor has been established (U.S. EPA, 2009). Carcinogenic risk of trace elements was evaluated using Eq. (17) (U.S. EPA, 2005): CRi ¼

n X

ADD j  SF

ð17Þ

1

CR is the carcinogenic risk; SF is the slope factor of a carcinogen; i is the kind of the trace element; j is the kind of the exposure pathway. 2.4. Study area

ADDing

CW  IR  EF  ED ¼ BW  AT

ADDderm ¼

CW  SA  Kp  ET  EF  ED  10−3 BW  AT

ð12Þ

ð13Þ

ADDing and ADDderm are the average daily dose contacted through ingestion and dermal absorption of water, respectively. CW is the average concentration of the studied element in water. IR is the ingestion rate of water. Water intake is directly related to the degree of receptor exposure to pollutants. EF is the exposure frequency. ED is the exposure duration (a). Exposure duration is the time for which the receptors are exposed. BW is the body weight. AT is the averaging time. SA is the exposed skin area. Kp is the dermal permeability coefficient in water. ET is the exposure time. (4) Risk characterization: The purpose of this step is to summarize and integrate information from the preceding steps to synthesize an overall conclusion about risk. A good risk characterization will restate the scope of the assessment, express results clearly, articulate major assumptions and uncertainties, identify reasonable alternative interpretations, and separate scientific conclusions from policy judgments. Risk characterization is often quantified by carcinogenic risk and noncarcinogenic risk (Li and Zhang, 2010). However, nitrate has not been classified as to its carcinogenicity by the EPA, although it is under review (EPA, 1990), so carcinogenicity assessment for lifetime exposure is not considered in this study. To reflect potential non-carcinogenic risks of trace elements, the hazard quotients (HQs) were estimated by comparing exposure of contaminant from each exposure way with the corresponding reference dose (RfD) using Eqs. (14), (15) and (16) (U.S. EPA, 2005): HQ ing=derm ¼

ADDing=derm RfDing=derm

ð14Þ

2.4.1. The lower Liaohe River plain The root mean square error (RMSE), average magnitude of error (AME), and The lower Liaohe River Plain (LLRP), is an important industrial, agricultural and commercial center of NE China, located in the middle of Liaoning Province, between 120°00′–123°50′E and 40°30′–42°10′ N, as shown in Fig. 2. The geology of the LLRP consists of the phreatic aquifer constituted by Quaternary sediments, underlain by the Cenozoic rock. Fig. 2 shows cross-sections based on the hydrogeological map of the plain (Qi et al., 1987). The total area covers about 23,470 km2 and supports important agricultural and industrial activities. The LLRP is the largest alluvial plain and the most important grain production area of Liaoning Province. There are several important industrial cities scattered around the plain such as Shenyang, Anshan, Fushun, Yingkou and Liaoyang (Fig. 4). The major industrial economic sectors in this region include mining (coal, iron ore, petroleum, nonferrous metals and nonmetals), smelting and metallurgy (steel and nonferrous metal) and machining and weaving. The LLRP is a broad alluvial plain, which was formed by a river system. The detrital material was transported and deposited in the basin due to fluvial activity, forming a plain surrounded by mountains on three sides and by the Liaodong Bay on the other side. The geology of the LLRP consists of the phreatic aquifer constituted by Quaternary sediments, underlain by the Cenozoic rock (Fig. 2). The Quaternary sediments are in conformable contact with the underlying Cenozoic rock basement (Fig. 2). The thickness of the Quaternary sediments varies widely between 60 m and 380 m from NE to SW, and increasing from NW and SE towards the middle of the plain (Fig. 2) (Qin et al., 2013a, 2013b). The main minerals in and/or around the LRP areas include calcite, magnesite, dolomite, plagioclase, magnetite and pyrite. The sedimentary facies of the aquifers change from alluvial and proluvialalluvial in the mountain front area, over alluvial-marine sediments in the middle of the plain, to marine sediments in the coastal area. Upstream of the LLRP consists of seven alluvial fans. The sizes of the Liaohe,

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Fig. 2. Two hydrogeological cross-sections (A–B, and C–D) through the study area (locations are indicated in this figure, the geological background and boundary of saline water were digitized and modified from Qi et al. (1987)).

Hunhe, Taizihe and Dalinghe alluvial fans (with areas ranging from 1000 to 1500 km2) are much larger than the other three alluvial fans, which are relevant to the lengths of the river, intensity of runoff as well as the sources of detrital material. The lithological structure of these fans shows a zonation in the horizontal direction, where the gravel, sand and clay sediments are mainly deposited at the top, middle and fringe of the fan, respectively. Groundwater resources are abundant in this region, and the natural groundwater recharge rate is 5.12 thousand million m3 a−1, with 4.78 thousand million m3 a−1 being used. Groundwater development and utilization was very high in the LLRP, groundwater accounted for 65% water supply in this area. There are a large number of environmental problems in the LLRP: (1) spread distribution of poor quality groundwater (i.e. high fluorine, low iodine, and high salinity); (2) Shrinkage and pollution of wetlands in the lower Liaohe river delta; (3) groundwater funnel (overexploitation leading to cones of depression) and ground subsidence in urban area; and (4) widespread groundwater nitrate pollution.

portion of the area (Fig. 4). The Xi River is the main sewage-receiving river in Shenyang. By receiving industrial and domestic wastewater from the west for a long time, the Xi River has been seriously contaminated (Guo et al., 2011). The polluted river water has permeated into the soil and aquifers by lateral seepage and has caused the coastal soil and shallow groundwater to become contaminated (Su et al., 2015). Twenty-six water supply well are located both side of Hunhe River (Fig. 4), and total amount of water supply up to 212,340,000 m3/a The chemistry of groundwater in Hunhe River alluvial fan characterize that pH 5.8–8.3, total dissolution solids 0.1–1 g/L, total hardness N300 mg/L. A total of 37 groundwater samples were collected in 2011, the analysis results showed that main contaminants were total dissolu− + tion solids, total hardness, NO− 3 , NO2 , NH4 , Fe, Mn, Cd, Pb, and phenol. The total dissolution solids, the hardness, Fe, and Mn were influenced by − + hydrogeological settings. The NO− 3 , NO2 , and NH4 mainly come from fertilizers in farmlands, livestock manure in farms, and sewage (Su et al., 2015). The phenol, Cd, and Pb mainly come from industrial activities in Shenyang city (Han et al., 2015).

2.4.2. The Hunhe River alluvial fan The Hunhe River alluvial fan located in northeast of the LLRP, which is also Shenyang City's location (Fig. 4). The study area covers about 1005 km2 and has an elevation of 35–50 m, decreasing gradually from northeast to southwest. The area is characterized by a temperate monsoon climate with an average annual temperature and rainfall of 6.2–9.7 °C and 587.5 mm, respectively. The site is mainly recharged directly by precipitation, infiltration from surface streams, and irrigation. The irrigation area account for 18.65% of the whole area. The eastern part of the study area has a hilly topography, while other areas are covered by Quaternary loose deposits that are thickened with a lithology that changes gradually from coarse to fine from east to west (Su et al., 2015). The Hunhe River flows through the study area from northeast to southwest, and a sewage river (Xi River) flows through the northwest

2.5. Example data sets For the lower Liaohe River Plain data sets, data of agricultural and forest land, the sewage and dangerous waste discharges in the LLRP were obtained from “Liaoning Province Statistical Yearbook 2013”. Values for groundwater exploitation and the river network density were obtained from the China Groundwater Resources Data System and Liaoning Province Water Resources Bulletin 2012. The risk density of chemical enterprises in the district was calculated from the Inventory of Chemical Plants in Liaoning Province 2012. The data of hydrogeological drilling and shallow groundwater quality analysis results are done in collaboration with Geological Survey Institute of Liaoning Province. There was a total of 228 groundwater monitoring point in the region, including 99 new shallow water drilling wells, 129

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drinking water supply well. And the locations of new boreholes, with careful layout scheme, are permitted by Land and Resources Bureau of Liaoning Province. All of the drilling and sampling work according to China's laws and regulations, was not related to the natural reserve and special, endangered or protected species. Groundwater vulnerability to contamination of the LRP was evaluated and mapped by Sun et al. (2007). In the process of field investigation, 228 samples were divided into four groups (high, middle, low vulnerability and Liaohe River delta zones) in terms of the regional geologic environment map and with reference to the vulnerability assessment result of Sun et al. (2007). Then 228 shallow groundwater level value was collected, and 228 groundwater samples were analyzed. It should be noted that the well construction (e.g. well depth, soil media, aquifer thickness, and vadose zone media) may have impacts on concentration of pollutants, and the ways and degrees of influence may vary depending on the contaminant of concern. However, due to the limit of field investigation, the type of pollutants and difference of well construction were not taken into consideration in this study. For the Hunhe River alluvial fan data sets, a total of 37 groundwater samples were collected in 2011 from the shallow irrigation or domestic wells, with the sampling depths ranging from 1 m to 38 m. For both samples of lower Liaohe River Plain and Hun River alluvial fan were gathered in plastic containers and then stored at 4 °C in iceboxes. The samples were immediately transported to the laboratory and all analyses were completed within a week. The pH, water temperature, and COD were measured by a handheld water quality meter in the field, the total dissolved solids (TDS) were calculated by summing + + 2+ 2− the main ionic species (Cl−, NO− and Ca2+) 3 , SO4 , Na , K , Mg with total alkalinity and silica, and the acid–base titration method was − − − used for determination of HCO–3 and CO2– 3 contents. The F , Cl , NO2 , + + 2+ 2+ − 2− + NO3 , SO4 , NH4 , Na , K , Mg and Ca contents were determined by ion chromatography (ICS-1100), following the US Environmental Protection Agency (EPA) standard methods. Samples with a difference in electrical balance exceeding 5% were not included. 2.6. Statistical and spatial analysis Statistical analyses were performed with SPSS Statistics V20.0 (SPSS Inc. Quarry Bay, HK). Nemerow pollution index method (Wu et al., 2015), a commonly-used water quality evaluation method was used to evaluate groundwater quality. Interpolation mapping was conducted using Inverse Distance Weighted (IDW) within ArcGIS 10 software. 3. Results 3.1. Groundwater relative risk of the LLRP 3.1.1. Risk sources As for groundwater system, the risk sources may be related to land use (agriculture, forestry, industry, and city), river net, waste water, and solid waste. In this study, data of agricultural and forest land, the sewage and dangerous waste discharges in the LLRP were obtained

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from “Liaoning Province Statistical Yearbook 2013 (in Chinese)”. Values for groundwater exploitation and the river network density were obtained from the China Groundwater Resources Data System and Liaoning Province Water Resources Bulletin 2012 (in Chinese). The risk density of industries in the district was calculated from the Inventory of Chemical Plants in Liaoning Province 2012 (in Chinese). According to the field surveys and data research, the principal sources of risk, seven kinds of sources were identified (Table 1): groundwater exploitation, drainage, chemical industry, agricultural area, sewage discharges, hazardous waste, and forested area. These sources were characterized by degree of groundwater exploitation, river network density, industry density above a certain size, agricultural area ratio, waste-water discharge intensity, and dangerous waste produce intensity, and were then normalized by dividing by the maximum value. The risk screening scores of major potential contamination sources was illustrated in Table 2. In addition, the normalizing source risk values were then presented in Fig. 3 by dividing 17 county level administrative areas, which are Xinming county, Liaozhong county, Shenyang's downtown, Heishan county, Beining county, Linghai county, Panjin county, Panshan county, Dawa county, Yingkou's downtown, Dashiqiao county, Haicheng county, Taian county, Anshan county, Dengta county, Liaoyang county, and Liaoyang's downtown, respectively. 3.1.2. Risk pathway In this study, in order to reflect impacts of risk source on risk receptor accurately, the impacts were described by two specific aspects, which were groundwater quality and groundwater table. As in similar studies (Chen et al., 2012), categories of no, lowest, low, medium, high, and highest risk were used to describe SSC and SEC on a scale from 0 to 1. At the same time, with considering the regional difference that stressor acted on receptor and each receptor accepted the stressor, the SSC of each assessing unit was different. The SSC and the SEC of the study area is supplied in Table 2. A value of 0.9 indicates a clear or validated relationship, 0.7 indicates a relatively strong relationship, 0.5 indicates an obvious relationship, 0.3 indicates less common ground, 0.1 shows that the relationship is not clear, and the value of 0.0 means no relationship at all. 3.1.3. Risk receptor The risk receptor of groundwater is aquifer system, especially groundwater quality and water level, and the endpoint is water quality degradation and environmental questions caused by groundwater level decreasing. Shallow groundwater in the LLRP quality was evaluated by comparing 228 groundwater samples in “the Chinese Groundwater Quality Standards(GB/T 14848-93)”, which divides the groundwater quality into five categories (I, II, III, IV and V, respectively), indicates the good or bad quality of groundwater, Class I and II is suitable for various purposes, Class III is applicable to centralized drinking groundwater sources, or for industry and agriculture, Class IV and V represents poor water quality, should be strictly processed to use. Overall, the groundwater quality is CLASS V in Shenyang, CLASS IV in Panjin and Yingkou,

Table 1 Exposure and response filters of risk sources, receptors, and endpoints. Sources

Groundwater exploitation Drainage Chemical industry Agricultural area Sewage discharges Hazardous waste Forested area a b

SSCa

SECb

Contamination

Flow change

Groundwater quality deterioration

Groundwater supply capacity reduce

0.3 0.7 0.9 0.3 0.5 0.7 0.0

0.9 0.3 0.3 0.0 0.3 0.0 0.0

0.3 0.7 0.7 0.5 0.5 0.7 0.5

0.7 0.5 0.5 0.0 0.3 0.0 0.0

SSC is the source–stressor–receptor exposure coefficient in sub-regions. SEC is the stressor-endpoint response coefficient in sub-regions.

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Table 2 Risk screening scores of major potential contamination sources. Name

Contaminant

L1a

Qb

Tc

Overall risk

Risk level

Agricultural area Agricultural area Chicken farm Garbage heap A Mine dumps Gas station

Corn crop-pesticides Soybean crop-pesticides Nitrate Chromium Arsenic

0 0 −1.5 0 0

3.12 2.05 −5.63 1.29 −3.18

−0.1 −0.2 −1.5 0.7 3.7

3.02 1.85 −4.38 1.99 0.52 4

High Medium Low Medium Low High

a b c

Likelihood of a contaminant release from the source. Quantity released from the source. Toxicity of the contaminant.

and CLASS III in the west and the southeast of the Liao River Plain. For groundwater quality in the different alluvial fans plains, the quality is CLASS II in the Haichenghe alluvial fan and Yangchenghe-heyuguhe alluvial fan, CLASS III in the central alluvial plain, CLASS IV in the Daxiaolinghe fan and in the Liaohe river delta zone, and CLASS V in the Liao River alluvial fan and the Hun River alluvial fan (Fig. 5c). The most frequently reported pollutants are iron, manganese, and ammonia nitrogen. The main sources of groundwater pollution are sewage, industrial waste-water effluent, municipal solid waste, agricultural pollutants, and mining activities (Qin et al., 2013a, 2013b). Groundwater accounts for 70.78% of the water supply in Shenyang, and 89.2% of the water supply in Jinzhou (Liaoning Statistics Bureau, 2014). 3.1.4. Calculating and mapping of relative risk The results of the relative risk assessment for the LLRP are shown in Fig. 5d. The relative risk rank V areas are mainly in the Hunhe River alluvial fan in Shenyang; it covers 2335 km2, with 9.9% of the region, with the reason of high supply of groundwater resources in the area, and the groundwater resources therefore need to be protected in this area (Su et al., 2015). Because of the low (some parts are medium) values for the vulnerability index, industrial solid waste and waste from well-developed regional chemical enterprises is not effectively used or disposed of, so groundwater is polluted to different degrees. Further, some of the groundwater engineering structures for checking water measures are not complete, which means that there is aquifer pollution from surface sewage, and different aquifers are polluted by the same contaminants. The relative risk rank IV areas are mainly located in Liaoning Province, in Xinmin and Yingkou, and cover 3978 km2, accounting for

16.9% of the area. In this area the degree of risk is high. The groundwater is widely contaminated by fertilizers and pesticides through soil percolation, moreover, long-term applications of sewage for irrigation are compromising farmland and groundwater quality (Xiao et al., 2019). The situation is exacerbated by excessive use of ammonia nitrogen, nitrite nitrogen and nitrate nitrogen, and there is organic pollution in agricultural areas. The relative risk rank III areas are distributed in the central plains, on both sides of the Liao River, and the relative risk rank II areas are found in the southeastern coastal areas, meanwhile the relative risk rank I area is in the western mountains, where there is relatively low risk to groundwater environment. Hunhe River alluvial fan was one of the highest relative risk areas (Fig. 5d), therefore, it was selected to assess groundwater contamination risk. 3.2. Groundwater contamination risk in the Hunhe River alluvial fan 3.2.1. Contamination source load The contamination source survey (440.71 km2 residential area, 140.04 km2 agricultural area, 110 industrial enterprises, 33 landfills, 44 livestock farms, 161 gas stations, and 1 sewage ditch) was carried out in July in 2012. The survey result showed that the major contamination sources were agricultural activity (pesticides and fertilizers in farmlands, livestock manure in farms), industrial activity (solid waste, wastewater), municipal activity (sewage and landfill), and gas and oil tanks. Combined with land-use, the contamination source distribution was shown in Fig. 6a. The survey does not provide the risk assessor with chemical use information needed to complete the subsurface contamination load.

Fig. 3. Map of (a) land uses of the lower Liaohe River Plain and (b) drinking water supply wells (associated with water treatment plants) in the Hunhe River alluvial fan. The Hunhe River alluvial fan located in northeast of the lower Liaohe River Plain, which is showed by dotted line borders in (a).

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Fig. 4. Groundwater relative risk assessment of the lower Liaohe River Plain (a: the normalization of risk value for the relative risk sources based on administrative areas at county level; b: distribution of groundwater vulnerability index; c: distribution of groundwater quality; d: distribution of relative risk rank of groundwater).

However, several databases and handbooks are available to estimate typical chemical presence based on facility type, including the Integrated Risk Information System (IRIS) and the Health Effects Assessment Summary Tables (U.S. EPA, 1989); therefore, these were used to estimate chemicals associated with the PCSs listed in Table 2. Because detailed information pertaining to underground tanks in the study area is unavailable, the number of gas stations in every grid was used to determine the risk of tanks. Each of the contamination source layers representing a various land use was assigned a value and fitted in one city scale map (Fig. 6a). All layers were overlapped using the simple summation on ArcGIS and the results were shown in Fig. 6b. The subsurface contamination load map for the Hun River alluvial fan is classified into five levels: very low, low, medium, high and very high (Fig. 6b). The highest contamination load was in the northern portion of the study area, where there is a high number of gas stations. The southwest parts of the studied area have a moderate potential contamination risk due to active agrochemical applications. Low contamination risk zones were widely distributed throughout the studied area, which may have been related to the distribution of land use (Fig. 6b).

directly into a deep aquifer, increasing the intrinsic susceptibility of an aquifer at the scale of the wellhead (Simpson et al., 2014). The indices and scores of conduit were introduced by Simpson et al. (2014): (1) presence of a well (yes = 1, no = 0), effective surface seal (good seal installed = 0, adequate seal installed but length or width is not ideal = 1, adequate seal likely installed = 2, adequate seal likely not installed = 3), well cap/cover (adequate cap installed = 0, adequate cap likely installed = 1, adequate cap not likely installed = 2), likelihood of ponding around the wellhead (ponding is unlikely = 0, ponding is probable = 1, ponding is highly likely = 2), and abandoned (well in use or closed = 0, well likely abandoned and in disrepair = 2). The conduits risk divided into six classes according to the cumulative conduit score: 1–5 very low, 6–11 low, 12–19 medium, 20–29 high, N30 very high. In Hunhe River alluvial fan, 289 wells were investigated for assessing conduit risk. The conduit risk characterized most wells were low risk, while high conduit risk distributed some water sources (Fig. 6c). In order to overlay groundwater susceptibility and conduits risk, the study area was divided into 500 m × 500 m grids, the accumulated risk from wells of each grid was calculated and illustrated in Fig. 6c.

3.2.2. Intrinsic vulnerability assessment of the Hun River alluvial fan Using the modified DRASTIC model, an intrinsic vulnerability map of the Hunhe River alluvial fan was obtained with five classes (Fig. 6c). The vulnerability of the studied area had a banding distribution from north to south. The southern and northern portions had low vulnerability, indicating that the aquifer is not easily contaminated and the likelihood of being threatened by contamination is low. This is due to the relatively deeper depth of groundwater (9–15 m below the surface) and lower net recharge in the area (Moratalla et al., 2011). Most of the central portion of the study area showed moderate vulnerability, which was the comprehensive effect of several parameters (Su et al., 2015). The high vulnerability areas were mainly located in the central parts, although some were scattered in northern parts due to the shallow groundwater levels and larger hydraulic conductivity (Fig. 6c). Boreholes into the subsurface, including geotechnical and water wells, provide a pathway through which a contaminant can move

3.2.3. Groundwater value The groundwater quantity was divided into four classes from very high to very low, that is N5000, 3000–5000 1000–3000, 0–1000 m3/d. According to the Shenyang Water Resources Bulletin (2012), the groundwater quantity of each water plant in Hunhe River alluvial fan rangingfrom 3000 to 5000 m3/d, which indicate the groundwater supply is relative high. The groundwater quality was classified into five levels from very poor to good according to the quality standard for ground water. A total of 37 wells with their water quality data were selected to calculate groundwater value in Hunhe River alluvial fan. The groundwater value was then calculated and classified as shown in Fig. 6d. The western areas of the fan have the highest groundwater quantity and the worst groundwater quality, resulting in a low to medium value in this region of the fan. When compared with the eastern and northwest areas of the plain, the northeastern and southwestern regions show relatively high groundwater values (Fig. 6d).

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Fig. 5. Groundwater contamination risk assessment of the Hunhe River Alluvial fan (a. distribution of potential pollution sources; b. hazard ranking of potential pollution sources; c. distribution of groundwater vulnerability index; d. characteristics of groundwater value; e. basic contamination risk of groundwater; f. value-weighted groundwater risk distribution).

3.2.4. Mapping of groundwater contamination risk In this study, groundwater contamination risk was divided into basic contamination risk and value-weighted contamination risk. The basic contamination risk is composed of contamination load and intrinsic vulnerability (Gogu and Dassargues, 2000). The basic risk map of the study area generated by overlaying the subsurface contamination load map and intrinsic vulnerability map with equal weights using ArcGIS can identify which area is at the highest risk and which area should be monitored frequently. The map showed that the groundwater in the northeast and central portions of the fan, where intrinsic vulnerability risk is medium (Fig. 5b), is threatened by medium contaminate risk (Fig. 6e). Similar levels of risk are scattered throughout the southwest and east areas, where there is a relatively high level of agricultural application. The banding distribution in the northwest portion of the fan is at the lowest level of risk, primarily due to those areas having the lowest vulnerability class and no potential contamination sources. The valueweighted contamination risk was composed of the basic contamination risk and groundwater value. The value-weighted risk map (Fig. 6f) produced by the basic risk map and the groundwater value map were used to provide protection measures for decision makers. The spatial distribution of the value-weighted risk map was similar to the basic risk map, with banded distribution from north to south. The central parts and agricultural activities areas located in the high groundwater value region were at the greatest risk and should therefore receive the most attention.

The decision support maps can provide scientifically based defendable spatial distributions of land use restrictions and allocations of future hazardous activities (Fadlelmawla et al., 2011). However, subjectivity is unavoidable when choosing principles to evaluate the risk. Civita and de Maio (2006) considered the probability of the occurrence of an undesired event and a cost (damage) associated with the hazard-damage theory, while Fadlelmawla et al. (2011) made decisions according to land surface zoning to facilitate groundwater protection. Hazard, intrinsic vulnerability, and groundwater value have been widely applied to assess groundwater contamination risk. Additionally, the groundwater contamination risk is dynamic, necessitating that the data employed for evaluation be updated frequently. The groundwater contamination risk maps indicated that the groundwater was threatened by potential contamination throughout the study area, especially in the central portion of the Hunhe river alluvial fan. Agricultural applications were found to be the major contamination source, and the central area was most vulnerable to groundwater contamination due to the hydrogeological conditions. Except for the northwest, the groundwater values were above middle level (Su et al., 2015). The results indicate that effective measures should be taken, such as controlling sources of contamination with high-risk potential and forbidding establishment of polluting industries in highly vulnerable regions. Groundwater risk maps are constantly changing owing to the dynamics of potential contamination sources; therefore, the data presented herein should be updated regularly.

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Fig. 6. Human health assessment of groundwater nitrate in Hunhe River Alluvial fan (a. groundwater sampling sites; b. distribution of nitrate concentration; c. non-carcinogenic risk of nitrate for adult; d. non-carcinogenic risk of nitrate for children).

3.3. Human health assessment of nitrate in groundwater Water contamination and human health are interdependent (Khan et al., 2009). Consumption of contaminated water can cause serious health risk to humans. It has been discovered that exposure to potentially toxic chemicals such as heavy metals and nitrate in water can impose great risks on human health (Su et al., 2013; Ali et al., 2018). Nitrate pollution of groundwater has become a serious problem in northern China. Nitrate was one of the typical pollutants in groundwater in the LLRP. So nitrate was selected to assess human health effect in groundwater in Hunhe River alluvial fan.

3.3.1. Hazard identification of nitrate A total of 37 groundwater nitrate samples were collected in Hunhe River alluvial fan, the mean concentration of nitrate was 89.45 mg/L (ranged from 0 to 247.93 mg/L). In comparison with nitrate concentration in other areas of China, such as Shandong province for 25.397 mg·L−1 (minimum value with 0 mg·L−1, maximum value with 184.600 mg·L−1) (Zhai et al., 2017) and Northeast of China for 39.46 (minimum value with 0.02 mg·L−1, maximum value with 497 mg·L−1) (Zhai et al., 2017). The distribution of nitrate was

illustrated in Fig. 7b. The primary toxic effect of inorganic nitrates is the oxidation of the iron in hemoglobin by excess nitrites forming methemoglobin. Infants b6 months old have lower B5 reductase activity in their red blood cells than adults comprise the most sensitive population. Nitrite formed via reduction of nitrate in the human body can react with secondary amines to form nitrosamines, which can be carcinogenic. However, there is no conclusive evidence that nitrate and nitrite will cause cancer. Thus, the adults HI was 0.00–16.94 (average 2.18) and the children HI was 0.00–18.63 (average 2.39).

3.3.2. Dose–response assessment of nitrate Epidemiological studies have shown that baby formula made with drinking water containing nitrate nitrogen levels over 10 mg/L can result in methemoglobinemia, especially in infants b2 months of age. No cases of methemoglobinemia were reported with drinking water nitrate nitrogen levels of 10 mg/L or less (Bosch et al., 1950). The China Drinking Water Standards (GB5749-2006) set the nitrate limit in drinking water at 10 mg/L (measured as nitrogen), while the groundwater limit was 20 mg/L (measured as nitrogen). A Reference Dose (RfD) of 1.60 mg/kg/day (nitrate nitrogen) for chronic oral exposure was calculated from a NOAEL of 10 mg/L and a LOAEL of 11–20 mg/L in drinking

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water, based on clinical signs of methemoglobinemia in 0–3-month-old infants (Bosch et al., 1950). 3.3.3. Exposure assessment of nitrate The detailed values and description of the parameters are given in Table S1. Water intake is directly related to the degree of receptor exposure to pollutants (Table S2). There is insufficient data pertaining to the study area available for statistical analysis. The USEPA and the Dutch drinking water sector target reference values are given as the recommended values for drinking water: Daily 2 L for adults, infants and young children (body weight b 10 kg) daily 1 L. In this study, we selected 2.3 L/d for adults and 1.5 L/d for children based on the statistics values (90th percentiles) by Su et al. (2015). Taking into account exposure pathway and population migration, in this study we selected 365d/ a for EF, 24 y for adults ED, 6 y for children ED and as such e.g. also used by Su et al., 2015. The regional population distribution of sex and age for the study area (Shenyang) are presented in Table S3. We selected 59 kg for adults and 35 kg for children based on the 2002 national urban and rural average weights of residents of different genders and ages (Su et al., 2015). The average exposure time is ED (exposure duration) × 365d/a. This is a standard assumption value used by the EPA for non-carcinogenic substances (U.S. EPA, 1989). 3.3.4. Health risk characterization of nitrate Based on all of the mentioned information, we obtained a health hazard index (HI,) which represents the non-carcinogenic health risk level. Fig. 7c and d shows the distribution of adult and children health risk assessment. The spatial distributions of adult non-carcinogenic risk are remarkably similar with children. It is important to notice that the proportion of sites with a HI of nitrate larger than 1 were 46.43%, for both adult and child, suggesting that these sites can pose noncarcinogenic risks to the surrounding populations. Compare with adults, children have a higher susceptibility of exposure to environmental contaminants per unit body weight due to their behavioral and physiological characteristics. Although large parts of the area have an HI b 1, the distribution of HI N 1 across the study area suggests that nitrate is a contaminant of concern that needs special attention. 3.4. Relationships among different scales risk assessments Following the qualitative discussion above, it is observed that the framework of groundwater risk screening and assessment was classified into three layers: (1) environmental risk assessment at basin scale, (2) vulnerability risk assessment at local scale (e.g. industrial park scale, urban scale), (3) health risk assessment at site scale. As for each risk assessment, extensive researches for different purposes have been conducted in China over the last 20 years. The corresponding working guideline or technical requirement was gradually formed over time. These practical implementations provide scientific basis for the planning stage of socio-economic activities at different scales. However, perhaps the most importantly for GCVR as a useful tool for groundwater pollution prevention and control is combining these risk assessments to form optimal balance among the complexity of method, cost and uncertainty of assessment results. It is important that regulators recognize that risk management and control depend on location and scale. It changes from area to area and with the scale of the area investigated. Different type of risk assessment has both strengths and weaknesses. Ideally, risk assessment should start at the regional scale, and continue to the local and finally site scale in areas of concern. Environmental risk assessment has to start at the regional scale because it is most cost-effective approach that requires the lowest sample density. It also gives proposals for vulnerability risk assessment at local scale by combining with the distribution of high environmental risk areas. As the parameters for vulnerability risk assessment were often difficult to obtain, vulnerability risk assessment usually be conducted at local scale. The advantage of health risk

assessment is providing reliable basis for risk managers which are necessary to make proper, fully informed decisions enhancing public trust and credibility. However, the health risk assessment usually be conducted at site scale because it is most cost approach that requires the highest sample density. It's worth noting that the framework proposed in this study can be used in some cases, but definitely not in all. In some cases, such as some contaminants is low at regional scale but actually high at local scale, the high risk information of localized areas will not be screened by the framework proposed. This is mainly due to the existing sample density cannot be meet the contaminant risk screening requirements. In this case, the risk assessment at the site should be of primary importance with risk assessment at a regional scale in order to make the risk screening process more effective and ensure the inhabitants safe and healthy conditions. However, for regional groundwater risk screening and assessment, risk assessment at regional scale should be the first step, unless some local or site areas have identified by other information. 4. Conclusion In this study groundwater nitrate relative risk, contamination risk, and human health risk in basin of lower Liaohe river plain have been assessed by using three risk assessment models under a framework. About 9.9% of groundwater basin area in the Liaohe River plain is under extremely high relative risk (V class) zones and selected as priority area for contamination risk assessment. The high relative risk areas (IV class) are mainly located in northeast of the whole area, accounting for 16.9% of the area with 3978 km2. For groundwater contamination risk, agricultural applications were found to be the major contamination source, while hydrogeological conditions were responsible for high vulnerable of zones. According to health risk assessment, in some zones, nitrate in groundwater pose high non-carcinogenic risks to the public, especially to children and those living in the most severely polluted regions. Special attention and measures were suggested to be taken, such as controlling source emission of nitrate and forbidding establishment of nitrate-related industries in highly vulnerable regions. Applying groundwater risk assessment is essential for land planning and groundwater management. One of challenges for groundwater management is make a balance between time and labor consuming and meaningful of groundwater risk assessment. The framework proposed in this study was proved to be suitable for screening groundwater risk from regional scale to site scale. Such screening is an optimize strategy by take advantage of applicable scale of different risk methods. Taking into consideration of the groundwater vulnerability to nitrate, the results can provide effective support to the groundwater management because of its flexibility and clear explanation of the results. However, due to dynamics of potential contamination sources, groundwater risk results are constantly changing, the maps presented in this study should be updated regularly. It is noted that the accuracy of the groundwater risk assessment could be improved if there is further investigation of pollution sources. Although subjectivity was unavoidable in the study, some measures, such as the modification and screening of risk models, could overcome this disadvantage to some extent. In addition, more representative groundwater samplings could be collected and analysis for further validation of the vulnerability assessment results. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements The authors acknowledge the financial supports from National Key Research and Development Program of China (2018YFD0900805), the

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111 Project (B18006). The authors gratefully acknowledge reviewers for their scientific suggestions and constructive comments. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi. org/10.1016/j.scitotenv.2019.134102.

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