Characterization of controlling hydrogeochemical processes using factor analysis in Puyang Yellow River irrigation district (China)

As one of the most important water resources, groundwater constitutes nearly all usable freshwater resources in the world, if the polar ice caps and glaciers are not considered (Chapman 1996). Groundwater resources have been used for domestic, agricultural, industrial, and/or environmental purposes based on water quality (Prasanna et al. 2010). Groundwater quality is a collective result of various factors including natural processes and anthropogenic activities (Rajmohan & Elango 2004). In a natural system, atmospheric deposition, mineral dissolution–precipitation, and cation absorption–desorption processes are the responsible processes for groundwater chemistry. With the rapid increase in population and industrial and agricultural development, anthropogenic activities show increasing influence on the groundwater chemistry and natural geochemical processes. As such, changes in groundwater chemistry compositions caused by irrigation should not be neglected (Deshmukh et al. 2011; Qin et al. 2011) because large amounts of irrigation water not only change local hydraulic conditions but also transport chemical substances from irrigation water and the vadose zone into the groundwater aquifer, thereby altering the groundwater chemical compositions of groundwater.

In irrigation districts, groundwater chemistry is influenced by the hydrochemical processes that occur in the natural system and by irrigation water quality. As surface irrigation water usually has relative low salinity, infiltration of irrigation water largely dilutes the groundwater. However, in some cases, infiltration of irrigation water may also increase groundwater salinity. Affected by strong evaporation before and during the infiltration process, surface irrigation during the summer season may increase groundwater salinity (Kass et al. 2005). Contaminated irrigation water with high salinity also contributes to the increase in groundwater salinity. In shallow groundwater areas, a large amount of irrigation return flow and less groundwater abstraction raise the groundwater level and result in groundwater salinization caused by strong phreatic water evaporation. In addition to the influence of irrigation on general groundwater salinity, irrigation activities also affect the spatial distribution of groundwater salinity through the redistribution of soluble salts in a phreatic aquifer (Surinaidu 2016). With the influence of geographic conditions, permeability of soil, and thickness of unsaturated zone, irrigation water infiltration can alter the original groundwater flow, and chemical substances dissolved in groundwater usually move with the water flow and concentrate in low-lying areas and downstream regions. Together with the overall change in groundwater salinity, chemical compositions also change significantly in irrigation regions. Most of the chemical modifications can be attributed to the base-exchange reactions which are primarily controlled by the initial chemical composition of the irrigation water. Normally, infiltration of fresh irrigation water with high Ca²⁺ and less Na⁺ is usually accompanied by cation-exchange process in groundwater involved in the uptake of Ca²⁺ and release of Na⁺ into water, resulting in Na⁺/Cl⁻ increase in groundwater. Carbonate dissolution in the unsaturated zone can be induced by the removal of Ca²⁺ from the water (Appelo & Postma 2005). However, because domestic wastewater is enriched with Na⁺, irrigation of water contaminated by domestic sewage leads to reverse cation exchange. Excess Na⁺ and K⁺ are absorbed by clay minerals, whereas Ca²⁺ and Mg²⁺ are released into the water (Stigter et al. 1998; Appelo & Postma 2005; Kass et al. 2005).

To identify the dominant hydrochemical processes in groundwater and to assess their contributions to geochemical evolution in irrigation areas, different methods, including hydrogeochemical diagrams such as Piper trilinear nomograph and Gibbs diagram (Gibbs 1970), ionic ratios method, multivariate statistical analysis (Lawrence & Upchurch 1982; Munoz-Carpena et al. 2005; Panda et al. 2006; Cloutier et al. 2008), and inverse and forward geochemical modeling have been intensively used. Field sampling and investigation at basin scale usually produce a large number of data sets including different physical and chemical indicators. Identifying the dominant hydrochemical processes based on visual inspection and descriptive statistics only can be difficult. Eigenvector techniques including factor analysis (FA) are efficient dimensionality reduction methods conducted by extracting hidden common factors from a large amount of highly correlated variables. These methods are widely used to determine the dominant processes that may regulate the hydrogeochemical and physical components of groundwater (Dragon 2006; Panda et al. 2006; Yang et al. 2010; Surinaidu 2016).

The Yellow River (YR) is the second longest river in China and the most sediment filled river on Earth. Along the YR and its tributaries, irrigation districts with different scales are densely distributed. Due to sediment deposition in the watercourse of the flow path, the world famous ‘suspended river’ is gradually formed in the lower reaches of the YR, with the riverbed 3–8 m above ground. The location of the suspended river allows YR to replenish the groundwater at both sides all year around. The irrigation district located in the lower YR is the largest gravity irrigation area in China, with an accumulated amount of water of 294.95 billion m³ diverted from the YR since the 1970s and an average annual irrigation area of 20,956 km² since the 1990s (Wang 2007). Therefore, studying the impact of surface water irrigation on groundwater quality and the controlling hydrochemical processes in irrigation districts are important for managing water resources and securing the safeness of drinking water in these districts.

The objective of this paper is to determine the major factors controlling groundwater chemistry using FA, interpret the corresponding hydrogeochemical processes combined with ionic ratios, and identify the area of influence and its intensity by each hydrogeochemical process based on factor scores.

PYR is an important base for grain production and petrochemicals with a water-deficit region of 220 m³ water resources per capita, which is only one tenth of the country's average. It has nearly 90 tributaries belonging to the YR and Haihe River systems, respectively, most of which are medium and small rivers. Among them, YR, Jindi River (JDH), and Weihe River (WH), spanning the study area from west to east, are the most important ones of the river systems, and the amount of available passing water is 854 million m³.

To relieve pressure on local water resources and better utilize the unique geographical advantages of the YR, which is suspended over the ground in its lower reaches, local government began diverting the water from the YR for farmland irrigation during the 1950s. However, a deficiency in facilities supporting irrigation and blind development during the Great Leap Forward period caused the ‘stop irrigation’ period in the YR because of extensive land salinization (Wang 2007). During the establishment and operation of the first Puyang-Qingfeng-Nanle (PQN) irrigation canal in 1986, the PYR irrigation region officially entered the modern irrigation period. With the construction of the second and third PQN irrigation canals and relevant facilities in the past 30 years, the amount of water diverted from the YR has increased to around 500 million m³ per year. Across the YR and Haihe River basin, artificial canals in the irrigation district are mainly distributed from south to north.

The PYR irrigation district is superposed over the Dongpu sag, which is a Cenozoic extensional rift basin located in the southwest part of Bohai Bay basin. This sag is controlled by the master fault, the Lanliao fault, and is a large rollover structure over the listric Lanliao fault. Within the study area, the longest fault is the Changyuan fault, which is the boundary of Dongpu sag and Neihuang uplift, whereas the second longest fault is the YR fault, which is situated between Changyuan and Lanliao faults.

To better understand the hydrochemical composition of shallow groundwater, 50 groundwater samples were collected uniformly in the PYR irrigation district within the first week in August 2013, and were taken from shallow wells with depth <100 m. Two 50 mL polyethylene bottles with watertight caps were used to store the filtrated water (0.45 μm Millipore membrane filter) for cations (Ca²⁺, Mg²⁺, Na⁺, K⁺, Sr²⁺) and anions analysis, and another 100 mL was used for determining bicarbonate concentration. All samples were stored at 4°C after bottling.

Electrical conductivity (EC), pH, and water temperature were measured in situ via a previously calibrated multi-parameter portable meter (HACH40d, USA). concentration was determined according to acid–base titration method on the day of sampling before filtration. UV254 was measured by ultraviolet spectrophotometer (DR5000, USA).

The major ions of water samples were treated and analyzed in the Physical and Chemical Analysis Center Laboratory of the Institute of Geographic Sciences and Natural Resources Research (IGSNRR), Chinese Academy of Sciences (CAS). Cations (Ca²⁺, Mg²⁺, Na⁺, K⁺, and Sr²⁺) and SiO₂ were measured by inductively coupled plasma optical emission spectrometry (ICP-OES). Anion analysis was carried out on ion chromatography (IC). The limitation of detection of ICP-OES and IC are 1 μg/L and 1 mg/L, respectively. Analytical precision for major ions was within 1%. For all water samples, ion balance error was <5% (Appelo 1996).

The FA model assumes that for a specific variable, its variance is composed of the linear function of several common factors and a special factor unique to the original variable. The basic purpose of FA is to extract the common factors from a large number of highly correlated variables and calculate the factor loadings for each variable and factor scores for each sample. The premise of using FA is that variables are highly correlated, making it possible to extract common factors. In addition, enough samples are available to ensure the stability of the results of FA.

To facilitate FA, and eliminate the influence caused by differentiation of data units and dimensionality, the data were standardized to produce a normal distribution of all variables prior to FA (Davis 2002). The first step of FA was to generate the correlation coefficients matrix of the initial data set, in which most correlation coefficients were greater than 0.3. The derived factors, which were linear combinations of the original variables, were extracted by the principal component analysis method. Factor extraction was done using the criteria whose eigenvalues were greater than 1. The first factor obtained explains the largest number of variance, and the following factors explain repeatedly the smaller parts of the variance. To understand and interpret the derived factors, orthogonal rotation of the initial factors to terminal factor solutions was done with Kaiser's Varimax scheme. This method maximized the variance of the loadings on the factors and hence adjusted them to be near ±1 or zero. Factor loadings showed how the factors characterized the variables, with high factor loadings (close to 1 or −1) indicating the strength of the relationship (positive or negative) between a variable and a factor describing that variable (Dragon 2006). The sum of squares of factor loadings for each variable is called communality and reflects the proportion of the total variability of the variables accounted for by the factoring.

To evaluate the correctness of the FA, namely, to examine if the correlation coefficients among the variables are sufficiently high to be reduced by the factor analytical model, the Bartlett test of sphericity and the Keiser–Meyer–Olkinn (KMO) measure of sampling were computed. KMO test was used to examine partial correlation between variables, with values ranging between 0 and 1; the closer a value to 1, the stronger was the partial correlation that indicates the effectiveness of FA. Usually, when the value was greater than 0.5, conducting FA was approved. When the KMO value was greater than 0.8, it is considered excellent for FA, whereas a value less than 0.5 often indicated that the data set was not appropriate. Bartlett test of sphericity was used to determine whether the different variables were strongly correlated; P > 0.05 meant that the data followed the test of sphericity, and the variables were highly correlated, indicating their suitability for FA and vice versa.

Factor scores were calculated by summing the products of a series of factor score coefficients and their corresponding data. Factor scores greater than +1 usually indicated intense influence by the process; factor scores less than 0 suggest virtually the process represented by factor that has no effect, whereas near-zero scores reflected areas limitedly affected by the process. Data obtained from the in situ measurement and laboratory analysis were used as variable inputs for FA. FA was performed using Statistical Product and Service Solutions 19.0.

As one of the most useful multivariate statistics methods, successful application of FA largely relies on the reasonable and professional interpretations of the extracted results. Ionic ratio was used to distinguish the different hydrogeochemical processes and identify the major dissolved minerals responsible for different ion concentrations in water. and mole ratios were used to identify the major hydrochemical processes and compositions of minerals that are the source of Na⁺ and Ca²⁺ + Mg²⁺ in groundwater, respectively (Meybeck 1987). For the cation exchange process that exists in most groundwater systems, was the commonly used indicator. Joint use of FA and ionic ratio method could be promising in characterizing the dominant hydrogeochemical processes in groundwater; on one hand, ionic ratios of waters with different factors helped in the interpretation of extracted factors; on the other hand, factor scores for water samples contributed to the further understanding of the distribution feature, especially the extremum and outliers in the ionic ratio scatter plots. In addition, spatial mapping of factor scores that represented the governing hydrogeochemical processes was used to identify the intensity of influence and extent of specific hydrogeochemical processes.

Basic statistics of the chosen chemical parameters are listed in Table 1. In general, the dominant cation in groundwater was with median value of 340.1 mg/L, and the dominant anion was Na⁺ with median value of 153.1 mg/L. The coefficients of variance (CV) is the ratio of standard deviation to the mean value of a variable. CV shows the extent of variability in relation to the mean of variables, and is advantageous in allowing comparisons among variables that differ in units and ranges. Among these variables, showed the highest CV, which is probably affected by point contamination, whereas demonstrated the smallest CVs indicating the similar formation background of the groundwater in the study area. Correlation analysis results are shown in Table 2. EC was significantly correlated with most variables, with the highest correlation coefficient of 0.803 computed for Na⁺. Mg²⁺, Ca²⁺, and Sr²⁺ were highly and positively correlated, among which the correlation coefficient between Mg²⁺ and Sr²⁺ was greater than 0.9, possibly indicating their common source. No significant correlation was observed between Mg²⁺ and Sr²⁺, and Na⁺, which suggests that Na⁺ had a different source from the other major cations. As for the anions, were significantly correlated, whereas which is usually assumed to be a product of mineral weathering, was significantly and negatively correlated with Cl⁻, and no obvious relationship was found between ⁠. Beyond certain limits, and UV254 are often regarded as indicators of anthropogenic pollution (Fenech et al. 2012); these two parameters were significantly and positively correlated in the study, with a correlation coefficient greater than 0.5.

Table 1

Descriptive statistics of hydrochemical parameters

Parameters .	Minimum .	Maximum .	Median .	Standard deviation .	Coefficient of variance/% .
EC, μs/cm	924.0	4,580.0	1,661.5	758.7	41.86
⁠, mg/L	203.3	559.2	340.1	81.6	23.35
⁠, mg/L	12.8	298.3	95.0	58.3	58.26
Cl−, mg/L	27.9	418.2	98.0	75.8	63.50
⁠, mg/L	0.3	222.0	2.4	40.6	219.97
F−, mg/L	0.232	4.372	0.649	0.636	83.68
Ca2+, mg/L	30.6	173.9	83.4	33.1	37.71
Mg2+, mg/L	32.3	213.9	72.8	31.9	40.86
Na+, mg/L	37.8	578.9	153.1	117.8	65.70
K+, mg/L	0.396	6.983	1.69	1.320	66.10
SiO2, mg/L	8.9	21.4	17.31	3.2	19.08
Sr2+, mg/L	0.7	4.3	1.6	0.6	34.41
UV254	0.005	0.051	0.018	0.012	56.69

Parameters .	Minimum .	Maximum .	Median .	Standard deviation .	Coefficient of variance/% .
EC, μs/cm	924.0	4,580.0	1,661.5	758.7	41.86
⁠, mg/L	203.3	559.2	340.1	81.6	23.35
⁠, mg/L	12.8	298.3	95.0	58.3	58.26
Cl−, mg/L	27.9	418.2	98.0	75.8	63.50
⁠, mg/L	0.3	222.0	2.4	40.6	219.97
F−, mg/L	0.232	4.372	0.649	0.636	83.68
Ca2+, mg/L	30.6	173.9	83.4	33.1	37.71
Mg2+, mg/L	32.3	213.9	72.8	31.9	40.86
Na+, mg/L	37.8	578.9	153.1	117.8	65.70
K+, mg/L	0.396	6.983	1.69	1.320	66.10
SiO2, mg/L	8.9	21.4	17.31	3.2	19.08
Sr2+, mg/L	0.7	4.3	1.6	0.6	34.41
UV254	0.005	0.051	0.018	0.012	56.69

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Table 2

Results of bivariate correlation analysis

.	EC .	F− .	Cl− .	SO2−4 .	HCO−3 .	Ca2+ .	K+ .	Mg2+ .	Na+ .	SiO2 .	Sr2+ .	NO−3 .	UV254 .
EC	1.000
F−	−0.564**	1.000
Cl−	0.611**	−0.340*	1.000
	0.680**	−0.337*	0.521**	1.000
	0.127	0.142	−0.409**	−0.121	1.000
	0.464**	−0.807**	0.286*	0.297*	−0.216	1.000
	0.110	−0.371**	0.315*	0.157	−0.165	0.240	1.000
	0.610**	−0.427**	0.234	0.358*	0.231	0.543**	0.082	1.000
	0.803**	−0.209	0.449**	0.523**	0.283*	−0.008	−0.001	0.208	1.000
	−0.618**	0.275	−0.498**	−0.512**	0.054	−0.112	−0.119	−0.069	−0.670**	1.000
	0.454**	−0.400**	0.247	0.198	0.119	0.554**	0.126	0.878**	0.034	0.014	1.000
	0.557**	−0.627**	0.395**	0.396**	−0.139	0.564**	0.346*	0.391**	0.280*	−0.567**	0.283*	1.000
UV254	0.542**	−0.617**	0.431**	0.503**	−0.143	0.469**	0.380**	0.287*	0.349*	−0.541**	0.161	0.652**	1.000

.	EC .	F− .	Cl− .	SO2−4 .	HCO−3 .	Ca2+ .	K+ .	Mg2+ .	Na+ .	SiO2 .	Sr2+ .	NO−3 .	UV254 .
EC	1.000
F−	−0.564**	1.000
Cl−	0.611**	−0.340*	1.000
	0.680**	−0.337*	0.521**	1.000
	0.127	0.142	−0.409**	−0.121	1.000
	0.464**	−0.807**	0.286*	0.297*	−0.216	1.000
	0.110	−0.371**	0.315*	0.157	−0.165	0.240	1.000
	0.610**	−0.427**	0.234	0.358*	0.231	0.543**	0.082	1.000
	0.803**	−0.209	0.449**	0.523**	0.283*	−0.008	−0.001	0.208	1.000
	−0.618**	0.275	−0.498**	−0.512**	0.054	−0.112	−0.119	−0.069	−0.670**	1.000
	0.454**	−0.400**	0.247	0.198	0.119	0.554**	0.126	0.878**	0.034	0.014	1.000
	0.557**	−0.627**	0.395**	0.396**	−0.139	0.564**	0.346*	0.391**	0.280*	−0.567**	0.283*	1.000
UV254	0.542**	−0.617**	0.431**	0.503**	−0.143	0.469**	0.380**	0.287*	0.349*	−0.541**	0.161	0.652**	1.000

*Correlation is significant at the 0.05 level (two-tailed).

**Correlation is significant at the 0.01 level (two-tailed).

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Nitrate in groundwater is naturally found within the environment as part of the nitrogen cycle; however, its ever-increasing concentration makes nitrate a ubiquitous contaminant of natural water resources (Fenech et al. 2012). Agricultural activities, including fertilizer and manure application in farmland, are probably the most significant anthropogenic sources of nitrate in groundwater. Environmental land use conflicts and the arbitrary discharge of factory wastewater and domestic sewage without reasonable treatment are other possible sources of nitrate. UV254 in this paper was used as the surrogate parameter in terms of organic matter content because of its significant correlation with chemical oxygen demand (COD) and total organic carbon, which are the common indicators of organic pollution. As one of the most important petroleum and gas bases in China, Zhongyuan oilfield is located in Puyang County, which makes the infiltration of hazardous organic substances into the groundwater possible. Therefore, Factor 2, which has high loadings on and UV254, reflects the influence of anthropogenic activities. Except for the high and positive loadings on and UV254, Factor 2 also exerts high and negative loading on F⁻. Fluoride in groundwater receives widespread attention because consumption of water with fluoride concentration above 1.5 mg/L results in acute to chronic dental fluorosis and skeletal fluorosis (Brindha & Elango 2011). Fluoride in groundwater is mainly derived from the dissolution of fluoride-bearing minerals, such as fluorspar, fluorapatite, cryolite, and hydroxylapatite, and its concentration is affected by many factors, including the availability and solubility of fluoride minerals, groundwater flow velocity, temperature, pH, and calcium concentration in groundwater. Previous study in China suggests that most groundwaters with high fluoride concentration are located in low-lying areas with slow groundwater runoff, shallow groundwater table, and strong evaporation, which prolong the water–rock interaction and concentrate the fluorine content in groundwater. Therefore, negative loading of Factor 2 on fluoride can be considered as representative of nature groundwater that was unaffected or slightly influenced by the anthropogenic activities, whereas positive loadings of Factor 2 on and UV254 indicate the influence of anthropogenic activities.

Chloride in groundwater is derived from a wealth of sources, of which natural sources include rock–water interactions, seawater intrusion, and minor contributions from atmospheric deposition, and sources related to human activities include road salt, effluent from industrial facilities and municipal septic systems, landfill leachate, and some agricultural chemicals such as potassium chloride (Appelo & Postma 2005). Potassium in natural groundwater can come from the weathering of silicate minerals, orthoclase, microcline, hornblende, muscovite, and biotite in igneous and metamorphic rocks, and the main reason for the increase in potassium concentration is agricultural activities (Hem 1985). Statistical data show that fertilizer usage in Henan province continues to increase year by year. The amount of fertilizer consumption in 2010 was about 2.7 times of that in 1990, of which potash fertilizer consumption increased from 100,000 tons in 1991 to 616,000 tons in 2010 (Peng 2012). Chloride potassium is the largest used potash fertilizer, which accounts for over 90% of total potash fertilizer consumption. Excessive application of chemical fertilizer on farmland can cause percolation of nutrients into the groundwater. In the rainy season, surface runoff takes away most of the remaining fertilizer on farmlands, and lateral seepage of fertilizer-rich surface water is a means for nutrients to infiltrate groundwater. Therefore, Factor 4 with high loadings on Cl⁻ and K⁺ represents the effect of agricultural fertilizer activities on groundwater chemical components.

No obvious trend was observed from the spatial distribution of Factor 2 scores, and samples with high scores were mainly concentrated in urban areas, confluence area of rivers, and administrative boundaries in the lower reaches of rivers. In urban areas, concentrated population and factories, which mainly rely on local wastewater treatment plants, usually produce a large amount of industrial wastewater and domestic sewage. When wastewater treatment capacity is lower than local domestic sewage and wastewater discharge rate, urban sewage discharge without sufficient treatment can cause point-source pollution. In the study area, a large amount of domestic sewage from the western region of Puyang City was discharged into the third PQN canal because of lack of adequate infrastructure and wastewater treatment facilities. In the interaction area of Majia River and Zhulonghe River, with a distance of less than 1 km between, high factor scores possibly resulted from infiltration of contaminated river water. The role of stream riparian zones in regulating nutrient transportation has been a major research focus in the past decades (Carlyle & Hill 2001). Due to the allocation mechanism of water resources between Henan and Hebei provinces remaining obscure, regulation sluices over irrigation canals in the administrative boundary are virtually closed throughout the year. Thus, the contaminants are concentrated in this part of the water flow and cause groundwater pollution.

Different from the spatial distribution patterns of Factors 1 and 2, the spatial distribution of Factor 3 was closely related to groundwater level and its flow path. Samples with high scores were mainly located at sites with shallow groundwater depth and high water tables. Such observations were probably obtained because groundwater receives much more recharge from precipitation and YR irrigation water with and Ca²⁺ as the dominant anion and cation, respectively, in shallow groundwater areas. As indicated in previous research (Li et al. 2008), fresh water recharge largely promotes cation exchange process because fresh water supplies large amounts of calcium ions that are readily exchanged with sodium absorbed by clay minerals. Therefore, water samples in fresh water recharge areas usually have a high Na⁺/Cl⁻ ratio (Figure 9), and excess sodium ion can be attributed to the cation exchange process. Compared with samples from the south of Jindi River, samples with high factor scores in the north part close to Zhulonghe River did not receive large amounts of irrigation water, and excess sodium here is mainly due to significant cation-exchange capacity and low permeability of clay-rich soil. Factor 4 scores have a similar spatial distribution pattern to those of Factor 2. Samples with high scores are mainly located in the northmost part of the PYR irrigation district far away from the irrigation gates.

In the YR basin, shallow groundwater depth is from 2 to 8 m. The YR basin is significantly recharged by the lateral seepage of the YR and vertical infiltration of precipitation and irrigation water. The mixing process and dilution effect of the YR reduced groundwater ion concentration, and phreatic water evaporation and transpiration increased groundwater salinity. To prevent groundwater salinization caused by intensive evapotranspiration, groundwater level control and a scientific and reasonable irrigation program is vital for this area. Although significant pollution has not been observed in this area as yet, excessive application of fertilizers and pesticides can potentially threaten groundwater quality. The quality of the YR water also directly affects groundwater quality in this area. In Haihe River basin, which is located away from the YR, groundwater is deeper and lower. A thick vadose zone makes groundwater in this area less susceptible to contamination. However, long-term heavy irrigation still greatly influences groundwater quality to some extent. When the groundwater is contaminated, restoring its quality can be difficult and costly. As the Haihe River basin is located downstream of the irrigation district, irrigation water quality is not only related to the YR water quality, but is also affected by industrial wastewater and domestic sewage that come with the water flow. Due to insufficient sewage treatment capacity in this study region, large amounts of industrial and urban domestic effluents are discharged into natural river courses and artificial irrigation canals without sufficient treatment. River water quality analysis results showed that COD_cr is the most important contaminant in the Majia River, with more than 200 mg/L COD_cr in the Majia River mainstream (Cao et al. 2009).

Long-term irrigation of contaminated water would certainly threaten groundwater quality. To ensure groundwater quality in the Haihe River basin, the local government should first focus their efforts on standardization installation and control of discharge outlets and improve urban sewage treatment capacity and efficiency. In addition, we observed samples with high scores of concentrates at the administrative boundary between Henan and Hebei provinces. Cross-administrative region water pollution has always been a hotspot and problem in cross-boundary basin water resources utilization, water environmental programming, and water pollution prevention and control. Regulation sluices over irrigation canals at the boundary block surface water flow. Thus, contaminated water stagnates in the lower reaches, thereby preventing pollutant degradation and facilitating microbe proliferation. Lateral seepage of contaminated river water brings contaminants from surface water into the underground aquifer. The only way to avoid and protect the groundwater in these regions from contamination is by strengthening cross-regional collaboration, developing water resources, and protecting water quality.

Owing to large amounts of irrigation water not only changing local hydraulic conditions, but also transporting chemical substances in irrigation water and the vadose zone to groundwater aquifers, the groundwater chemistry in an irrigation region is much more complicated than the natural system. Studying the groundwater chemistry characteristics and its controlling geochemical processes is vital to learn the influence of irrigation practices on groundwater and to establish reasonable water resources management measures.

In this study, FA combined with ionic ratio method were used to identify the main geochemical processes controlling groundwater chemistry in PYR irrigation region; approximately three-quarters of the total variance of the data sets was explained by the four extracted factors. Factor 1 is composed of Ca²⁺, Mg²⁺, Sr²⁺, and EC, and reflects natural hydrogeochemical processes and intensity of water–soil/rock interactions. Factor 3, marked by strong correlation of Na⁺ and ⁠, represents another common and important hydrogeochemical processes – cation exchange process. Factors 1 and 3 are considered as natural factors that explained 41.69% of the total variation. Factor 2 is marked by strong correlation of and UV254, which are normally associated with human activities. Factor 4 is composed of Cl⁻ and K⁺, which possibly indicated the influence of chemical fertilizers, especially those made from potash.

The areas affected by identified hydrogeochemical processes were determined by the factor scores of samples. Water–soil/rock interaction, especially dissolution of carbonate minerals were concentrated in the places away from river course and places with deeper groundwater. Cation exchange processes mainly occurred in regions with intensive irrigation activities and shallow groundwater. Therefore, irrigation activities should be encouraged in regions with high water hardness and deeper groundwater. This is because large amounts of fresh irrigation water can not only greatly dilute groundwater ion concentration, abundant Ca²⁺ in irrigation water diverted from the YR can also promote cation exchange process which is an important water-softening approach. In addition, a thick vadose zone can make local groundwater less susceptible to contamination.

Water samples with high Factor 2 and 4 scores are mainly concentrated in urban areas, confluence area of rivers, and administrative borders. This indicates that these regions are in vulnerable eco-environments and need to be paid more attention. In urban areas with a dense population and advanced industry, large amounts of municipal effluents and industrial wastewater beyond sewage treatment capacity are usually produced, which directly results in urban groundwater contamination. To solve this problem and protect urban groundwater resources, local government should make all efforts to improve sewage treatment ability, set up an all-sided emission supervision system, and draw up relevant rewards and punishment measures. For the confluence area of estuaries, pollution mainly results from the infiltration of contaminated river water, therefore the premise of groundwater protection in this area is preventing the river water from contamination. Once the surface water is contaminated, it will be difficult to avoid groundwater pollution. Water pollution in cross-administrative regions has always been a hotspot and should be settled through bilateral friendly negotiation and proper coordination of the higher administrative departments.

The research is supported by the Key Project for the Strategic Science Plan in IGSNRR, CAS (Grant No. 2012ZD003), the National Natural Science Foundation of China (Grant No. 41671027), and the National Basic Research Program (973 Program) of China (No. 2010CB428805).