Abstract

The pollution of heavy metals in irrigation water was related to the management-control of the risk of heavy metals in farmland and the safe production of crops. There were various sources of irrigation water in the suburb farmlands of Xijiang River. We investigated the sources and distribution characteristics of heavy metals in the irrigation water sources on a riverside and inside-dyke farmland of Xijiang River. According to sample test results, it was found that nearly 100% of heavy metals that we were concerned about were detected in the irrigation water sources in the study. The box plot showed that the average content of other elements ranged from 1.65 to 43.75 μg·L−1, and the average level of cadmium was 0.02 μg·L−1 in the studied area. According to quantitative evaluation index and analysis of distribution characteristics, most heavy metals were at relatively low concentration in the irrigation water sources, while mercury content was far greater than other elements, followed by arsenic. Arsenic and mercury had become increasingly prominent problems in local irrigation water sources. River quality was more likely to be affected by river water and agricultural activities, while groundwater quality was more likely to be affected by natural causes. According to single/comprehensive water-quality evaluations, the comprehensive water quality evaluation ranged from level I to level III, but the single-factor index evaluations of mercury in most water samples and arsenic in two groundwater samples were of ‘Inferior’ level. Attention should be paid to the monitoring of mercury and arsenic (in the groundwater) in the studied area. The disposal methods of farmland water inflow and drainage should be increased if necessary, and reducing the influence of human activities on the natural weathering and leaching of heavy metals was an effective measure to avoid risk of agricultural production.

HIGHLIGHTS

  • Nearly 100% of heavy metals that we were concerned about were detected in the irrigation water sources in the studied area in the early busy season of farming.

  • The studied area should reduce the frequency and use of groundwater irrigation.

  • The monitoring of mercury and arsenic should be strengthened immediately in the studied area.

  • Scientific research on the irrigation water sources in the early busy season of farming can react to the maximum anthropogenic influence on irrigation.

Graphical Abstract

Graphical Abstract
Graphical Abstract

INTRODUCTION

Suburb farmlands in the lower reaches of Xijiang River were mostly irrigated by natural water. In recent years, the deterioration of water quality in rivers and lakes has become more and more serious, especially in industrial areas, and living/industrial drainage areas. Landfill leachate, industrial pollution sources and breeding industry were suspected sources of heavy metal pollution in natural water (Zhang et al. 2018a). Arsenic (As), mercury (Hg) and other heavy metals in the study of other natural water bodies of Xijiang River showed a greater risk. According to the recent study of surface water (large area) in the Pearl River Delta (almost all the lower reaches of Xijiang River were in the area), the content of arsenic varied dramatically from 0.2 to 722.5 μg·L−1 (Zhang et al. 2018a). The highest total mercury content in irrigation water in Nanhai District of Foshan City exceeded the Chinese standard of Environmental Quality Standards for Surface Water (China 2002a) by 1.3–6 times (Chen et al. 2011). In the process of agricultural production, the secondary release of chemical fertilizers and pesticides was one of the main factors of heavy metal pollution in the surrounding water system (Du et al. 2005). The water quality of Xijiang River was one of the important reasons for heavy metal pollution of irrigation water (mainly rivers and other natural water) in Foshan City (a big city in the lower reaches of Xijiang River), but industrial wastewater and domestic sewage were the direct causes of water pollution of Xijiang River (Chen et al. 2011). And the local effect of anthropogenic influence continued to deepen in the process of the deterioration of water quality in rivers and lakes (Kumarathilaka et al. 2018) resulting in the increasing potential risk from irrigation using natural water in the area.

Scholars have focused on the purification of sewage and reused water, irrigation risk and its transport mechanism in crops and farmland environment in recent years (Chaney et al. 2004; Fiol et al. 2006; Galletti et al. 2010; Ahsan et al. 2018; Huang et al. 2018; Zhang et al. 2018b), while there have been few trace investigations of heavy metals from irrigation sources in the Xijiang River Basin. The distribution of heavy metals in irrigation water was greatly affected by human activities during the early busy season of farming. This article took the irrigation water sources (river overlying water, groundwater, reservoir overlying water and initial rainwater runoff) on a suburb farmland in the lower reaches of Xijiang River as an example. We fully reported the pollution characteristics of heavy metals in each potential irrigation water source, and preliminarily explored the influence of human activities (other cases with relatively small impact) on the distribution of heavy metals in the irrigation water sources in the studied area. This was expected to provide help for irrigation water management and purification in the lower reaches of Xijiang River.

MATERIALS AND METHODS

Studied area

Agriculture had been a long history in Xijiang River Basin. As a large number of farmlands occupied by industrialization and urbanization, the importance of suburb farmlands along the river rose in the local agricultural supply chain. The lower reaches of Xijiang River were a typical example. The studied area was located at the intersection of rivers in the lower reaches. There were adequate surface water resources, abundant groundwater reserves and large rainfall. The rainfall was mainly concentrated from April to September, the total precipitation accounted for 80% of the annual precipitation, and the total precipitation days accounted for 74% of the annual precipitation days. Among them, the monthly average precipitation and the average monthly precipitation days were the highest in June.

The studied area was located in a town in Foshan City, Guangdong Province. The distribution of the Xijiang River Basin and the layout of the sampling points are shown in Figure 1. River water samples were named as FSJS 1–7, groundwater samples were named as FSDX 1–7, reservoir water samples were named as FSXS 1–7, and rainwater runoff samples were named as FSYS 1–5. The studied area was about 15.74 hm2 and included river beach farmlands and inside-dyke farmlands. The irrigation forms included rainwater storage irrigation, river water direct irrigation, river water storage irrigation and groundwater storage irrigation in the studied area. River water direct irrigation and groundwater storage irrigation were the most common. The main pollution sources included: (a) Land and water transportation, (b) industrial sewage outlet near the sampling point FSJS1, (c) agricultural irrigation and drainage, (d) river water quality, (e) rainwater and rainwater runoff.

Figure 1

Distribution of Xijiang River Basin and layout of sampling points.

Figure 1

Distribution of Xijiang River Basin and layout of sampling points.

Sample collection and pretreatment

The sampling period was in the early busy season of farming (summer), and the stage of preparation, sowing and topdressing as well. After long-term crop absorption, farmland self-purification and environmental purification, base fertilizer applied in the autumn of the previous year moved outward and its impact on the surrounding environment decreased significantly. During the period from May to July, frequent moderate rain and rainy weather had scoured the water environment of the irrigation system. At this time, external influences on the irrigation water environment were the smallest in this year, and this can feed back to some extent the impact of agricultural activities on the river overlying water, groundwater, reservoir overlying water and initial rainwater runoff.

The river water was taken from the overlying water with relatively stable fluctuations. Groundwater was taken from the outlet of the pump pipe (buried about 10 metres deep). The greater the water tables were (the average water table was greater than 7 m), the less was the possibility of pollutants entering, and the risk of external pollution was relatively reduced. The reservoir water samples were collected 0.5–1 h after the water inflow in the reservoir ended. Local rainfall was frequent, and rainwater runoff was a potential irrigation source. The rainwater runoff samples were collected at multiple points from a colored steel panel roof with concave and convex structure beside FSXS3. The surrounding area was open and no one had lived there for a long time, so it can be preliminarily judged that dry and wet atmospheric deposition was the main pollution source. Except for the rainwater runoff samples that were collected in July and August, the rest of the water samples were collected in August (the early busy season of farming). The water source of the inside-dyke reservoir was mainly groundwater, followed by river water, rainwater and rainwater runoff with fewer contributions. The river overlying water came from upstream migration, rainwater, runoff and farmland drainage.

Sample collection and pretreatment: (a) From July to August 2019, through the weather forecast published by the China Weather Network, auxiliary tools were reasonably selected and samples taken. (b) Positioning software assisted sampling. The layout of sampling points was completed through OvitalMap browser (mobile version), then we took samples according to the positioning information successively. The water quality status of the irrigation process was restored as much as possible. Sampling details referred to the Chinese standard of Technical Specification Requirements for Monitoring of Surface Water and Wastewater (China 2002b). The sampling bottles were a batch of 500 ml mineral water bottles; firstly the mouth of the bottle was blocked with fingers when collecting water, and after reaching a certain depth, it was filled with water and the cap was tightened. Finally, it was taken out and stored in a low-temperature box and transported back to the laboratory for pretreatment as soon as possible. (c) The sample testing was outdoor testing (pH value was detected on site) and indoor testing. The water samples were mixed into two parts (n = 2). According to different test content requirements, the pretreatment was completed as soon as possible, stored in a refrigerator at 4 °C, and the sample testing was completed within the specified time. Each water sample was set to be tested three times, and the average content (±standard deviation) was used for analysis.

Reagents and instruments

Water pH was measured with an acidity meter (STARTER 3100, Ohaus Instrument Co., Ltd). Total content of cadmium (Cd), lead (Pb), copper (Cu) and zinc (Zn) was digested using the method with HNO3-HClO4 mixed acid, and determined using an atomic absorption spectrometer (Pin AAcle 900T, PerkinElmer, USA). Total arsenic was digested using the sample pretreatment method, referring to the Chinese standard of Monitoring and Analysis Methods for Water and Wastewater (China 2002c), and determined using an atomic fluorescence spectrometer (AFS-9700, Beijing Haiguang Instrument Co., Ltd, China). Sample pretreatment and testing of total chromium (Cr), nickel (Ni) and mercury referred to the Chinese standard of Monitoring and Analysis Methods for Water and Wastewater (China 2002c).

Quality assurance

The experimental reagents were all high grade pure. The laboratory utensils were soaked in 10% nitric acid for more than 24 h before use, and washed with high-purity water before use. In the experiment, reagent blank and 20% parallel samples were made for each batch of samples. The recovery rate of heavy metal blank or sample standard addition was controlled within 10%. The relative standard error of repeated tests of each sample was less than 2%. The R2 value of the standard curve of the detection items was higher than 0.995, and the data accuracy met the requirements.

Data processing

The statistical analysis of experimental data and the production of data graphs was made based on Microsoft Excel 2016, SPSS 22.0 and Origin 9.0.

Assessment methods

The coefficient of variation (CV) can be used to describe the dispersion degree of each sample: CV < 10% belongs to weak variation, 10% ≤ CV ≤ 100% belongs to moderate variation, and CV > 100% belongs to strong variation (Zhou et al. 2016). The kurtosis is the characteristic number of kurtosis at the average content of the data distribution, which is used to describe the steepness and slowness of the data distribution. The skewness is used to describe the digital characteristics of the asymmetric degree of the data distribution. If its value is less than 0, it is left skewness, and greater than 0 it is right skewness. The Kolmogorov–Smirnov test (K-S test) is used to determine whether there is a normal distribution.

Identification index method

The single/comprehensive water quality identification index method was used to evaluate the water environment quality of irrigation water sources in the studied area, and the method was suitable for surface water evaluation. The grading process referred to the Chinese standard of Environmental Quality Standards for Surface Water (China 2002a). For multiple functions in the same water area, the standard value corresponding to the highest function was used.

The single-factor water quality identification index method (Pi) was composed of an integer and two or three decimal places, and its structure was according to Equation (1):
formula
(1)
The comprehensive water quality identification index method (P) was composed of an integer and three or four decimal places, and its structure was determined by Equation (2):
formula
(2)

In Equations (1) and (2), the symbol content and calculation method of X1, X2, X3 and X4, was based on two works in the literature (Xu 2005a, 2005b) for details.

Primary quantitative evaluation method

Recently, there have been many studies on water-saving rate, water-saving irrigation area and irrigation water utilization coefficient. It was found that appropriate grading of the existing evaluation system was of great help to further research. In order to facilitate the evaluation and quantitative description of water quality, reference was made to Wang et al. (2018) on the classification method of heavy metal pollution in paddy soil. Irrigation water quality limits referred to the Chinese standard of Standards for Farmland Irrigation Water Quality (China 2005). On referring to the Chinese standard of Environmental Quality Standards for Surface Water (China 2002a), a five-point quantitative description method is provided in Table 1. In this, nickel in groundwater refers to the Chinese standard of Standards for Groundwater Quality (China 2017). Since there are no detailed regulations for nickel in the Chinese standards of Standards for Farmland Irrigation Water Quality (China 2005) and Environmental Quality Standards for Surface Water (China 2002a), this article did not quantitatively evaluate nickel in water.

Table 1

Evaluation method of heavy metal contents in irrigation water (μg·L−1)

LevelEvaluationCdAsPbCuZnCrHg
Very low ≤ 2 ≤ 10 ≤ 40 ≤ 200 ≤ 400 ≤ 20 ≤ 0.2 
II Low 2–4 10–20 40–80 200–400 400–800 20–40 0.2–0.4 
III General 4–6 20–30 80–120 400–600 800–1,200 40–60 0.4–0.6 
VI Higher 6–8 30–40 120–160 600–800 1,200–1,600 60–80 0.6–0.8 
High 8–10 40–50 160–200 800–1,000 1,600–2,000 80–100 0.8–1 
VI Inferior > 10 > 50 > 200 > 1,000 > 2,000 > 100 > 1 
LevelEvaluationCdAsPbCuZnCrHg
Very low ≤ 2 ≤ 10 ≤ 40 ≤ 200 ≤ 400 ≤ 20 ≤ 0.2 
II Low 2–4 10–20 40–80 200–400 400–800 20–40 0.2–0.4 
III General 4–6 20–30 80–120 400–600 800–1,200 40–60 0.4–0.6 
VI Higher 6–8 30–40 120–160 600–800 1,200–1,600 60–80 0.6–0.8 
High 8–10 40–50 160–200 800–1,000 1,600–2,000 80–100 0.8–1 
VI Inferior > 10 > 50 > 200 > 1,000 > 2,000 > 100 > 1 

RESULTS AND DISCUSSIONS

pH

During the studied period, river water pH ranged from 7.12 to 7.56, groundwater pH ranged from 7.92 to 8.33, reservoir water pH ranged from 8.47 to 9.12, and rainwater runoff pH ranged from 6.11 to 6.69.

Heavy metal contents of sampling points

The heavy metal contents of each sampling point are shown in Figure 2. The samples were mixed uniformly to the greatest extent before analysis, and the element difference of the same sample may be related to the suspended phase (type and quantity). Nearly 100% of heavy metals that we were concerned about were detected in the irrigation water sources. Except for cadmium, the contents of the other seven elements were mostly in the range of a few to dozens of μg·L−1. On referring to the Chinese standard of Standards for Farmland Irrigation Water Quality (China 2005), mercury exceeded the standard by 74.36% (44 samples), and the maximum content ranged from 3.1 to 4.0 μg·L−1, which exceeded the mercury concentration in most natural water bodies reported at home and abroad. This may be related to farmland drainage and industrial pollutant emissions in the studied area (Yin et al. 2019). Arsenic exceeded the standard by 10.26% (44 samples), and the other elements did not exceed the standard (44 samples).

Figure 2

Heavy metal contents of every sampling point. (Note: ND is less than the detection limits, and it is calculated as 0 μ·L−1 in data processing.)

Figure 2

Heavy metal contents of every sampling point. (Note: ND is less than the detection limits, and it is calculated as 0 μ·L−1 in data processing.)

The box plot of heavy metal contents in the whole of the samples is shown in Figure 3. According to the test results, the median was selected as the average level of heavy metal contents in all irrigation water sources in the area. The box plot shows that the contents of cadmium, arsenic, lead, copper, zinc, chromium, nickel, mercury were 0.02 μg·L−1, 43.75 μg·L−1, 22.94 μg·L−1, 9.50 μg·L−1, 10.38 μg·L−1, 2.69 μg·L−1, 3.25 μg·L−1, 1.65 μg·L−1, respectively.

Figure 3

Box plot of heavy metal contents in all samples.

Figure 3

Box plot of heavy metal contents in all samples.

Characteristics of heavy metal contents in the river overlying water

Comparing Tables 1 and 2, cadmium, lead, copper, zinc and chromium were at ‘Very low’ level, arsenic was at ‘High’ level and mercury was at ‘Inferior’ level. No other heavy metals exceeded the standard except for arsenic and mercury. The standard-exceeding ratio of arsenic was 14.29% and the standard-exceeding rate of mercury was 64.29%. The standard-exceeding points were mostly concentrated in areas with more irrigation and drainage activities. During the sampling period, the sampling point of FSJS2 was river water direct irrigation. The analysis of the pipeline effluent is shown in Table 3. In the process of water intake, the heavy metal contents at FSJS2 were relatively lower than that at the other points in the same period, and the heavy metal contents in the pipeline outlet water were obviously higher. Water lifting irrigation reduced the mercury content of the overlying water in the local river. It can be seen that the migration of river water was not the main source of mercury in the river overlying water and it was most likely related to the release of sediments. Jia et al. (2018) have pointed out that mercury entering the water body would be converted into methyl mercury quickly, and would be easily enriched into sediments. He (2010) and Guo et al. (2018) have pointed out that sediments were the input source of mercury in overlying water, and mercury concentration in overlying water was affected by temperature and was higher in summer than in winter. It could be preliminarily judged that the excessive mercury in the local river water may be related to irrigation drainage and release of sediments at high temperature.

Table 2

Descriptive characteristics of heavy metal contents in the river overlying water (n = 14)

ElementsMin/(μg·L−1)Max/(μg·L−1)SD/(μg·L−1)CV/%Limits/(μg·L−1)OSR/%KUSKK-S test
ZP
Cd ND 0.480 0.123 230.01 ≤ 10 0.00 12.92 3.54 0.410 0.000 
As 39.46 56.92 4.53 9.89 ≤ 50 14.29 2.07 1.19 0.196 0.182 
Pb 2.50 31.50 7.89 55.45 ≤ 200 0.00 0.60 0.62 0.098 0.200 
Cu 7.00 16.00 2.51 22.50 ≤ 1,000 0.00 − 0.17 0.15 0.152 0.200 
Zn ND 23.25 6.57 72.37 ≤ 2,000 0.00 0.22 0.57 0.122 0.200 
Cr ND 11.50 3.41 156.66 ≤ 100 0.00 3.62 1.91 0.285 0.003 
Ni ND 8.25 2.65 64.68 − 0.93 0.25 0.122 0.200 
Hg ND 3.80 1.22 67.44 ≤ 1 64.29 − 1.13 0.21 0.119 0.200 
ElementsMin/(μg·L−1)Max/(μg·L−1)SD/(μg·L−1)CV/%Limits/(μg·L−1)OSR/%KUSKK-S test
ZP
Cd ND 0.480 0.123 230.01 ≤ 10 0.00 12.92 3.54 0.410 0.000 
As 39.46 56.92 4.53 9.89 ≤ 50 14.29 2.07 1.19 0.196 0.182 
Pb 2.50 31.50 7.89 55.45 ≤ 200 0.00 0.60 0.62 0.098 0.200 
Cu 7.00 16.00 2.51 22.50 ≤ 1,000 0.00 − 0.17 0.15 0.152 0.200 
Zn ND 23.25 6.57 72.37 ≤ 2,000 0.00 0.22 0.57 0.122 0.200 
Cr ND 11.50 3.41 156.66 ≤ 100 0.00 3.62 1.91 0.285 0.003 
Ni ND 8.25 2.65 64.68 − 0.93 0.25 0.122 0.200 
Hg ND 3.80 1.22 67.44 ≤ 1 64.29 − 1.13 0.21 0.119 0.200 

Notes: ND is less than the detection limit, SD means standard deviation, CV means coefficient of variation, KU means kurtosis, SK means skewness, and similarly below.

Table 3

Average content of heavy metals at FSJS2 and its pipeline effluent (μg·L−1)

Sampling pointsCdAsPbCuZnCrNiHg
FSJS2 0.04 43.05 15.63 12.50 10.63 1.50 2.50 0.65 
Pipe mouth 0.03 43.18 16.00 12.00 13.88 4.88 7.13 2.55 
Sampling pointsCdAsPbCuZnCrNiHg
FSJS2 0.04 43.05 15.63 12.50 10.63 1.50 2.50 0.65 
Pipe mouth 0.03 43.18 16.00 12.00 13.88 4.88 7.13 2.55 

The contents of arsenic and mercury were relatively high. It can be seen from the CV value that arsenic belonged to weak variation. In other words, the arsenic contents did not change much in the river water. Mercury (0.2 ± 0.28 μg·L−1, Figure 2) in the upstream did not exceed the standard. At the sampling points where the farmland drainage was discharged into the river overlying water, the water sample detection exceeded the standard (1.1 ± 0.14–3.35 ± 0.35 μg·L−1). The mercury content (1.75 ± 0.21 μg·L−1) in the sewage discharge outlet downstream exceeded the Chinese standard of Environmental Quality Standards for Surface Water (China 2002a).

It can be seen from the CV value that cadmium and chromium belonged to strong variation. According to the kurtosis values, the mercury distribution was relatively uniform in the river water, and standard exceeding points were not abnormally higher than the average content. Other elements had one or several abnormal distribution points in the river water, and their contents were abnormally higher than the average contents; cadmium especially was the most obvious, and it can be seen that the self-purification effect of river water failed to adjust the local distribution of elements in time. All skewnesses were greater than 0, the tailings were on the left, and it can be seen that the heavy metal contents were very low in the river water. The K-S test showed that cadmium (Z = 0.410, P = 0.000 < 0.05) and chromium were right-skewed distributions, and arsenic, lead, copper, zinc, nickel and mercury were approximately normal distributions, which showed that the river water was affected by external input, while the impact was limited, and the heavy metal contents were mostly low in the river water.

Above all, all the heavy metals in the river water were subject to a certain extent to external influences. The upstream river water was less affected by local drainage and had the lowest arsenic and mercury content. Compared with arsenic, other elements were more affected along the way. Mercury had a trend of enrichment at individual points, which was related to drainage.

Characteristics of heavy metal contents in the groundwater

Comparing Table 1 and Table 4, cadmium, copper, zinc and chromium were at ‘Very low’ level, lead was at ‘Low’ level, and arsenic and mercury were at ‘Inferior’ level. On referring to the Chinese standard of Standards for Groundwater Quality (China 2017), the nickel contents ranged from 0.00 to 19.50 μg·L−1, the average was 7.20 μg·L−1, and this belonged to level II.

Table 4

Descriptive characteristics of heavy metal contents in the groundwater (n = 10)

ElementsMin/(μg·L−1)Max/(μg·L−1)SD/(μg·L−1)CV/%OSR/%KUSKK-S test
ZP
Cd ND 0.080 0.028 92.19 0.00 − 0.82 0.51 0.186 0.200 
As 49.20 184.80 43.98 41.64 90.00 − 0.84 0.54 0.183 0.200 
Pb 40.25 65.00 8.51 17.20 0.00 − 0.77 0.59 0.192 0.200 
Cu 1.75 25.25 7.93 62.70 0.00 − 0.96 − 0.17 0.192 0.200 
Zn 1.50 18.50 6.21 69.61 0.00 − 0.97 0.47 0.133 0.200 
Cr ND 12.00 4.30 93.01 0.00 − 0.94 0.47 0.159 0.200 
Ni ND 19.50 7.16 99.40 − 0.85 0.74 0.185 0.200 
Hg 1.20 3.10 0.60 26.30 100 − 0.42 − 0.63 0.179 0.200 
ElementsMin/(μg·L−1)Max/(μg·L−1)SD/(μg·L−1)CV/%OSR/%KUSKK-S test
ZP
Cd ND 0.080 0.028 92.19 0.00 − 0.82 0.51 0.186 0.200 
As 49.20 184.80 43.98 41.64 90.00 − 0.84 0.54 0.183 0.200 
Pb 40.25 65.00 8.51 17.20 0.00 − 0.77 0.59 0.192 0.200 
Cu 1.75 25.25 7.93 62.70 0.00 − 0.96 − 0.17 0.192 0.200 
Zn 1.50 18.50 6.21 69.61 0.00 − 0.97 0.47 0.133 0.200 
Cr ND 12.00 4.30 93.01 0.00 − 0.94 0.47 0.159 0.200 
Ni ND 19.50 7.16 99.40 − 0.85 0.74 0.185 0.200 
Hg 1.20 3.10 0.60 26.30 100 − 0.42 − 0.63 0.179 0.200 

The main heavy metal contaminants were arsenic and mercury in the groundwater of the studied area. Arsenic content in the groundwater of the studied area was much higher than that in healthy groundwater studied by Cheng et al. (2019). The mercury content of the groundwater was slightly lower than that of the reservoir.

It can be seen from the CV value that all elements belonged to moderate variation. According to the kurtosis value, the spatial distribution of each element was relatively uniform in the groundwater, other elements were closer to uniform distribution except for mercury, and it can be seen that the external influences of groundwater were the same/similar in the studied area. The skewnesses of cadmium, arsenic, lead, zinc, chromium and nickel were greater than 0, the tailings were on the left side, and the contents were relatively low in the groundwater. Copper and mercury skewnesses were less than 0, the tailings were on the right side, and the contents were relatively high in the groundwater. According to the K-S test results, cadmium, arsenic, lead, copper, zinc, chromium, nickel and mercury presented an approximately normal distribution. Considering every evaluation indicator, all the elements were relatively less affected by external influences, mainly by natural influences.

High arsenic groundwater is a global problem, and it can exceed drinking water standards by more than a hundred times due to the influences of groundwater irrigation, changes in agricultural methods and construction of dyke works (Eid et al. 2019). Research on shallow groundwater in the Pearl River Delta found that the total arsenic content ranged from 0 to 560 μg·L−1, and the average was 12.78 μg·L−1, which was subject to industrial pollution sources and the breeding industry (Zhang et al. 2018a). Research on spring water, civilian wells and drilling wells in the Pearl River Delta found that excessive copper and arsenic were from natural sources, lead was basically derived from rock weathering, and lead concentration in some shallow groundwater was abnormally increased due to anthropogenic pollution. Cadmium had no obvious distribution pattern, and the reason for exceeding the standard was likely to be the coexistence of natural and anthropogenic influences (Cheng et al. 2019).

Considering the surrounding environmental characteristics, it could be seen that the problem of arsenic and mercury in groundwater in the studied area might be related to groundwater irrigation, agricultural deposited waste leachate and geochemical migration of the surrounding industrial pollution sources, mainly groundwater irrigation. Considering factors such as the surrounding environment of the pumping wells, installation depth and water table, it could be judged that the groundwater was affected by natural and anthropogenic influences, mainly natural influences.

Anthropogenic sources/anthropogenic activities were the main factors leading to the high regionalization of elements. The high background of the region was an important reference for risk variation, and reducing anthropogenic influences on the natural weathering and leaching of heavy metals was an effective measure to avoid risk from agricultural production in high background regions.

Characteristics of heavy metal contents in the reservoir overlying water

Comparing Table 1 and Table 5, cadmium, lead, copper, zinc and chromium were at the ‘Very low’ level, arsenic was at the ‘High’ level, and mercury was at the ‘Inferior’ level.

Table 5

Descriptive characteristics of heavy metal contents in the reservoir overlying water (n = 10)

ElementsMin/(μg·L−1)Max/(μg·L−1)SD/(μg·L−1)CV/%OSR/%KUSKK-S test
ZP
Cd ND 0.210 0.062 187.68 0.00 9.01 2.95 0.388 0.000 
As 4.19 66.20 21.39 52.85 40.00 − 0.81 − 0.52 0.141 0.200 
Pb 7.00 62.00 18.78 57.96 0.00 − 1.37 0.29 0.187 0.200 
Cu ND 14.00 4.63 50.87 0.00 − 0.17 − 0.91 0.259 0.055 
Zn ND 16.75 6.46 89.75 0.00 − 1.34 0.31 0.189 0.200 
Cr 0.25 11.25 3.52 61.28 0.00 − 0.81 − 0.01 0.128 0.200 
Ni 1.50 18.50 4.99 50.54 0.01 0.12 0.154 0.200 
Hg 0.80 3.40 0.95 51.70 80.00 − 1.32 0.52 0.178 0.200 
ElementsMin/(μg·L−1)Max/(μg·L−1)SD/(μg·L−1)CV/%OSR/%KUSKK-S test
ZP
Cd ND 0.210 0.062 187.68 0.00 9.01 2.95 0.388 0.000 
As 4.19 66.20 21.39 52.85 40.00 − 0.81 − 0.52 0.141 0.200 
Pb 7.00 62.00 18.78 57.96 0.00 − 1.37 0.29 0.187 0.200 
Cu ND 14.00 4.63 50.87 0.00 − 0.17 − 0.91 0.259 0.055 
Zn ND 16.75 6.46 89.75 0.00 − 1.34 0.31 0.189 0.200 
Cr 0.25 11.25 3.52 61.28 0.00 − 0.81 − 0.01 0.128 0.200 
Ni 1.50 18.50 4.99 50.54 0.01 0.12 0.154 0.200 
Hg 0.80 3.40 0.95 51.70 80.00 − 1.32 0.52 0.178 0.200 

It can be seen from the CV value that cadmium belonged to strong variation, and other elements belonged to moderate variation. It can be seen from the kurtosis value that there were one or several distribution points abnormally higher than the average content for cadmium. The spatial distribution of other elements was relatively uniform, nickel distribution was close to the normal distribution, and the distribution of arsenic, lead, zinc, chromium and mercury was closer to the uniform distribution. It can be seen that the arsenic, lead, zinc, chromium and mercury were affected by external influences (groundwater and river water underwent natural influences such as standing still, precipitation and sun exposure), which were relatively close. It can be seen that the skewnesses of cadmium, lead, zinc, nickel and mercury were greater than 0, the tailings were on the left, and the contents of most heavy metals were relatively low. Research points that were abnormally higher than the average may be affected by human/agricultural activities, such as the use of reservoir water to wash vegetable roots and leaves. Some reservoirs were adjacent to the drainage ditch and vegetable stems and other agricultural waste accumulation points. The skewnesses of arsenic, copper and chromium were less than 0, the tailings were on the right side, and the contents of most heavy metals were high. According to the K-S test results, cadmium presented a right-skewed distribution, and arsenic, lead, copper, zinc, chromium, nickel and mercury presented an approximately normal distribution.

Characteristics of heavy metal contents in the initial rainwater runoff

Comparing Table 1 and Table 6, cadmium, arsenic, lead, copper, zinc and chromium were at the ‘Very low’ level, and mercury was at the ‘Inferior’ level.

Table 6

Descriptive characteristics of heavy metal contents in the initial rainwater runoff (n = 10)

ElementsMin/(μg·L−1)Max/(μg·L−1)SD/(μg·L−1)CV/%OSR/%KUSKK-S test
ZP
Cd ND 0.048 0.017 80.03 0.00 1.47 0.60 0.209 0.200 
As 3.66 6.88 1.30 24.11 0.00 − 1.34 − 0.29 0.182 0.200 
Pb 0.00 72.25 30.67 100.56 0.00 − 1.56 0.45 0.211 0.200 
Cu 1.25 7.75 2.65 84.10 0.00 3.89 1.92 0.360 0.033 
Zn 178.50 456.00 110.33 30.65 0.00 2.23 − 1.42 0.305 0.143 
Cr ND 3.75 1.77 107.46 0.00 − 2.83 0.32 0.224 0.200 
Ni ND 1.25 0.55 156.49 1.74 1.53 0.339 0.062 
Hg ND 4.00 1.71 152.88 40.00 2.57 1.69 0.304 0.146 
ElementsMin/(μg·L−1)Max/(μg·L−1)SD/(μg·L−1)CV/%OSR/%KUSKK-S test
ZP
Cd ND 0.048 0.017 80.03 0.00 1.47 0.60 0.209 0.200 
As 3.66 6.88 1.30 24.11 0.00 − 1.34 − 0.29 0.182 0.200 
Pb 0.00 72.25 30.67 100.56 0.00 − 1.56 0.45 0.211 0.200 
Cu 1.25 7.75 2.65 84.10 0.00 3.89 1.92 0.360 0.033 
Zn 178.50 456.00 110.33 30.65 0.00 2.23 − 1.42 0.305 0.143 
Cr ND 3.75 1.77 107.46 0.00 − 2.83 0.32 0.224 0.200 
Ni ND 1.25 0.55 156.49 1.74 1.53 0.339 0.062 
Hg ND 4.00 1.71 152.88 40.00 2.57 1.69 0.304 0.146 

It can be seen from the CV value that lead, chromium, nickel and mercury belonged to strong variation. It can be seen from the kurtosis value that cadmium, copper, zinc, nickel and mercury had one or more distribution points higher than the average content, the spatial distribution of arsenic, lead and chromium was relatively uniform, and the distribution of arsenic was closer to a uniform distribution. It can be seen from the skewness value that, except for arsenic and zinc, the skewnesses of other elements were greater than 0, the tailings were on the left, and the contents of most heavy metals were relatively low. The distribution points that were abnormally higher than the average contents may be affected by external influences, such as: atmospheric deposition mostly including exhaust gas, wastewater and solid waste caused by transportation, industrial production, agricultural activities, residents' daily life, nearby restaurants and tea houses, etc. According to the K-S test results, it can be seen that cadmium, arsenic, lead, zinc, chromium, nickel and mercury presented an approximately normal distribution, and copper presented a right-skewed distribution.

Correlation analysis of heavy metals in the irrigation water sources

The correlation matrix of heavy metal contents of each irrigation water source is shown in Table 7.

Table 7

Correlation matrix of heavy metal contents in the irrigation water sources

TypeElementsCdAsPbCuZnCrNiHg
River water Cd        
As −0.873**       
Pb −0.294 0.717**      
Cu 0.680** −0.273 0.456     
Zn 0.741** −0.340 0.406 0.970**    
Cr 0.872** −0.551* 0.146 0.835** 0.899**   
Ni 0.597* −0.141 0.579* 0.955** 0.968** 0.838**  
Hg 0.613* −0.165 0.559* 0.966** 0.975** 0.848** 0.986** 
Groundwater Cd        
As 0.958**       
Pb 0.973** 0.990**      
Cu 0.955** 0.925** 0.941**     
Zn 0.971** 0.956** 0.970** 0.945**    
Cr 0.995** 0.966** 0.979** 0.961** 0.984**   
Ni 0.968** 0.976** 0.978** 0.915** 0.983** 0.981**  
Hg 0.909** 0.918** 0.908** 0.973** 0.917** 0.920** 0.883** 
Reservoir water Cd 
As 0.575 
Pb 0.685* 0.955** 
Cu 0.523 0.976** 0.899** 
Zn 0.673* 0.941** 0.961** 0.894** 
Cr 0.688* 0.982** 0.986** 0.940** 0.962** 
Ni 0.744* 0.957** 0.945** 0.934** 0.953** 0.978** 
Hg 0.704* 0.926** 0.986** 0.864** 0.961** 0.963** 0.925** 
Rainwater runoff Cd 
As 0.929* 
Pb 0.927* 0.961** 
Cu 0.926* 0.788 0.889* 
Zn 0.881* 0.938* 0.813 0.641 
Cr 0.867 0.953* 0.983** 0.806 0.801 
Ni 0.911* 0.820 0.916* 0.956* 0.663 0.882* 
Hg 0.920* 0.817 0.917* 0.976** 0.659 0.870 0.997** 
TypeElementsCdAsPbCuZnCrNiHg
River water Cd        
As −0.873**       
Pb −0.294 0.717**      
Cu 0.680** −0.273 0.456     
Zn 0.741** −0.340 0.406 0.970**    
Cr 0.872** −0.551* 0.146 0.835** 0.899**   
Ni 0.597* −0.141 0.579* 0.955** 0.968** 0.838**  
Hg 0.613* −0.165 0.559* 0.966** 0.975** 0.848** 0.986** 
Groundwater Cd        
As 0.958**       
Pb 0.973** 0.990**      
Cu 0.955** 0.925** 0.941**     
Zn 0.971** 0.956** 0.970** 0.945**    
Cr 0.995** 0.966** 0.979** 0.961** 0.984**   
Ni 0.968** 0.976** 0.978** 0.915** 0.983** 0.981**  
Hg 0.909** 0.918** 0.908** 0.973** 0.917** 0.920** 0.883** 
Reservoir water Cd 
As 0.575 
Pb 0.685* 0.955** 
Cu 0.523 0.976** 0.899** 
Zn 0.673* 0.941** 0.961** 0.894** 
Cr 0.688* 0.982** 0.986** 0.940** 0.962** 
Ni 0.744* 0.957** 0.945** 0.934** 0.953** 0.978** 
Hg 0.704* 0.926** 0.986** 0.864** 0.961** 0.963** 0.925** 
Rainwater runoff Cd 
As 0.929* 
Pb 0.927* 0.961** 
Cu 0.926* 0.788 0.889* 
Zn 0.881* 0.938* 0.813 0.641 
Cr 0.867 0.953* 0.983** 0.806 0.801 
Ni 0.911* 0.820 0.916* 0.956* 0.663 0.882* 
Hg 0.920* 0.817 0.917* 0.976** 0.659 0.870 0.997** 

Note: **is significantly correlated at level 0.01 (two-sided); and *is significantly correlated at level 0.05 (two-sided).

The correlation of many elements in the river water was significant, indicating that they might have the same source. There was a significant negative correlation between cadmium and arsenic at the P < 0.01 level, and a significant negative correlation between arsenic and chromium at the P < 0.05 level, so it was inferred that they may not have the same source. Considering the farming slack period, arsenic concentration in the river water ranged from 3.22 to 4.35 μg·L−1, and it could be inferred that arsenic concentration increase in the river water was affected by irrigation and drainage, and lead and arsenic were consistent. It could also be preliminarily judged that cadmium and chromium in the river water were relatively less affected by agriculture.

All heavy metals in the groundwater were significantly correlated at the level of P < 0.01, suggesting the sources of heavy metals in the groundwater were stable and were affected similarly by industrial and agricultural activities. Considering the depth of groundwater, it could be inferred that the risk of leaching was not high. Its concentration was relatively higher than other environments and was related to frequent agricultural irrigation. The results showed that the elements in the groundwater tended to be natural sources.

Except for cadmium, other heavy metals in the reservoir water were all significantly correlated at the P < 0.01 level, and it can be seen that heavy metal sources were relatively stable. The preliminary judgment was less affected by external influences, mainly from the migration of water sources. Considering the ‘original’ water sources in the environment, individual elements tended to accumulate and increase.

The heavy metals in the rainwater runoff were mainly composed of air dust particles. The correlation of many elements in the rainwater runoff was significant, indicating that most elements in the rainwater runoff had the same source.

Evaluation of irrigation water quality in the studied area

The evaluation results of each sample are shown in Table 8. The comprehensive water quality evaluation results of each sampling point ranged from level I to level III, which met the requirements for the use of water environment function areas. According to the single-factor water quality identification index method, mercury in each irrigation water source was the main pollution factor and most sampling points had an index greater than 6, and water quality evaluation was ‘Inferior’ level; the arsenic indexes of two groundwater sampling points were greater than 6 and the water quality evaluations were ‘Inferior’ level.

Table 8

Evaluation results of single-factor water quality identification index Pi and comprehensive water quality identification index P at each sampling point

Sample No.Pi
Comprehensive evaluation
CdAsPbCuZnCrHgPLevelEvaluation
FSJS1 1.00 1.90 3.30 2.00 1.00 1.40 6.81 2.510 II Up to standard 
FSJS2 1.00 1.90 3.10 2.00 1.20 1.20 4.60 2.100 II Up to standard 
FSJS3 1.00 1.90 1.80 2.00 1.20 1.00 8.43 2.510 II Up to standard 
FSJS4 1.00 1.90 3.20 1.90 1.20 1.00 8.23 2.610 II Up to standard 
FSJS5 1.20 1.90 3.20 2.00 1.30 1.80 6.11 2.510 II Up to standard 
FSJS6 1.00 4.10 1.70 1.90 1.10 1.00 7.42 2.610 II Up to standard 
FSJS7 1.00 1.80 3.10 2.00 1.30 1.20 4.10 2.100 II Up to standard 
FSDX1 1.00 6.61 5.20 2.00 1.00 1.70 7.82 3.620 III Up to standard 
FSDX2 1.00 4.40 3.80 1.30 1.20 1.20 7.72 2.910 II Up to standard 
FSDX3 1.00 4.90 3.80 2.00 1.30 1.00 7.62 3.110 III Up to standard 
FSDX4 1.00 4.20 5.10 2.00 1.20 2.00 7.02 3.210 III Up to standard 
FSDX5 1.10 6.41 3.80 1.80 1.20 1.30 6.41 3.120 III Up to standard 
FSXS1 1.00 1.60 3.40 1.40 1.00 1.40 4.90 2.100 II Up to standard 
FSXS2 1.00 4.10 5.00 1.50 1.20 1.20 6.61 2.910 II Up to standard 
FSXS3 1.00 4.30 5.10 2.00 1.00 1.90 8.23 3.410 III Up to standard 
FSXS4 1.00 1.90 3.10 2.00 1.30 1.50 7.62 2.610 II Up to standard 
FSXS5 1.10 1.20 3.10 2.00 1.20 2.00 6.00 2.400 II Up to standard 
FSYS1 1.00 1.10 5.00 1.20 2.40 1.30 1.00 1.900 Up to standard 
FSYS2 1.00 1.10 1.00 1.10 2.40 1.40 1.00 1.300 Up to standard 
FSYS3 1.00 1.10 5.40 1.30 2.30 1.00 6.41 2.610 II Up to standard 
FSYS4 1.00 1.10 1.30 1.30 2.30 1.00 9.04 2.410 II Up to standard 
FSYS5 1.00 1.10 3.40 1.80 2.10 1.10 4.10 2.100 II Up to standard 
Sample No.Pi
Comprehensive evaluation
CdAsPbCuZnCrHgPLevelEvaluation
FSJS1 1.00 1.90 3.30 2.00 1.00 1.40 6.81 2.510 II Up to standard 
FSJS2 1.00 1.90 3.10 2.00 1.20 1.20 4.60 2.100 II Up to standard 
FSJS3 1.00 1.90 1.80 2.00 1.20 1.00 8.43 2.510 II Up to standard 
FSJS4 1.00 1.90 3.20 1.90 1.20 1.00 8.23 2.610 II Up to standard 
FSJS5 1.20 1.90 3.20 2.00 1.30 1.80 6.11 2.510 II Up to standard 
FSJS6 1.00 4.10 1.70 1.90 1.10 1.00 7.42 2.610 II Up to standard 
FSJS7 1.00 1.80 3.10 2.00 1.30 1.20 4.10 2.100 II Up to standard 
FSDX1 1.00 6.61 5.20 2.00 1.00 1.70 7.82 3.620 III Up to standard 
FSDX2 1.00 4.40 3.80 1.30 1.20 1.20 7.72 2.910 II Up to standard 
FSDX3 1.00 4.90 3.80 2.00 1.30 1.00 7.62 3.110 III Up to standard 
FSDX4 1.00 4.20 5.10 2.00 1.20 2.00 7.02 3.210 III Up to standard 
FSDX5 1.10 6.41 3.80 1.80 1.20 1.30 6.41 3.120 III Up to standard 
FSXS1 1.00 1.60 3.40 1.40 1.00 1.40 4.90 2.100 II Up to standard 
FSXS2 1.00 4.10 5.00 1.50 1.20 1.20 6.61 2.910 II Up to standard 
FSXS3 1.00 4.30 5.10 2.00 1.00 1.90 8.23 3.410 III Up to standard 
FSXS4 1.00 1.90 3.10 2.00 1.30 1.50 7.62 2.610 II Up to standard 
FSXS5 1.10 1.20 3.10 2.00 1.20 2.00 6.00 2.400 II Up to standard 
FSYS1 1.00 1.10 5.00 1.20 2.40 1.30 1.00 1.900 Up to standard 
FSYS2 1.00 1.10 1.00 1.10 2.40 1.40 1.00 1.300 Up to standard 
FSYS3 1.00 1.10 5.40 1.30 2.30 1.00 6.41 2.610 II Up to standard 
FSYS4 1.00 1.10 1.30 1.30 2.30 1.00 9.04 2.410 II Up to standard 
FSYS5 1.00 1.10 3.40 1.80 2.10 1.10 4.10 2.100 II Up to standard 

Status analysis

The average contents of heavy metals are shown in Table 9 in the irrigation water sources in the studied area. The comparison of the heavy metal contents between the reservoir and the corresponding groundwater showed that there was no significant difference. The average contents of certain heavy metals in individual reservoirs had been greatly reduced compared with the corresponding groundwater. It can be seen that the method of irrigation after a period of precipitation certainly had scientific and practical effectiveness and was one of the simplest and most effective methods for removing heavy metals in irrigation water.

Table 9

Average contents of heavy metals in the irrigation water sources (μg·L−1)

ContentCdAsPbCuZnCrNiHg
River water 0.05 45.80 14.23 11.14 9.07 2.18 4.09 1.81 
Groundwater 0.03 105.62 49.48 12.65 8.93 4.63 7.20 2.28 
Reservoir water 0.03 40.47 32.40 9.10 7.20 5.75 9.88 1.84 
Rainwater runoff 0.02 5.41 30.50 3.15 360.00 1.65 0.35 1.12 
ContentCdAsPbCuZnCrNiHg
River water 0.05 45.80 14.23 11.14 9.07 2.18 4.09 1.81 
Groundwater 0.03 105.62 49.48 12.65 8.93 4.63 7.20 2.28 
Reservoir water 0.03 40.47 32.40 9.10 7.20 5.75 9.88 1.84 
Rainwater runoff 0.02 5.41 30.50 3.15 360.00 1.65 0.35 1.12 

For individual elements exceeding the standard, the addition of pretreatment devices could further optimize all elements (Eid et al. 2019). Taking the study of low concentrations of cadmium as an example, the constructed wetland system treated irrigation influent with an average influent concentration of 0.232 μg/L, which was down between 0.054 and 0.063 μg/L after treatment (Jiang et al. 2019). It can be seen that there was a greater possibility of repurification of trace cadmium in irrigation water.

Taking the purification study of cadmium in the irrigation water as an example, it will bring a chain of ecological benefits: the use of the plant pond constructed wetland system effectively intercepted cadmium in the irrigation water (average content 0.232 μg·L−1), the removal rate reached 72.03%, compared with the unpurified irrigation water, and the cadmium content of brown rice irrigated with optimized water was down between 10.50% and 24.51% (Zhang et al. 2019). The plant pond constructed wetland system effectively intercepted the cadmium-excess in the irrigation water (average content 6.65 μg·L−1) in the typical mining area of Hunan, and the total removal rate of the system reached 87.94%. Compared with the unpurified irrigation water, the cadmium content of brown rice irrigated with optimized water was reduced 0.12 mg·kg−1 (Liu et al. 2019).

CONCLUSIONS

  • (1)

    Irrigation water sources may be slightly polluted by between one and three elements in the lower reaches of Xijiang River and the entire basin. Except for arsenic and mercury, other elements met the irrigation requirements and were at a relatively safe ‘Very low’ level in the irrigation water sources. The heavy metals were mostly in low concentrations in the irrigation water sources, and sampling points that were abnormally higher than the average contents may be affected by agriculture-related activities. Agricultural irrigation drainage and groundwater pumping were the main reasons for the relatively high mercury content of corresponding water sources.

  • (2)

    The comprehensive water quality evaluation results of each sampling point ranged from level I to level III, which met the requirements for the use of water environment functional areas. It should be noted that the mercury contents of most water samples and arsenic contents of two groundwater samples were relatively high, and the single-factor index evaluation results were ‘Inferior’ level. The simple standing-precipitate of the reservoir could no longer ensure that the arsenic and mercury inflow reached the standard, especially mercury.

  • (3)

    In order to ensure safe irrigation water in the agricultural production process, it is suggested to consider the following two suggestions. For one thing, the monitoring of mercury in most water sources and arsenic in the groundwater should be strengthened immediately in the irrigation influent; and it is recommended that the local area reduce the frequency and use of groundwater irrigation. For another, it is possible to appropriately add a pre-reservoir for irrigation water (such as a plant pond constructed wetland system or a rapid purification device), plant water hyacinths along the way and add irrigation drainage treatment facilities such as ecological ditch constructed wetlands.

DATA AVAILABILITY STATEMENT

All relevant data are included in the paper or its Supplementary Information.

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