
==== Front
Heliyon
Heliyon
Heliyon
2405-8440
Elsevier

S2405-8440(24)13414-7
10.1016/j.heliyon.2024.e37383
e37383
Research Article
Examining ecological risks of metals in downstream river sediments: Researching agricultural carbon emission efficiency in environmental regulation perspective
He Li W0126901@163.com
a
Chang Liu SUD2268@163.com
a
Beibitovich Sansyzbaev Alisher alisher.sansyzbaev@narxoz.k
b
Hanif Muhammad Wasif wasifcui@163.com
c⁎
Cheng Hu 361156871@163.com
d
a School of Economics and Management, Northeast Agricultural University, Harbin, Heilongjiang, 150038, China
b Science Department, Narxoz University NJSC, Almaty, Kazakhstan
c College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing, China
d Department of Environmental Science, Tongji University, China
⁎ Corresponding author. wasifcui@163.com
03 9 2024
15 9 2024
03 9 2024
10 17 e3738323 5 2024
31 8 2024
2 9 2024
© 2024 The Authors
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
This research investigates the ecological impacts of heavy metals in downstream river sediments with a focus on carbon emission efficiency in agriculture under the environmental regulation context. In this study, thirty sediment samples have been taken from the downstream region of the Shichuanhe River located in Xi'an, Shaanxi province, China, to analyze the availability of heavy metals. The results show that there are high ecological impacts in relation to the heavy metals and that impacts the carbon emission efficiency in agriculture directly. The analysis has also found the importance of significant environmental regulations in the management of such risks. This research has offered a unique angle on the relationship between heavy metal pollution and agricultural carbon release and has given important information on the improvement of environmental control measures. The implications highlight the need for the implementation of measures to enhance the policies that will help to prevent the ecological threats that are connected with heavy metals in the agricultural areas and, thus, improve the management of the environment.

Keywords

Agriculture carbon emission
Environmental regulation
Agricultural wastewater
Heavy metal contamination
Risk evaluation
==== Body
pmc1 Introduction

This research intends to assess the rationale that how the heavy metals in the downstream river sediments influence the agricultural carbon emission efficiency and determine the ecological consequences on downstream deposits [1]. Therefore, this study seeks to analyze the contamination levels and the sources to offer an understanding of the proper environmental laws and agricultural practices in the regions of Xi'an and Shaanxi. It is essential to solve these problems to reduce pollution, preserve the environment, and improve the effectiveness of agriculture emissions and associated regulations [2].

The water's silt accumulation may provide crucial information on the quality of the local water supply and environmental contamination. The likelihood of wastewater discharge, gas emissions, solids transfer, and accumulation increases as industrial development spreads close to a river or its environment. These events all add to the river's silt load. The primary input sources are surface runoff and dust deposition, which cause many artificial pollutants to build up in the sediment [3]. Recent research shows areas in the Ganges River basin with higher household and commercial waste flow have higher pollution levels. The release of heavy metal elements into the aquatic environment will change the concentrations of these elements in the sedimentary deposits and the concentrations at the bottom of the marine environment. These elements will accumulate and concentrate due to biological processes, which may have long-term effects on human health and the ecosystem's environmental balance. Therefore, exploring the heavy metal dispersion and contamination patterns in sediments is crucial to understanding the water quality of a particular ecosystem. Heavy metal pollution has severely affected the Weihe River Basin, the most densely populated area in Shaanxi Province and a major industrial and agricultural center [4]. A decline in biotic and abiotic parameters was seen throughout the time, indicating a rise in the basin's Class V water (a water quality indication in which all parameters exceed acceptable limits, rendering the water unfit for human consumption).

The average lead exposure rate was reported, with the most significant quantity being found three years after exposure as opposed to two years earlier [5]. As a result, the river and the surrounding land have been negatively impacted by this destructive environmental catastrophe. The remediation system included sedimentation tanks, a multistage biological oxidation reactor for contract processing, wetland-based filtering, and an ultraviolet sanitization unit. Soil contamination in the vicinity of an agricultural area in Xi'an affects around two-thirds of the Shichuanhe River's downstream zone, where most of the land is used for agriculture [6]. This puts food safety at risk. Studies on heavy metal legacy in river bed sediments around Xi'an have mainly concentrated on significant rivers and semi-urbanized areas. Variability factors were not included in the field experiments for the lower Shichuanhe River, which were primarily focused on sedimentary composition. The evolution of river landscapes and water quality are the main topics of the investigations (Iqbal and Bilal, 2021). Due to this lack of engagement in executing policies, the governance of the Shichuanhe River needs to work on keeping up with the dynamic governance activities that are characteristic of the river. This study aims to investigate the potential for heavy metals to spread and contaminate sediment deposits in the downstream regions of the Shichuanhe River. It concentrates on the seven heaviest metal components: lead, Zinc, arsenic, nickel, and copper. The study is based on earlier research examining these components' properties and distribution [7].

This research has theoretical and practical implications for environmental science and sustainable agriculture. In theory, it enriches the knowledge of the correlation between the heavy metal pollution of river deposits and the efficiency of agricultural carbon emissions. It also provides a new view on how environmental measures can reduce these threats to the environment. Thus, integrating environmental chemistry into the theory of agricultural efficiency improves the existing models by underlining the importance of sediment quality for effective agriculture. In terms of practical application, the study offers policy suggestions for policymakers and environmental managers; it underscored the importance of enhanced ecological legislation and enforcement to tackle the problem of heavy metal pollution in agricultural areas. Thus, the research is useful for the identification of the specific environmental risks and their influence on carbon emissions, which can help to create effective measures for the protection of the environment and agricultural productivity, as well as for the improvement of land and water management.

2 Study area

The study under examination centers on Jingjisichang in Xi'an City, the capital of Shaanxi province, China. This particular climate lies on a continental position and is characterized by a warm, temperate, semi-arid type, where the rain occurs mainly during summer and autumn. In this area, rainfall occurs from 558 to 750 mm, lower than the evaporation rate (ranging from 1000 to 2000 mm per annum). The north-south transition from a hilly to a flat terrain plateau happens in this area [8]. Here, field observations, as well as surface material exposure, show that rock formations are hidden. The hydrology of the regions mentioned especially denounces the Lower Shihong River and its tributary, the Qinghe River, with the most significant impact. The Shichuanhe River, within a 137 km distance and from Tongchuan City as its origin, is one of the largest tributaries of the Weihe River, flowing together at Jiaokou Town in Lintong District. The river is near upstream of Mizi Industrial Park as well, apart from its flow through the most densely populated areas in the middle and lower reaches (H. [9]). These contrasting industrial and residential neighborhood traits suggest the region's specificity. The agricultural land stretches along the plain areas, especially near the foreshore of the Shichuanhe River, which is mostly from loess that formed during the Cenozoic and Quaternary epochs. This agrarian belt provides staple foods [10]. These crops are mainly cultivated. The number of permanent residents on the Shichuanhe River has already reached 920,000 by the end of 2020. The main eco-dilemmas of the area include an over-occurrence of sewage discharge exceeding the current treatment capacities, as well as air and water pollution. Despite new sewage treatment plants and garbage landfills that will be able to handle some 40 % of the liquid and solid waste being generated, there still needs to be more capacity to handle all waste fully.

3 Sampling, materials and methods

3.1 Selection of sampling locations and sampling procedure

3.1.1 Sample location and time

The inquiry involved collecting sediment samples from a site in the lower reaches of the Shichuanhe River, especially around the confluence of the Qinghe and Shichuanhe Rivers, as depicted in Fig. 1. The places chosen for this study were located beside the river with marked muddy water, which had an elevated content of silt in it. The flood season of the Weihe River basin is between June and September. Therefore, the sample collection was purposely arranged for November 2021. It is necessary to note that within this project, five sample sectors were developed, each marked with 4–6 places for sampling based on the corresponding tactical decision. In the end, thirty deposit samples were obtained skillfully by utilizing a compact ground sampling device. These samples were mainly clayey sediments, and after collection procedures, the samples were placed in plastic bags and then taken to the laboratory for further analysis (Fangueiro et al., 2002). Table 1 represents the sample area locations.Fig. 1 Sample cite details a) A segment of the Weihe River in Shaanxi, China. b) The study site is situated along the Weihe River. c) A comprehensive map delineating the research area explicitly emphasizes sample collection points.

Fig. 1

Table 1 Locations of sample rivers.

Table 1River Name	Location/Site considered for sample	
Qinghe River	Qinghe Bridge, Deqing County, Zhejiang	
Beijing Qinghe	
Qinghe Town, Huai'an, Jiangsu	
Shandong Qinghe River	
Shichuanhe River	Xi'an, Shaanxi	
Lantian County, Shaanxi	
Blue Dragon Ridge, Shaanxi	
Weiyang District, Xi'an	
Baqiao District, Xi'an	

3.1.2 Sampling depth

The sediment samples were taken from the surface 0–10 cm layer of the downstream region of the Shichuanhe River. This specific depth was chosen in order to get a sample from the uppermost sediment layers where heavy metals can be expected to concentrate because of water runoff and sedimentation.

3.1.3 Sampling equipment

The samples were taken using stainless steel sediment cores, and hence, there was no contamination at the time of sampling. To minimize cross-contamination, all the equipment was rinsed with deionized water and then washed with acid before any sampling was done. The corers were then air-dried and kept in clean, airtight containers until their use was needed.

3.1.4 Sampling preparation conditions

The sediment samples were collected in 500 ML glass bottles. After collection, the samples were taken into the laboratory and placed inside pre-labeled, airtight polyethylene bags to minimize contact with air and moisture, which could change the chemical characteristics of the samples as shown in Fig. 1(a–c). The samples were then packed in a cooler with ice packs to reduce the chances of biological activity or chemical changes by the time the samples were delivered to the laboratory. Upon arrival, the samples were stored in a refrigerator at 4 °C and were used for analysis.

3.2 Methodology for conducting sample tests and evaluations: statistical process

The enrichment factor (EF) serves as a crucial method for gauging the presence of heavy metals in sediment layers. The formula for determining the EF is equation (1):(1) EF=(CiCref)sample(CiCref)background

Here, Ci Indicates the heavy metal levels found in sediment samples (expressed in μg/g) and Cref Refers to the levels of a chosen standard element at sampling sites. Background levels for various metals such as Arsenic, Mercury, Lead, Cadmium, Copper, Nickel, Zinc, and Iron are set explicitly at 14.00 μg/g, 0.02 μg/g, 20.90 μg/g, 0.14 μg/g, 22.22 μg/g, 29.60 μg/g, 80.30 μg/g, and 2.85 % respectively. Researchers employ the potential ecological risk index (PERI) to determine the ecological threats of heavy metals. This index categorically breaks down the possible environmental risks based on the biological toxicity of these metals. The calculation for PERI is articulated as follows in equation (2):(2) PERI=∑i=1nTmi×CmiCbi

In this equation, Tmi refers to the relative toxicity values assigned to each metal. Cbi denotes the natural, or background, concentrations of the metals in local soil environments—listed as Arsenic 14.0001 μg/g, Mercury 0.03 μg/g, Lead 21.90 μg/g, Cadmium 0.15 μg/g, Copper 22.30 μg/g, Nickel 30.01 μg/g, and Zinc 80.40 μg/g. Cmi is the concentration of each metal as determined by recent measurements.

The sediment samples were shipped back to the lab for one of the first steps, which involved a natural drying protocol using a drying oven. To continue, the gravel size or stone size pieces and the roots throughout was/were carefully removed. The sediment was thoroughly ground to a dust-like consistency, having a size of no more than 75 μm. The sediment samples for measurement were considered accurate, weighing 0.20 g and 0.0001 g. Next, the samples were put into a beaker made of polytetrafluoroethylene with a capacity of 250 mL. In response, a mixture of 24 mL of hydrochloric acid (HCl) with 8 mL of nitric acid (HENO3) was added. After that, the temperature rise began with a temperature within the range of the liquid phase and kept an hour-long dissolution period. After the system was reliable, the two acids of Hydrofluoric acid (HF) that come with the silicone extraction were added. Extending this process, 15 mL of a diluted hydrochloric acid solution (HCl: A) ratio of the H2O (1 g) was added, and a heating process followed until the complete dissolution resulted, which was later filtered.

Through these proceedings, a reference material standard was added, and volumetric flasks adjusted the needed volume of 100 mL. For the elemental analysis, each sample is stirred separately. It allows an accuracy and precision of ±5 %. Specifically to the particularistic heavy metals, the study used the PE-OPTIMA 7000, equipped with the same plasma emission spectroscope with inductive coupling. Considering that heavy metal concentration is significantly distinct within the crop sediment tested samples, this study realized it supervised regional background values and average crustal concentrations as standards for assessing heavy metal levels in this area. Three distinct methodologies were utilized to evaluate heavy metal hazards. It includes the measures that include geochemical accumulation rating, ecological risk potential index, and secondary phase concentration ratios to primary phases (RSPs). The mathematical expressions for these indexes are given below in equations (3), (4), (5):(3) Igeo=log2[Ci/kC0i]

(4) RI=∑i=0nEri=∑i=0n(Tri×CiC0i)

(5) RSP=(F1+F2+F3)/F4

In this research, the term 'Ci' refers to the concentration of heavy metal 'i' represented in the sediment, measured in milligrams per kilogram (mg/kg). The constant 'K' is often assigned a value of 1.5. The word 'C0i' represents the initial level of heavy metal 'i' in a particular location, measured in mg/kg. The toxicity response coefficient for each element is denoted as 'Tir,' a unitless measure that represents the comparative toxicity. The term 'Eir' represents the ecological danger that any particular ingredient may cause and is dimensionless. A certain sample point's prospective ecological risk index is represented by 'RI,' a complete and dimensionless metric [11,12].

4 Empirical findings

4.1 Analysis of test findings and geographical dispersion of heavy metals

The study analysis portrays the quantities of seven primary heavy metals at different samplings in Table 1. A detailed analysis of this information unearths Zinc (Zn), which has a mean concentration of 67.90 mg/kg. However, less is known about Cadmium (Cd) concentration, the least abundant among these elements, with only a slight average concentration of 0.41 mg/kg. The order of heavy metal accumulation, descending from highest to lowest, is as follows: Cu (23.3 mg/kg) > > Cr (66.6 mg/kg) | Ni (22.9 mg/kg) > Zn (67.3 mg/kg) > Cd (0.41 mg/kg) > As (17.9 mg/kg) > Pb (21.1 mg/kg). Compared to outdoor soil levels, the content of these elements is found to be approximately the same or to a level just slightly below; the level of these elements is still outdoors. The critical point is that the level of Pb is commonly lower than that of the soil background. Nevertheless, Cr concentrations are more than the background concentrations and are 0.80–1.07 and 1.07–1.17, respectively, whereas Ni and Zn are 0.76–1.13 and 0.74–1.17, respectively. Therefore, the range at which Co levels are found is 1.19–1.72 higher than the background, whereas Cd levels vary between 1.40 and 3.79 times higher than the background level. Our research resulted in finding the variation coefficient among these seven elements, ranging from 6.3 to 20.8; Cr, Nickel, Arsenic, and Lead show minimal variation (<1o), while Zinc, Copper, and Cadmium depict more variable concentrations (10 < variation <500) (Moll and Froehlich, 2023; Nguyen and Muir, 2018; Chang, Iqbal and Chen, 2023; Orellana et al., 2020).

Along with that, the ANOVA of contents of Nickel, Zinc, Copper, Arsenic, and Lead shows discrepancies between the five sampling points, with Nickel (F = 4.50, P = o.oo7), Copper (F = 3.8112, P = 0.o16), Zinc (F = 6.80, P = o. Nevertheless, there is no significant difference between the Cr and Cd concentrations within the statistical criterion (P > 0.05). Table 2 presents the empirical results of heavy sediment metals contents. Such a distribution of nitrogen and phosphorous in all the areas can be considered uniform enough to point against any strong impact of human activities [13].Table 2 The numerical data of heavy metals content (mg/kg) in sediments.

Table 2Element (mg/kg)	Nickel	Chromium	Copper	Arsenic	Zinc	Cadmium	Lead	
Dry-weight	
Mean	29.9	66.6	23	17.9	67.9	0.41	21.2	
Maximum	37.4	79.2	31.4	22.7	87.4	0.79	27.4	
Minimum	25	59.1	18.6	15.7	55.2	0.27	17.2	
Coefficient Variation	9	6.2	12.5	8.2	10.7	19.7	9.2	
standard deviation	2.7	4.14	2.87	1.47	7.24	0.08	1.96	
Anova P value	0.007	0.12	0.002	0.004	0.002	0.50	0.003	
Anova F-value	4.47	2.02	3.76	2.96	5.73	0.91	3.21	
UCC	20	35	25	1.5	71	0.098	20	
Background Value	33	74	28	13.2	75	0.208	28	
Cao Hai	30.4	39.8	22.8	15.8	316.0	10.33	67.7	
Wei River	32.2	98.0	34.5	11.38	97.8	0.331	39.6	
PEL	48.6	111	149	33	459	4.98	128	
TEL	22.7	43.4	31.6	9.79	121	0.99	35.8	

The Sediment Quality Guideline (SQG) is pivotal in assessing sedimentary pollution. It comprises two critical thresholds: the Threshold Effect Level (TEL) values and the Point of Departure (PD). Pollutant concentrations under the TEL level are usually considered harmless for benthic organisms; however, after exceeding the PEL level, organisms will struggle with toxic impacts. Through analysis, we managed to compare the densities of different components of the environment in the area investigated with the SQG regulations[14]. It is found the mean levels of Copper (Cu) at 23.0 mg/kg, Zinc (Zn) at 67.9 mg/kg, Cadmium (Cd) at 0.41 mg/kg, and Lead (Pb) at 21.23 mg/kg were all below the indicative threshold occurrence total toxic effect (TTELE) level On the other hand, the concentrations of Chromium (Cr) at 66.62 mg/kg, Nickel (Ni) at 22.93 mg/kg, and Arsenic (As) at 17.93 mg/kg belong to the TEL and PEL values, thus demonstrating the partial negative effect on the benthos/benthic organisms.

Fig. 2 tested the study for sampling point CT1 iron that the highest concentration of elements was Ni (22.93 mg/kg), Cu (23.0 mg/kg), Zn (67.9 mg/kg), As (17.93 mg/kg) and Pb (21.23 mg/kg). The UCC existed in concentrations of 1.87, 1.26, 1.23, 15.13, and 1.37 times at that site compared to UCC benchmarks. On the contrary, sampling BD5 reaches 2.26 and 8.07 times the prescribed UCC standards for Ca, which has the highest value; accordingly, CT1 exhibits the second-highest concentration for Cr. These two sites, named Ct1 and Cj5, were detected to be the most effective places for extensive metal concentration (Fig. 2). Topically, CT1 is settled on the crosspoint of the Qinghe and Shichuanhe Rivers, but CJ5 is about where the Shichuanhe and Weihe Rivers meet, indicating the presence of meteorologic precipitation at both sites. The concern for the heavy metal levels in the Qinghe River at the three selected points of the Weihe River did not change much, but the CH3 point was a clear exception, as seen in Fig. 2. Cd, the only exception being CH3, does show lower concentrations at sampling sites when compared with adjacent sites. Heavily polluted Xiangqiao areas and the spatial concentration pattern was relatively sparse. The variability was substantial in the Jiaokou areas, and the other four samples in the transfer direction had a comparatively consistent pattern. In summary, the Xiangqiao region has the lowest average amounts of Cr, Ni, Cu, Zn, As, and Pb. The confluence of the Qinghe River has been identified as a crucial point where heavy metals tend to accumulate, highlighting its importance in the regional distribution of these elements.Fig. 2 Heavy metal content in sediments with highlight on CT1 and CJ5.

Fig. 2

4.2 Findings from different techniques for assessing pollution

4.2.1 Potential ecological risk assessment and the geo accumulation factor

Fig. 3 results show that all sample places have Igeo values ranging from −0.26 to 0.32 and are outside the neutral zone. More than half of them come ahead of zero and enter the 0 < Igeo ≤1.0 range, whether metal sediments are more harmless. Cr goes above ND (0.17–0.95), but it never reaches the moderate pollution level (1.11), and therefore it is in the delicate category [15]. Notably, As and Cd exhibit the interfering effects that represent medium-high and medium, respectively, pollution intensity, with As varying between 2.80 and 3.33 and Cd ranging between 0.89 and 2.43. This put them in the number one position of impactful pollutants compared to others. It is alarming that E tc Syngbc exists over three at half of the sampling area, where the limit value still needs to be reached. The situation is different, as can be seen from Cd. It registers 2.43 and 0.89 Igeo near the Shichuanhe River near Jiaokou Town, respectively. Compared to the comparative parameters of Shaanxi, the geo-accumulation index (GI) of Chromium, Nickel, Copper, Zinc, and Lead are all lower than zero.Fig. 3 Geo-accumulation index ranges for heavy metals.

Fig. 3

Considering the heavy metal's toxicity to the mind, this reflects the main risk problem that occurs due to the inherent toxicity of heavy metals. Hakanson's unified ecological risk classification system involves evaluating the risk to each element, using the Toxicity Response Coefficients he had already calculated. These coefficients are as follows: Zn = 2, Cr = 6, Cu = Pb = Ni = 30, As = 90, and Cd = 99. These values depict the rationales behind the amounts of harm these elements bring to human health because most arsenic compounds are fatal (e.g., As2O3), while others, such as lead, concentrate in children's brains, a big risk of cognitive impairment. So, apart from Cadmium, other sedimentary elements stay in their allowed ranges. In Shaanxi, soil cadmium is considered an importation hazard, with a risk index between 40 and 80. An aggregate potential ecological risk index (RI) constructed for the five surveyed localities indicates all of them as exhibiting a low-risk status (RI < 15o) relative to the regional baseline values ([16]; [18]). On the contrary, the said classifications are Medium risk (150 ≤ RI < 300), with resource usage, energy consumption, and carbon emissions being lower than the benchmark standard.

The area's toxic levels of Cadmium (Cr) and Arsenic (As) are still high; however, metal sediments are classified as moderately elevated (Fig. 4). All analyzed elements exceeded the trends, moving in the unfavorable direction compared to UCC benchmarks, signaling a risk increase. RI is above 300 because of its high natural soil background.Fig. 4 Potential ecological risk indices of heavy metal.

Fig. 4

4.3 Variation in heavy metal speciation

It is important to note that species composition variation depends on the pollution type. Different metal oxoanions and organic complexes exhibit different bioavailability and mobility properties. While the mildly acid-soluble is more mobile for this fraction, the reducible and oxidizable fractions are actors in the scenario, which is the response of this type to environmental changes. Unlike the fraction, which is still being leached and is therefore active in biological environments, the residual fraction, whose molecules are tightly bonded to the rocks' structural network, acts very little. Consequently, it is less likely to cause any harm to the environment [19]. Fig. 5 examines different kinds of heavy metals referred to as speciation. When talking about residual fraction, Cu, Ni, Zn, and Cr are predominant, about 80 % of their respective concentrations in total (Fig. 5).Fig. 5 RSP approach analysis for heavy metals in sediments.

Fig. 5

On the one hand, only 14 % of cadmium ions (Cd) are stable in the divalent chemical form. This implies that almost one-third (86 %) of Cd is in bioavailable fractions (B1 + B2 +B3), for which the relatively soluble fraction also takes up to 72.7 %. This approach is one of the reasons for the high bioavailability of Cd compared to other elements, which further results in more of its plant uptake[20].

Over 95 % of Chromium remains unsolvable in its residual state, and then it produces a green medium. 86 %, 84 %, and 80 % of the ecological burdens of Zinc, nickel, and copper are considered insignificant; thus, environmental issues of low impact are classified as low. The fact that copper's reduction coefficient amounts to 17 % - well beyond those of other metals - is of the essence, suggesting potential aggravation of environmental effects consequently to a change of the conditions. The moderately reducible lead component comprises 33 % of its content, the most among the non-residual fractions. This plays an instrumental role in microorganism survival, whose growth could be halted due to external changes like temperature variation or moisture alteration (Y. [21]). RSP is used in this assessment to evaluate the risk levels of heavy metal fractions through the primary phase to secondary phase ratio. Table 2 illustrates that the medium RSP to cadmium stands at 6.85, the highest on record; thus, it withstands the rest as the most dangerous of the assessed metals. In some places, a Cd value above 10 indicates extreme pollution [17]. Because of its all-over bioavailability and mobility profile, Cadmium is the most dominant element that can potentially pose ecological risks in the area's surface sediment (Table 3). However, there might be highly hazardous constituents existing in trace quantities. Their RSP value being less than one implies that there are just minimal or non-existent ecological issues.Table 3 The results from the RSP (ratios of secondary-phase to primary-phase) approach analysis for heavy metals.

Table 3	Cd	Cr	Cu	Pb	Zn	Ni	
SCX11	4.19	0.05	0.26	0.66	0.18	0.2	
SCX1	3.1	0.04	0.23	0.57	0.15	0.17	
SCG16	5.74	0.04	0.24	0.64	0.18	0.19	
SCG1	10.86	0.07	0.28	0.76	0.21	0.2	
SCH19	7.4	0.06	0.28	0.71	0.2	0.2	
SCH1	10.3	0.06	0.27	0.7	0.2	0.19	
SCT13	7.02	0.05	0.22	0.71	0.18	0.2	
SCT1	7.24	0.05	0.15	0.65	0.13	0.18	
SCJ20	7.95	0.07	0.38	0.75	0.17	0.24	
SCJ8	4.66	0.03	0.18	0.38	0.08	0.15	
AVG	6.85	0.05	0.25	0.65	0.17	0.19	

4.4 Investigation of the origins and pathways of heavy metals

Principal Component Analysis (PCA), an analytical technique, was used to determine the environmental niche of heavy metal elements in sediments. The PCA technique was employed in this study to pinpoint the drivers of the heavy metals captured within the sediment samples from the surveyed area, as shown in Table 3, which details the respective findings. The results show that two main variables pull most of the weight in our pollution mix, with 96.4 percent accounted for overall. These aspects cover the most significant facts. The PCA points to two primary sources of heavy metals, but the difference is not statistically significant according to the outcome (Table 4). The first factor, which emerges due to the noticeable loading of all elements, is the low co-loading of Cadmium, thus indicating significant differentiation from Cadmium compared with other components. The second factor, whether it is merely negative for all elements except Chromium and Cadmium, which are the leads, is Cadmium being the prime constituent [22].Table 4 The outcomes of PCA (principal-component-analysis) for the presence of metal-laden sediments.

Table 4PCA	Ni	Cu	Cr	As	Cd	Zn	Pb	Contribution rate	
First	0.958	0.986	0.77	0.987	0.134	0.966	0.963	76.20 %	
Second	−0.136	−0.137	0.604	−0.064	0.982	−0.194	−0.083	20.20 %	

The factor load diagram in Fig. 6 displays the two main components discovered using PCA. The first factor is the most influential, explaining 76.2 % of the total variance. This component primarily determines metal-laden sediments' distribution and origin (S. H. [23]). Upon examining the points where heavy metals cross with the coordinate axes in Fig. 6, it is evident that Factor 1 primarily consists of Zinc, Arsenic, Nickel, Copper, and Lead. This composition implies a possible similarity in the pollution caused by these five heavy metals. The results account for 20.2 % of the overall variation and are a supplementary regulator of heavy metal distribution (Fig. 6). The presence of Chromium and Cadmium suggests a potential similarity in their origins. Nevertheless, the noticeable difference in the intercepts of these two elements on the diagram indicates that Chromium is linked to a dual-factor influence.Fig. 6 PCA loading diagram for heavy metal in sediments.

Fig. 6

Hierarchical Cluster Analysis (HCA) was used with PCA to comprehend the elemental sources better. HCA has exceptional proficiency in analyzing data by organizing it hierarchically and grouping pieces that share similar sources or accumulations. The results, depicted in Fig. 7, classify the regional components into three separate categories. A significant deviation from the principal component analysis (PCA) findings is the observed correlation between Chromium and Zinc. This link implies that the distribution patterns of Chromium in this region are similar to those of Zinc, suggesting that both elements are affected by comparable environmental or human-related variables.Fig. 7 A hierarchical cluster analysis.

Fig. 7

5 Discussion

5.1 Distribution in space

The distillation of heavy metal content in Xiangqiao Town, as detailed in the Table and shown in Fig. 2, reveals that the current levels are markedly lower in this location. Specifically, rivers simultaneously carry pieces of disintegrating bedrock of their flow routes, mainly concentrated at waterways' confluences. Hence, an example of this is that arsenic (As), lead (Pb), copper (Cu), and Zinc (Zn) concentrations are higher at the Qinghe confluxing area, whereas chromium (Cr), nickel (Ni), and Cadmium (Cd) more at the Shichuanhe confluence area. The stretch of the river from upstream of the two basins up to these confluences represents a relatively constant distribution of heavy metal concentrations. The sediments that contain heavy metals can be highly changed both by natural examples and by human activities. However, at deficient levels of the mixture, such as natural parent materials, it is mainly the inherited composition that controls the content of the mix.

On the other hand, air pollution and greenhouse gas emissions are human-generated factors when the ambient levels are high. At the point downstream of the Qinghe River, near Erlongkou Reservoir, the domestic waste stockpiling phenomenon has been discovered to have accumulated a tremendous amount of resources[24]. At the transition with the Shichuanhe River, impoundment of the reservoir exerts two influences, i.e., water flow and volume reduction. This impaired flow causes the problem of decreased diffusion of heavy metals, which causes favorable conditions for the accumulation of these harmful substances, as in the case of the neighboring environment. Shengjiaokou Town is located at the Xishu River confluence, with 20,000 people. Over-urbanization is a significant issue here. Gateway Town's sewage treatment plant discharges untreated domestic sewage into the river, most of the time during the dry season, increasing heavy metal in the beach sediments. The standpoint among the two convergences presents the numbers of the metal-laden deposits that are not similar to the background value range of the province Shaanxi but with the distribution pattern characteristic of the recent villages and fewer pollution sources.

5.2 The degree of pollution emission

The accumulation of heavy metal pollutants in the studied area is the lowest, and the elemental concentrations are naturally higher than above this level. Even though the area's standard for background values is much higher than the acceptable range set out in the UCC, a significant number of locally polluted areas can be found along the low reaches of the Shichuanhe River that runs through Shaanxi. Comparative assessment of diverse means of appraisal points to the Cd (Cadmium) consideration of which as a high environmental hazard is unanimous among all. Notified, the geo-accumulation and ecological potential risk index indicators show arsenic (As) is a high-concern element[25]. Even though the level of Cd does not get above the Threshold Effect Level (TEL) as the Sediment Quality Guidelines (SQG) require, it can be related to the use of approaches other than the ones SQG. Two major weaknesses of the CQG benchmarks, based on benthic toxicity tests, include the omission of sediment characteristics that may play a role and the possible existence of variations in environmental background.

On the one hand, this GAI measures heavy metals settling in the sediments; on the other, neither of the campaigns considers the toxic effects of associated chemical forms, methylmercury being the most concerning. The potential ecological risk index (ERI) has a distinctive feature that works out each specific toxicity of elements[26]. Thus, it is determined that arsenic (As) and lead (Pb) are hazardous elements with high toxicity coefficients (As = 10; Pb = 5). This is at odds (Zinc (Zn) and Chromium (Cr), which are below Zn) with that of 1 and 2, respectively. As concentrations are above 17.93 mg/kg and Pb (21.23 mg/kg), Cd does not appear as an issue. The RSP (Risk Assessment of Metal Contamination in Rock and Soil Media based on Speciation of Particulate Heavy Metals) concept, regarding the distribution of elements, ensures a dominant RESIDUAL fraction of Cr, despite the disparity in its overall concentration, indicating relatively low risk. In other words, Zn, with its relatively large metalloid soluble character, is determined as having the highest hazard in the area.

5.3 Examination of the source

The geographical heavy metal distribution contamination in the region shows a high concentration, particularly in two locations, namely the town Xiangqiao and the region in between. Curiously, the concentration of heavy metals is lower where two rivers meet. Due to the scattered nature of the cities in this area, pollution mainly originates from diffuse sources, resulting in limited effects on the quality of sediment in the river. Xiangqiao Town and its upstream districts, known for their innovative environmental restoration efforts, have significantly increased their sewage processing capacity from 4ooo to 15ooo m³/day by 2021. In addition, a 50.3 km section of the riverbanks has been restored by removing sludge and planting trees, leading to heavy metal levels that are now lower than the baseline levels in Shaanxi. Nevertheless, despite the continuous environmental mitigation measures at the confluence places, pollution continues to exist in these areas. Principal Component Analysis (PCA) classifies the elements into two separate origins, whereas Hierarchical Cluster Analysis (HCA) proposes a divide into three unique sources. Cadmium (Cd) seems to have a distinct source, whereas Zinc (Zn), Arsenic (As), Nickel (Ni), Copper (Cu), and Lead (Pb) exhibit a consistent origin. The origins of Chromium (Cr) show a complex pattern, closely resembling Zn but having a lower correlation with As, Ni, Cu, and Pb, as illustrated in Fig. 6. The amounts of Zinc, Arsenic, Nickel, Copper, and Lead in Shaanxi are similar to the normal levels found in the environment. However, only As has a higher average concentration. The examination of heavy metal fractionation indicates that more than 60 % of the fraction is residual, suggesting that it results from natural sedimentary buildup rather than human intervention. Studies show a clear relationship between sediment density and the capacity to engross heavy metals. Increasing the sediment content from o.5 to I.5 mg/kg decreases the time it takes for Pb to reach adsorption equilibrium from 90 to 60 min and for Cd from 50 to 40 min. This discovery highlights the impact of silt accumulation at the meeting sites of rivers on the distribution of elements. Only 3.3 % of the sample locations show Chromium (Cr) levels higher than the average background values. This indicates that there is very little external influence on the distribution of Cr, apart from environmental variables.

In contrast, the region has an increased concentration of Cd, which originates from different sources and has a constant spatial distribution, suggesting that it comes from diffuse origins. Due to the area's primary emphasis on agriculture and low levels of industrial pollution, factors such as atmospheric deposition are similar to those seen in the wider Weihe River basin. Nevertheless, the concentration of Cd in this location (0.41 mg kg⁻1) exceeds that in the Weihe River basin, which is more industrialized (o.431 mg/kg). Approximately 63 % of the Cd is soluble in water and is not well mixed with sediments, indicating that agricultural runoff and domestic sewage are the main contributors. Phosphate fertilizers used in agriculture, which include high levels of Cd due to impurities in the raw phosphate ore, significantly increase the concentration of heavy metals in the soil. Research indicates a remarkable 38-fold rise in soil Cd levels over 26 years due to the long-term use of calcium phosphate. A study conducted in New Zealand demonstrates that over fifty years, the levels of extractable Cd in soils fertilized for a lengthy duration increased from 0.39 to 0.85 mg kg⁻1. This finding has raised concerns over the safety of crops grown in these soils. Therefore, phosphate fertilizers play a significant role in increasing Cd levels in agricultural environments. Untreated sewage and trash from domestic waste contribute significantly to the pollution of Cd, resulting in increased levels of Cd in farmlands. The concentration of Cadmium in the area varies from 1.4 to 4.0 times the background value of the soil, with an avg. of 0.50 mg/kg. The highest concentration (0.79 mg kg⁻1) is found in Jiaokou Town, indicating a correlation with home sewage and population density.

5.4 Relevance of findings with previous studies

The analysis of the ecological impacts concerning the presence of heavy metals in Downstream river sediments and its correlation with Agriculture's carbon emission efficiency under the environmental regulation paradigm is a highly significant and multi-dimensional research discipline. Past studies have presented a strong groundwork for these dynamics where researchers have paid attention to issues like contamination of river sediments by heavy metals and the efficiency of agricultural carbon emissions. These aspects can be synthesized new, expanding the literature by investigating how these factors are linked and their combined effects on environmental and agricultural sustainability [27]. This is because heavy metals, which are toxic, persistent, and capable of accumulating in the food chain, are often found in river sediments. Studies have shown that industrial effluents, fertilizers, and pesticides in agriculture and discharging raw sewage in rivers pollute the river bed with heavy metals like lead, Cadmium, mercury, and arsenic. These contaminants are dangerous to the ecosystem, harming aquatic animals and plants, and metal sediments enter the food chain, causing infection in humans. Many papers have been published to describe the spatial distribution, concentration, and sources of heavy metals in river sediments and the associated ecological risks. Although the undertakings of individual and corporate entities recognize these risks, there is awareness of the need to consider them within larger environmental and agriculture systems [28].

At the same time, the indicator of carbon emissions in agriculture has become another critical issue that has been considered a priority to elaborate on climate change and such significant global trends as sustainable development [29]. It is widely known that agriculture also contributes not inconsiderably to the emission of greenhouse gases, and therefore, upgrading carbon emission efficiency is crucial in combating climate change. Earlier studies have thus investigated the practices and technologies that form the so-called 'carbon smart' agriculture; these include precision farming, conservation tillage, and renewable energy. Further, there is evidence of policy and regulatory measures on sustainable agricultural development. Environmental standards are and should remain critical determinants of agricultural production practices and impacts on the earth's atmosphere, including climate change [30]. The combination of these two research fields, namely, the heavy metal ecology of river sediments and the carbon emission efficiency in agricultural production, takes a broad view of the regulation of environmental issues [31]. For this reason, these two pursuits are mutually dependent, especially when analyzing the best approaches and policies for supporting ecological health and the sustainable use of the world's agricultural lands (F. [32]). Earlier literature has suggested an interrelationship between heavy metals and carbon emission efficiency. Yet, with the relatively recent focus on the interlinkage between HHMs and environmental regulation, a focus on understanding the interactions of heavy metals and carbon emission efficiency in the context of examination under environmental regulation is relatively scarce.

Measures regarding controlling heavy metal pollution in riverbeds are of great concern in promoting agricultural activity. For example, limitations on applying fertilizers and pesticides, common causes of heavy metal pollution, may force farmers to use other practices that influence carbon release [33]. The evidence also indicates that such approaches can policy stimulate innovations within the agriculture sector so that there will be improvements in efficient practices to sustainable practices. On the other hand, other regulations, which are an attempt at raising the efficiency of carbon emissions in agriculture, like the bonuses for the usage of renewable energy types or credits for carbon, can affect practices in farming that may decide levels of heavy metal pollutants in the runoff. Furthermore, the examinations have shown that environmental regulations concerning heavy metal risks and carbon emission efficiency firstly depend on the overall combinations of policy measures. Earlier studies support the need to design policies and strategies holistically because there are complex linkages between various components of the environment. For instance, management practices implementing watershed-integrated activities involving water quality and agricultural emissions have produced good results[34]. Implementing those approaches would achieve sustainable development objectives as much as the ecological aspects concerning agricultural production are considered (.

6 Conclusion and implications

6.1 Conclusion

This research focuses on the ecological effects of heavy metals on downstream river sediments, particularly the carbon emission efficiency in agricultural production under the environmental regulation regime. In the current study, thirty sediment samples have been collected from the downstream area of the Shichuanhe River in Xi'an, Shaanxi province, China, to assess the availability of heavy metals. The results demonstrated that the ecological impacts are high in terms of heavy metals and that the affect the carbon emission efficiency in agriculture directly. The research has also identified that substantial environmental regulations play a crucial role in the overall mitigation of such risks. This research has provided a new perspective to the study of heavy metal pollution and agricultural carbon emission and has provided vital information on the enhancement of environmental regulation. The implications underline the necessity of the measures for the improvement of the policies that will contribute to the prevention of the ecological threats which are associated with heavy metals in agricultural lands and, therefore, to the better management of the environment.

6.2 Implications for local farmers to mitigate the identified risks

The following strategies can, therefore, effectively be implemented by local farmers and policymakers to minimize cadmium contamination in river sediments in China. That is why soil and water testing for Cadmium at least once, if not twice a year, is imperative, particularly for local farmers. Knowledge of contamination levels can help inform the choices of what crop to grow and how to cultivate it. Selecting crops with low potential to absorb Cadmium seems appropriate because some crops, such as rice, mobilize Cadmium more than others. This also goes a long way in explaining why it is essential that irrigation water does not contain Cadmium. The intake of clean or treated water can help eliminate Cadmium from getting into the soil and the crops to a large extent. Applying such techniques of organic farming as the application of organic fertilizers can help to minimize the absorption of Cadmium by the crops since the organic matter has the characteristics of reducing the solubility of Cadmium to crops. One of the practical and eco-friendly bio-technological measures to eliminate contaminated soils includes using plants that can remove Cadmium from the soil through a process referred to as phytoremediation. For the policymakers of China, it is crucial to ensure responsible industrial emissions regulation and proper Cadmium reached industrial discharge regulation, as well as the regular use of Cadmium reached fertilizers and pesticide regulation. Maximum allowable concentrations should be implemented to address the issue with Cadmium within the environment. The ministry should provide more incentives by subsidizing the farmers who use best practices that reduce cadmium content, like soil testing, organic farming, and using clean water to prepare irrigation water. Hence, there is a need to educate them through conducting educational programs and workshops to enable the farmers to learn how to avoid and prevent the effects of cadmium contamination. Exposing the farmers to training on using soil choice for crops to be grown, among other essentials, can yield massive results. Stringent measures for monitoring cadmium concentration in the soil and water, periodical checks or surveys, and continuous implementation of laws and regulation minimizes the cases of its contamination. Additional calls for research include more work into discovering new technologies and practices for cadmium removal from contaminated items and products, as well as more concerns about the effects of Cadmium on agricultural activities and human beings in the long run. Through the execution of the above suggestions, farmers and the policymakers in China will be able to minimize the hazards associated with cadmium contamination and improve the security of food production systems in the country.

6.3 Implications for policymakers for carbon emission and environmental regulation

To reduce this risk, the environmental regulation governance has to be strict among the policymakers. Firstly, there must be increased compliance with emission standards and the availability of better technologies that will prevent pollution. In the same way, compliance-related measures, including well-defined and highly prescriptive emission controls, can decrease carbon footprints to a large extent. Second, promoting non-conventional energy use, for example, post-implementation incentives for subsidies or tax exemptions for the use of solar and wind energy and others, are possible ways of encouraging shift. Third, using CCS technologies can assist with emissions mitigation from the existing fossil fuel-based industries. Also, sustainable agriculture measures should be stimulated through policies restricting the application of carbon-emitting inputs and enhancing carbon stock in the soil. The use of intensive monitoring and reporting systems assists in compliance and evaluation of progress in meeting emission targets. The government should also encourage PPPs concerning community advancement in developing technologies and methodologies for reducing carbon emissions. Last but not least, public awareness and the dissemination of knowledge about the reduction of carbon emissions contribute to the creation of culture in organizations. In this manner, it is possible to effectively manage carbon emissions and uplift environmental regulation governance to create a better and more sustainable world.

Data availability statement

The data that has been used is confidential.

CRediT authorship contribution statement

Li He: Writing – original draft. Liu Chang: Writing – original draft, Visualization, Software. Sansyzbaev Alisher Beibitovich: Project administration, Investigation, Conceptualization. Muhammad Wasif Hanif: Writing – review & editing, Software, Methodology, Data curation. Hu Cheng: Supervision, Methodology, Investigation.

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.
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