A study based on high-density data examines the current status and evolution laws of heavy metal environmental capacity in the Bardawu area of the Qinghai-Tibet Plateau

Abstract

As the first ladder of China, the Qinghai-Tibet Plateau has always been known as the “roof of the world”. Its environmental carrying capacity can be estimated more accurately than other regions because of its harsh natural environment, low population density, limited industrial and agricultural development, and low human activities. However, the current ecological risks of Co and threshold research are limited, and there is a lack of awareness of W’s environmental risks. Hence, this study assessed the ecological support potential of the Bardawu region within Dulan County, Qinghai Province, using 7373 soil specimens, determined regional soil baseline measures, and applied the substance equilibrium linear technique along with the ecological aggregate indicator technique to examine the heavy metal content of the soil. A comprehensive evaluation of the environmental capacity and health risks was conducted to provide a reference for pastoral planning. The findings indicated that the cumulative static ecological capacity of the six trace heavy elements in the soil was ranked as follows: Cr > Li > Ni > Cu > W > Co, with W and Co positioned as the final pair. The remaining areas with a high environmental capacity were predominantly found in the study zone. The central sector exhibited diminished environmental capacity in the southwest and northeast and presented a contamination hazard. Land use, soil type, and geological type considerably affected the six elements in the study area at the p < 0.05. The Bardawu region’s mean comprehensive index of soil environmental capacity was 0.98, indicating an intermediate level of environmental capacity and a moderate health risk. This study focuses on the geological context and influence of pastoral activities on the soil, augments the distribution of various elements across the Tibetan Plateau, and suggests preliminary soil governance strategies. The findings of this study lay the groundwork for soil environmental conservation and remediation efforts in highland regions.

Introduction

Soil is a fundamental substrate for crop cultivation contributing to the cyclic movement of substances and energy within land-based ecosystems. The ecosystem’s intrinsic interconnectivity enables soil to act as a transporter and conduit for heavy metals in the air, water, and biotic entities. Trace metal components are invariably introduced into the soil via rock weathering, rainfall, airborne dust accumulation, and anthropogenic activities¹. These processes are typically protracted, latent, irreversible, delayed, or chaotic. The degradation of heavy metals in soil is challenging, and their potential transmission into the food web poses a concrete or potential health hazard to humans². Consequently, soil contamination with heavy metals has garnered considerable attention across various sectors. The concept of soil environmental capacity is defined as the adherence to specified ecological norms over a designated timeframe, aiming to safeguard the productivity and integrity of farm produce without breaching the tolerable loading limit of the soil^3,4. Breaching this threshold results in soil contamination with heavy metals, sparking assorted ecological issues and posing a risk to human well-being^5,6. It is crucial to study the soil’s environmental capacity.

Recently, a growing number of researchers have delved into soil environmental capacity. Initial studies predominantly focused on establishing models to gauge the environmental capacity of soil contaminated with heavy metals. For instance, Yujiepan et al.⁷ adopted techniques such as substance equilibrium linear modeling to estimate the environmental capacities of eight heavy metals, including Hg, As, Cr, Cu, Ni, Pb, Zn, and Cd, across 23 agricultural zones in the northern sector of Zhongshan Municipality. Subsequently, with the operational capability figures from the 24th of 2015 serving as a reference point, they executed an early risk evaluation. They formulated 25 approaches to manage soil contamination with heavy metals, aligning with objectives related to ‘soil safety‘⁷. Zhang et al.⁸ explored the levels and temporal-spatial patterns of Pb, Hg, Cd, Cr, and As in topsoil at a coal chemical site in the Ningxia Autonomous Region, China. The chessboard method obtained topsoil samples and determined heavy metal concentrations. Leaching experiments were used to measure the soil residual rates of the five heavy metals, which were applied to the soil environmental capacity model to predict quantitative changes in heavy metal concentrations. The spatiotemporal distribution of heavy metals over 20 years was estimated using kriging technology⁸.

However, previous studies have been conducted in areas with developed economies and intense human activity, where economic activities are affected by policies. Various human activities and policies make it difficult to predict environmental capacity. In addition to human activities, soil erosion, lithology formation, weathering, and geological processes are natural sources of heavy metal pollution, destroying soil’s ecological balance⁹. Therefore, the study of changes in carrying capacity is complex, causing the accuracy and usability of carrying capacity predictions to be controversial. Therefore, selecting an area with a single human activity for the study is necessary. Owing to its harsh natural environment, the Qinghai-Tibet Plateau has a low population density, limited industrial and agricultural development, and fewer human activities¹⁰. Compared to other regions, the environmental load of the Qinghai-Tibet Plateau can be estimated more accurately.

Co is an essential trace element for human, animal, and plant growth¹¹. An appropriate concentration of Co can promote plant growth, but an excessively high concentration is toxic to plants¹². Although W is a non-essential element, tungstate poses a potential health risk to humans. Tungstate is easily absorbed after oral contact, which may increase its burden on the body^13,14. Consequently, this study presents a novel and creative examination of the Tibetan Plateau, focusing on the ecological load capacity of a variety of soil-borne heavy metals, with particular emphasis on Co and W. To date, there have been few comprehensive investigations on the ecological load capacity and alterations of such elements in the soils of the Tibetan Plateau. Through systematic field sampling and detailed laboratory analysis, we provide the first comprehensive assessment of the elemental carrying capacity of Co and W in the soils of this plateau region.

The Material Balance Linear Model, utilized in this study, is particularly effective for quantifying the static and dynamic environmental capacities of heavy metals in soils¹⁵. This model is based on the principle that the environmental capacity of a region is influenced by both natural and anthropogenic factors, allowing for an accurate estimation of the soil’s ability to absorb contaminants while maintaining ecological balance. The Environmental Capacity Index Method, on the other hand, offers a comprehensive evaluation by integrating multiple individual capacity indices. This method is well-suited for assessing the cumulative impact of various heavy metals, providing a holistic view of the soil’s overall capacity and potential risk levels.

Based on these considerations, this study employed the GIS spatial analysis technique, substance equilibrium line approach, and all-inclusive index approach for an in-depth analysis of the environmental threshold of heavy metals in Bardawu. The principal objectives of this study were as follows: (1) To apply the substance equilibrium line approach to estimate the environmental threshold of heavy metals in Bardawu. (2) To implement an all-inclusive environmental index approach to evaluate soil threshold risks along with public health alerts. (3) To provide a benchmark for the environmental threshold of heavy metals in the soil of Bardawu.

Materials and methods

Study area

The study area is situated on the southern edge of the Qaidam Basin within the Burhan Buda Mountains in the eastern section of the Kunlun Mountains, south of Nomuhong. Administratively, it falls under the jurisdiction of Dulan County in Qinghai Province. The area is approximately 100 km west of Golmud, 40 km north of Nomuhong Farm, and 40 km east of Nomuhong Farm. Dulan County spans approximately 220 km, with National Highway 109 passing through the northern part of the study area (Fig. 1). Geographically, it is located between 96°00′00″ and 96°15′04″ east longitude and 36°08′07″ and 36°18′53″ north latitude.

Fig. 1.

Location of the study area. Software: MapGis.

The study area measures 322.1 km², with the terrain generally higher in the south and lower in the north. Geomorphologically, the central and eastern Kunlun Mountains exhibit considerable height differences and severe cutting, forming large undulating and extremely high mountainous areas. And the region supports some cultivation of crops such as barley, wheat, and rapeseed, primarily in areas where the land is arable. Livestock farming, particularly yak and sheep herding, plays a more significant role in the local economy, with pastoralism being a traditional practice.

The study area’s main land-use types include bare land, bare rock, gravel, and sandy land. The primary soil types in the study area include Alpine Desert Steppe, Aridisol, and Saline Soil. The main geological types include the Middle Triassic Yingyunshan Formation and 20 other species, including long rocks.

Sample collection and analysis

The sampling process employed random sampling and grid methods considering the survey area’s natural geography and landscape characteristics. This process adhered to the principle of effectively controlling the catchment area and maintaining a roughly uniform distribution. A total of 7373 sampling points were arranged, spanning a sampling area of 368 km², with an average sampling density of 20.04 points/km². There were 78 repeated sampling points, accounting for 1.1% of the total.

The granularity of the samples was specified as -10 ~ + 60 mesh. A multi-site aggregate sampling approach was used. The samples underwent comprehensive sifting through a set of stainless-steel sieves with 10-mesh and 60-mesh openings, with the particle size captured between − 10 ~ + 60 mesh selected as the definitive sample. The masses of the primary and reserve samples exceeded 150 g. Measures to prevent contamination were applied throughout the samples’ gathering, transportation, and treatment to maintain their wholesomeness and dependability.

X-ray fluorescence spectrometry and inductively coupled plasma mass spectrometry were used as the primary methods in this study, supplemented by atomic fluorescence spectrometry and emission spectrometry to analyze Cr, Li, Ni, Cu, W, and Co. During the analysis, 60 pieces of national Class I water system sediment reference materials were used for quality control.

We calculated the logarithmic difference (△lgC) and standard deviation (λ) of the logarithmic deviation between the measured and standard values. The ΔlgC and λ of all the element monitoring samples were within the allowable monitoring limits, where both (△lgC) were less than 0.10.

This study’s soil Cr, Cu, and Ni screening values are based on the Chinese national standard “Soil Environmental Quality Agricultural Land Soil Pollution Risk Management and Control Standards” (GB15618-2018). The elements Co, Li, and W correspond to the regional screening benchmarks of the US EPA. The risk screening figure (RSL) was used as a hazard benchmark, as shown in Table 1. It is important to note that the table is not included in this response but is expected to be presented in the complete version of this study.

Table 1.

Background and reference values of heavy metals in soil in Bardawu area.

Element	Background Value (mg/kg)	Reference Value (mg/kg)
Co	6.71	23
Cr	16.03	200
Cu	6.81	100
Li	21.59	160
Ni	6.08	100
W	0.60	63

Methods

Establishment of soil background value

Soil geochemical background values refer to the concentrations of substances in the soil that result from natural geological and pedogenic processes, unaffected by any human activities¹⁶. Given that the contents of the six elements Cr, Li, Ni, Cu, W, and Co in the study area essentially follow a normal distribution, this study uses the arithmetic mean (X) to represent the central tendency of the data distribution and the standard deviation (S) to represent the dispersion of data. X ± 2 S represents the range value for the 95% confidence level¹⁷. Using this method, the background values of soil elements in Bardawu area were calculated. This approach provides a robust statistical framework for understanding the distribution and concentration of these elements in soil.

In our study, we utilized regional background values for the analysis, which provides a general estimation of heavy metal concentrations. However, it is important to acknowledge that this approach may introduce certain limitations in the accuracy of the results. Specifically, regional background values may not fully capture the variability in soil composition across different horizons. To improve the reliability of environmental assessments, future studies should consider sampling from the parent material or the lowest soil horizons, as highlighted by Aytop et al.¹⁸. This approach ensures a more accurate determination of geochemical background values and can enhance the robustness of enrichment factor calculations in agricultural areas.

Material balance linear model

1. Static environment capacity.

The static potential of the soil was determined by referencing the soil ecology’s baseline value and pivotal concentration¹⁹. The wider the gap between these two figures, the more extensive the soil’s ecological potential. This gap signifies the soil’s capacity to absorb heavy metals without damaging the ecosystem or surpassing the established safety thresholds.

Soil environmental capacity is often chosen for studies because the parameters for these models are readily available. It provides a quantifiable measure of soil’s ability to sustain its function and productivity in the face of environmental stressors such as heavy metal accumulation²⁰.

1

where Q_s is the static environmental capacity (kg/hm²), M is the soil weight per kilogram of the cultivated layer (2.25 × 10⁶kg/hm²), C_ic is the risk reference value (mg/kg), and C_ib is the soil background value of the pollutants (mg/kg).

• (2)Remaining environmental capacity.

The residual soil capacity is the variance between the maximum soil burden and contamination condition⁴. The computational equation for the residual soil capacity is as follows:

2

where Q_i is the remaining capacity of the soil (kg/hm²), namely, the current ecological capacity for element I in the soil, and C_io denotes the determined concentration of this element in the soil (mg/kg).

• (3)Dynamic environmental capacity.

Soil constituents undergo a dynamic balance. From the outset, soil possesses an inherent value known as the soil baseline value, which is organically established throughout the soil development process. Although it is a component of an artificial nutrient cycle, it exhibits consistent properties.

These contributions are diverse and ongoing. Underground filtration, overland flow, and vegetative absorption diminish these contributions. These losses affect the current composition of soil elements and have implications for subsequent contributions.

As the soil constituents are maintained in this state of dynamic balance, the quantity that the soil can accommodate fluctuates in relation to the soil environmental quality benchmark, given that the soil’s capacity is not fixed²¹.

According to the material balance linear framework, it is postulated that there is a direct correlation between the discharge of soil contaminants and the concentration of soil contaminants. The formula for computing the annual average dynamic capacity is derived through sequential recursion.

3

K represents the retention rate, implying that, after a year of cultivation, the quantity of a specific element in the soil is the aggregate of the previous year’s soil amount and the current year’s addition. The translocation of heavy metals in the soil predominantly involves plant uptake, overland flow, and subsurface infiltration. Studies have indicated that the mobility of heavy metals in the soil is limited, with a retention rate of approximately 90%. It is postulated that the retention rate for diverse elements in the soil is K = 0.9 because of the absence of experimental data for soil K values in the Bardawu territory. Q_n denotes the mean dynamic annual potential (kg/hm²a), and n represents the observation years. Further relative analysis is infeasible if the predetermined duration is insufficient and the modifications are indistinct. Conversely, if the duration is excessive, the forecasted outcomes differ considerably from real measures. Consequently, the dynamic soil potential for Bardawu region was determined to be 15 and 30 years, corresponding to 2036 and 2051, respectively.

Environmental capacity index method

The aggregate index approach was used to assess the soil’s ecological capacity. The proportion of the residual ecological capacity of element i in the soil relative to the constant ecological capacity constitutes the individual ecological capacity index, and the mean of the various individual ecological capacity indices comprises the aggregate ecological capacity index²².

4

5

where P(i) denotes the individual ecological capacity index for heavy metal element i in the soil, PI represents the overall ecological capacity index, and n represents the number of elements. The categorization criteria for the ecological capacity of heavy metals are presented in Table 2.

Table 2.

Classification standards of PI on environmental capacity.

Level	Capacity level	Pi	PI	Risk level
1	High capacity	Pi > 1	PI > 1	No risk
2	Medium capacity	0.7 < Pi ≤ 1	0.7 < PI ≤ 1	Mild risk
3	Low capacity	0.3 < Pi ≤ 0.7	0.3 < PI ≤ 0.7	Moderate risk

Data analysis

This study employed a variety of software tools for data processing, analysis, and visualization. Microsoft Excel 2019 was used for data processing and descriptive statistics of the elements. ArcGIS 10.8 was used to plot the sampling distribution position. The inverse distance weight interpolation method was applied to draw a spatial distribution map of the environmental capacity. The SPSS 26 software was used to conduct single-factor testing and data analysis. This includes examining the significance of the differences, distribution characteristics, and influencing factors of environmental capacity under different land use, soil, and geological types. The confidence level for these tests was set at 0.05. Origin 2021 software was used to draw data relationship diagrams.

These tools collectively enable a comprehensive analysis of the soil environmental capacity, considering various factors, and presenting the results visually intuitively. This multifaceted approach ensures a thorough and robust examination of soil environmental capacity.

Results and discussion

Soil heavy metal environmental capacity

Investigating the carrying capacity of soil units is essential for ensuring environmental sustainability. The pollution characteristics of different heavy metals in soils vary, resulting in differences in their accumulation and dispersion potentials^23,24. The content characteristics of the six heavy metals in the 7373 soil samples from the study area are shown in Table 3. From the perspective of heavy metal content, the six heavy metals from the largest to smallest were Cr, Li, Ni, Cu, Co, and W, with average values of 24.04, 22.49, 10.41, 9.85, 8.71, and 1.28 (mg/kg), respectively. The contents of the six heavy metals did not exceed the risk reference value, but the maximum values exceeded the soil background value in Dulan County, Qinghai Province, indicating that the overall content of elements in the study area was low, and the overall environment was good. The coefficients of variation for Cr, Cu, Ni, and W were 2.34, 1.02, 2.90, and 4.04, respectively. Their degree of dispersion is relatively high and they exhibit strong variation, indicating that there is a certain degree of surface enrichment for these four elements, especially W.

Table 3.

Statistical analysis of heavy metal content (mg/kg).

Element	Minimum	Maximum	Mean	Standard deviation	Coefficient of Variation
Co	0.0	101.0	8.7	6.7	0.8
Cr	5.8	2175.0	24.0	56.1	2.3
Cu	2.1	125.0	9.9	10.0	1.0
Li	0.0	74.6	22.5	7.0	0.3
Ni	0.0	1235.0	10.4	30.2	2.9
W	0.0	149.0	1.3	5.2	4.0

Current status of static environment capacity

Based on Eq. (2), the constant ecological capacities of Co, Cr, Cu, Li, Ni, and W in the study area soil samples were 36.65, 413.93, 209.67, 311.41, 211.31, and 140.41 kg/hm², respectively. Considering quantitative measures, there were variances in ecological capacity across various heavy metals. Within this range, the constant ecological capacity for Co was comparatively diminished. In contrast, the constant ecological capacities for the remaining five metals were comparatively elevated, and the associated hazards were minor. Tables 1 and 3 show that the average contents of the four elements Co, Cu, Li, and W were relatively close to the soil environmental background values in the Bardawu area of Dulan County, Qinghai Province, whereas the standard deviations of the two elements Li and Ni were relatively small. The average value was slightly higher than the soil background value in the study area. Co, Cr, and Ni had relatively high coefficients of variation. After investigation, it was found that there was an abnormal combination of elemental characteristics in the study area, with Ni as the main element and Co and Cr as supplements. It is speculated that this anomaly is related to rock mass intrusion and W ore in the study area, with a high mineralization potential for W, which has the highest coefficient of variation.

Distribution of remaining environmental capacity

Using Eq. (2) in conjunction with the GIS spatial analysis technique, we derived a geospatial representation of the residual ecological capacity, as depicted in Fig. 2. Significance testing revealed that the residual ecological capacity for the six studied elements, land utilization patterns, soil categories, and geological classifications, all exhibited significant influences at p < 0.05.

Fig. 2.

Remaining environmental capacity of the heavy metals in the soil of the study area. Software: MapGis.

Land use type

There may be differences in elemental content across soils with different land-use types²⁵. The remaining environmental capacity distribution of the six elements in the different land-use types in the study area is shown in Table 4. The average remaining environmental capacity of Co was 32.37 kg/hm². The maximum value of Co was observed in bare land, whereas the minimum value was observed in bare rock and gravel. Low Co values were mainly distributed in the central and southern parts of the study area, which were more concentrated, whereas the distribution in the north was smaller and more scattered. High-value areas were mainly concentrated on the eastern and western sides of the study area, with small amounts scattered on the northern and southern sides. The average remaining environmental capacity of Cr was 393.50 kg/hm². The maximum value of Cr was observed in the bare land, and the minimum value was observed in the bare rock and gravel. Low Cr values were mainly concentrated in the southeast, central, and northwest of the study area. In contrast, high values were mainly concentrated in the west and northeast of the study area. The average remaining environmental capacity of Cu was 202.60 kg/hm². The highest Cu value was observed in bare rock and land, but the highest values in bare land and sandy land were similar. Bare rock and bare land had the lowest values. Areas with low Cu values were mainly concentrated in the northwestern part of the study area. High-value areas were mainly concentrated in the southwest and northeast. The average remaining environmental capacity of Li was 310.14 kg/hm². The highest value of Li was observed in bare soil, whereas the minimum value was observed in bare rock and gravel. The low-value areas of Li were mainly concentrated in the southwest and northeast of the study area, with small amounts scattered in the north. The high-value areas were mainly concentrated in the southern part of the study area, with a small amount distributed in the northern part. The average remaining environmental capacity of Ni was 200.70 kg/hm². The highest value of Ni was observed in bare soil, whereas the minimum value was observed in bare rock and gravel soil. Low Cr values were mainly concentrated in the southeast, central, and northwest of the study area. In contrast, high values were mainly concentrated in the west and northeast of the study area. The average remaining environmental capacity of Cu was 202.60 kg/hm². The highest Cu value was observed in bare rock and land, but the highest values in bare land and sandy land were similar. Overall, elements are primarily concentrated in sandy soils, while their enrichment is less pronounced in bare land. Weathering is the most important and fundamental process in metal contamination, and the elemental content is likely derived from rock weathering. Grazing reduces plant cover, leading to grassland degradation and even desertification, which in turn accelerates the weathering process^26,27.

Table 4.

Distribution table of remaining environmental capacity of soil elements and land use types in the study area (kg/hm²).

Soil Use Type	Co	Cr	Cu	Li	Ni	W
Bare land	38	416	213	313	211	140
Bare rock, gravel land	31	392	201	309	200	139
Sandy land	29	373	194	309	191	136
Mean	32	394	203	310	201	138

Soil type

Elements exhibit different mobility and adsorption characteristics in various soil types²⁸. The remaining environmental capacity distribution tables for the six elements in different soil types are listed in Table 5. The average remaining environmental capacity of Co was 31.78 kg/hm². The maximum value of Co was observed in the alpine desert steppe soil, and the minimum value was observed in the salinized Zongzei soil. The average remaining environmental capacity of Cr was 392.80 kg/hm². The maximum value of Cr was observed in alpine desert steppe soil; however, the difference from the maximum value in the other two soil types was small, and the minimum value was observed in salinized Zongzei soil. The average remaining environmental capacity of Cu was 202.54 kg/hm². The maximum value of Cu was observed in gray-brown desert soil, and the minimum value was observed in salinized Zongzei soil. Li’s average remaining environmental capacity was 308.95 kg/hm². The maximum and minimum values of Li occurred simultaneously in the alpine desert steppe soil. The average remaining environmental capacity of Ni was 200.74 kg/hm². The maximum value of Ni was observed in alpine desert steppe soil, and the minimum value was observed in salted calcium rice soil. The average remaining environmental capacity of W was 138.49 kg/hm². The maximum value of W was observed in alpine desert steppe soil; however, the difference between the maximum value and the other two soil types was small, and the minimum value was observed in salinized Zongzei soil. Generally, elements are enriched in salinized Zongzei soil and less enriched in alpine desert steppe soil. The soil parent material of the salinized Zongzei soil is alluvial deposits, which further shows that the elements Co, Cr, Li, Ni, and W originated from the geological background. Salinized Zongzei soil is used as a pasture for camels and goats, as well as for artificial grazing, and has an impact that cannot be ignored.

Table 5.

Distribution table of remaining environmental capacity of soil elements and soil types in the study area (kg/hm²).

Soil type	Co	Cr	Cu	Li	Ni	W
Alpine desert steppe soil	34	406	206	311	206	140
Gray-brown desert soil	32	393	205	309	202	138
Salted brown calcium soil	30	379	197	306	194	137
Mean	32	393	203	309	201	138

Geological type

The natural weathering of geological formations is a significant source of heavy metal contamination in soils²⁹. The remaining environmental capacity distribution tables for the six elements in different geological types are listed in Table 6. The average remaining environmental capacity of Co was 28.78 kg/hm². The maximum value of Co was observed in the Middle Triassic porphyry monzogranite, and the minimum value was observed in the Middle Triassic quartz diorite. The mean remaining ecological capacity of Cr was 378.31 kg/hm². The maximum value of Cr was observed in the Middle Triassic porphyritic monzogranite, and the minimum value was observed in the Middle Triassic quartz diorite. The average remaining environmental capacity of Cu was 197.82 kg/hm². The maximum value of Cu was observed in the Middle Triassic monzogranite, and the minimum value was observed in the Middle Triassic tonalite diorite. Li’s average remaining environmental capacity was 306.63 kg/hm². The maximum value of Li was observed in the Middle Triassic porphyritic monzonitic granite, and the minimum value was observed in the Middle Triassic monzonitic granite. The average remaining environmental capacity of Ni was 196.29 kg/hm². The maximum value of Ni was observed in the Middle Triassic porphyritic monzogranite and the minimum value was observed in the Middle Triassic quartz diorite. The average remaining environmental capacity of W was 138.07 kg/hm². The maximum value of W was observed in the Middle Triassic porphyritic monzogranite and the minimum value was observed in the Middle Triassic quartz diorite. Generally, elements are mostly enriched in the Middle Triassic quartz diorite and relatively less enriched in other intrusive rocks. The Middle Triassic quartz diorite originated mainly from the study area’s Tielmutuda East and Blackstone Mountains. According to previous research, Ni (Cr) characteristic anomalies exist in Blackstone Shandong, and Ni was found in Tielemutuda. The abnormal characteristics of Co are likely related to rock mass intrusion. At the same time, some scheelite and chalcopyrite were found in the study area. It can be inferred that Co, Cr, Cu, Li, Ni, and W were most likely affected by the geological background³⁰.

Table 6.

Distribution table of remaining environmental capacity and geological types of the soil in the study area (kg/hm²).

Geology	Co	Cr	Cu	Li	Ni	W
Ordovician Qimantage Group	38	423	213	268	213	139
Biotite granite	40	432	215	300	214	140
Jinshuikou Group Marble Formation	31	412	207	316	205	139
Biotite gneiss section of the Jinshuikou Group gneiss formation	23	359	188	315	187	139
Amphibolite gneiss section of the Jinshuikou Group Gneiss Formation	19	344	176	315	181	135
Qiujidongou Formation of Qingbaikou Period	29	375	190	291	186	136
Holocene alluvial deposits	31	390	193	313	201	139
Late Permian diorite	-7	290	180	321	179	141
Late Pleistocene flood alluvial deposits	30	383	208	309	203	136
Late Triassic syenite granite	37	414	208	317	205	139
Lower Pleistocene Qiquan Formation	41	421	216	309	217	141
Early Devonian granodiorite	35	411	197	306	207	136
Early Devonian pyroxene peridotite	22	329	190	307	184	140
Early Devonian gabbro	28	356	202	308	200	128
Xiaomiao Formation of Changcheng system	24	314	152	290	168	138
Middle Triassic porphyritic monzogranite	41	428	216	304	214	141
Middle Triassic monzogranite	33	412	204	311	207	139
Middle Triassic granodiorite	36	412	210	308	211	139
Middle Triassic Quartz Diorite	13	262	187	314	146	136
Middle Triassic tonalite diorite	31	399	205	312	199	140
Mean	29	378	198	307	196	138

Dynamic environment capacity prediction

Given that heavy metals maintain a fluctuating balance within the soil, it is imperative to consider the fluctuating ecological capacity of the soil when examining the ecological capacity related to soil heavy metals³¹. As per Eq. (3), the fluctuating ecological capacity for the six heavy metals in Dulan County, Qinghai Province, over 15 and 30 years was projected. The projections are shown in Fig. 3. Findings concerning fluctuating ecological capacity indicate a reduction in the ecological capacity of the six elements in the study zone to diverse extents, with Cr experiencing the most pronounced diminution, followed by Li. At the same time, Co’s variations are not substantial with only a minor diminution, and the other trio of elements also exhibited declines. This indicates that the intrinsic decontamination capabilities of Co, Cr, Cu, Li, Ni, and W in the study zone were constrained, necessitating immediate intensification of soil pollution mitigation measures. Notably, the ecological capacity of Co in the region remains the most deficient from 2021 to 2051, at merely 5.91 kg/hm², suggesting a more pronounced issue with Co heavy metal contamination than with other elements, warranting particular vigilance. Typically, components such as Co, Cr, Cu, Li, Ni, and W are influenced by geological factors and livestock grazing. This implies that the influence of endogenous sources might escalate³². However, the prospective capacity alteration for Co might be nominal, potentially owing to the beneficial effects of the existing targeted grazing policies. To safeguard the soil ecosystem, it is crucial to implement a holistic enhancement of governance, stringent regulation of assorted sources, a proactive approach to prevention, and all-encompassing mitigation strategies. Considering the variances in retention capacities among various soil types for distinct heavy metals, optimal soil classifications should be chosen for land application, with due consideration for ecological conservation, to facilitate the effective governance of heavy metal contamination.

Fig. 3.

Dynamic environmental capacity in the soil of the study area. Software: Origin.

Comprehensive environmental capacity assessment

The composite index is used to assess potential risks to ecosystems and human health³³. Using Eq. (5), a graphical early alert system was implemented for the individual and aggregate environmental capacity indices of the six heavy metals in the study area, as depicted in Fig. 4. The mean overall soil ecological capacity index within Dulan County, Qinghai Province, was 0.98, indicating an intermediate ecological capacity and moderate associated health hazard. The areas with diminished values of Co, Cr, Cu, Ni, and W predominantly spanned the central and northern sectors of the study area. In contrast, the regions with elevated values were primarily situated in the southwestern and northeastern sections of the area, with less-valued zones for Li primarily in the southwest and northeast. Areas with elevated Li values were found in the central and southern regions. This shows a severe capacity overload phenomenon in the central part of the study area, which deserves special attention. The average capacity indices of Co, Cr, Cu, Li, Ni, and W in the study area were 0.95, 0.98, 0.98, 0.99, and 0.98, respectively, indicating a medium environmental capacity. The corresponding health risks are mild, but their future capacity shows a downward trend; Future weathering processes may intensify; however, implementing well-planned grazing strategies can enhance biodiversity protection, which is an effective measure for the sustainable use of alpine meadow grasslands. Such strategies can help mitigate weathering and reduce the influx of heavy metal elements from weathering sources³⁴. therefore, we cannot take them lightly.

Fig. 4.

Evaluation map of the soil heavy metal environmental capacity. Software: MapGis.

Co had the lowest environmental capacity in the study area and was primarily concentrated in sand, bare rock, and gravel, indicating that natural weathering and erosion had a considerable impact. Co has physiological functions such as stimulating hematopoiesis and participating in coenzyme reactions³⁵. Excessive intake of Co can damage the human respiratory, thyroid, and cardiovascular systems and even cause cancer. However, this problem cannot be overlooked³⁶. From a spatial perspective, the low-value areas of the five elements (except Li) mainly were enriched in the middle of the study area. The investigation found abnormal elemental characteristics of Ni (Cr) and Ni (Co) in the middle of the study area, which has a considerable mineralization potential. It can be inferred that the mineral veins may have caused the elemental enrichment of Co, Cr, Ni, Cu, and W. Overall, the results revealed the distribution characteristics of these elements in the soil and their potential impact on the environment. We found that there were major spatial variations in the carrying capacities of Co and W, which were closely related to topography, geology, and other factors³⁷. This discovery not only provides a new perspective for understanding the distribution of heavy metals in soil on the Tibetan Plateau but also provides useful information for future environmental management and land-use planning.

Land use type

The distribution of the individual environmental capacity indices of the six heavy metals in different land use types in the study area is shown in Table 7. There were certain differences in the indices of the different heavy metal elements for different land-use types. The environmental capacity index of the bare land was the highest, with an average value of 1.03. In bare land, the effects of natural weathering and erosion were the weakest, and rock formations were less exposed. Therefore, there is an excess in environmental capacity. However, it is necessary to strengthen afforestation to delay the continued decline in environmental capacity. The environmental capacity index of sandy land was the lowest, with an average value of 0.93. Compared with bare land, bare rock, and gravel land, sandy land is the most severely weathered, resulting in a lower environmental capacity. Desert greening measures should be actively promoted, and grazing should be planned rationally.

Table 7.

Distribution table of single environmental capacity index of different land use types and heavy metals in the study area.

Land use type	Co	Cr	Cu	Li	Ni	W
bare land	1.11	1.03	1.03	1.01	1.03	1.00
bare rock, gravel land	0.91	0.97	0.97	0.99	0.97	0.99
sandy land	0.84	0.92	0.94	1.00	0.93	0.97
Mean	0.95	0.97	0.98	1.00	0.98	0.99

Soil type

The distribution of the individual environmental capacity indices of the six heavy metals in the different soil types in the study area is shown in Table 8. There were certain differences in the indices of the different heavy metals among the different soil types. The alpine desert grassland soil environmental capacity index was the highest, with an average of 1.00. Although artificial grazing and natural weathering may influence alpine desert grassland soils, the soil is relatively intact, rocks are less exposed, and vegetation coverage is relatively high. Therefore, environmental capacity is relatively healthy. The environmental capacity index of the salinized Zongze soil was the lowest, with an average value of 0.95. The parent material of the salinized Zongze soil was an impact-flood material. It is speculated that element enrichment may have been caused by ancient river movements bringing out underground elements, and large-scale human grazing in modern times. This results in a lower environmental capacity.

Table 8.

Distribution table of single environmental capacity index of different soil types and heavy metals in the study area.

Soil type	Co	Cr	Li	Ni	W
Alpine desert steppe soil	0.99	1.00	1.00	1.01	1.00
Gray brown desert soil	0.94	0.97	1.00	0.99	0.99
Salted brown calcium soil	0.88	0.94	0.99	0.95	0.98
Mean	0.94	0.97	1.00	0.98	0.99

Geological background

The allocations of separate ecological capacity indices for the six heavy metals across the various geological classifications in the study area are presented in Table 9. Distinct variations existed in the indices of various heavy metals across divergent geological classifications. The Middle Triassic porphyritic monzogranite had the highest environmental capacity index, with an average value of 1.06. In the Middle Triassic, quartz diorite had the lowest environmental capacity index, with an average value of 0.77. This may be related to the abnormal combination of Ni in the study area and ore veins.

Table 9.

Distribution table of single environmental capacity index of different geological types and heavy metals in the study area.

Geology	Co	Cr	Cu	Li	Ni	W
Ordovician Qimantage Group	1.12	1.05	1.03	0.86	1.04	0.99
biotite granite	1.18	1.07	1.05	0.97	1.04	1.00
Jinshuikou Group Marble Formation	0.93	1.02	1.00	1.02	1.00	0.99
Biotite gneiss section of the Jinshuikou Group gneiss formation	0.67	0.89	0.91	1.02	0.91	0.99
The amphibolite gneiss section of the Jinshuikou Group Gneiss Formation	0.56	0.85	0.85	1.01	0.88	0.97
Qiujidongou Formation of Qingbaikou Period	0.85	0.93	0.92	0.94	0.91	0.97
Holocene alluvial deposits	0.92	0.96	0.94	1.01	0.98	1.00
Late Permian diorite	-0.19	0.72	0.87	1.03	0.87	1.01
Late Pleistocene flood alluvial deposits	0.90	0.95	1.01	1.00	0.99	0.97
Late Triassic syenite granite	1.09	1.02	1.01	1.02	1.00	0.99
Lower Pleistocene Qiquan Formation	1.22	1.04	1.05	1.00	1.06	1.01
Early Devonian granodiorite	1.02	1.02	0.96	0.98	1.01	0.97
Early Devonian pyroxene peridotite	0.64	0.81	0.92	0.99	0.90	1.00
Early Devonian gabbro	0.82	0.88	0.98	0.99	0.97	0.92
Xiaomiao Formation of Changcheng system	0.71	0.78	0.74	0.94	0.82	0.98
Middle Triassic porphyritic monzogranite	1.20	1.06	1.05	0.98	1.05	1.01
Middle Triassic monzogranite	0.98	1.02	0.99	1.00	1.01	1.00
Middle Triassic granodiorite	1.07	1.02	1.02	0.99	1.03	0.99
Middle Triassic Quartz Diorite	0.37	0.65	0.91	1.01	0.71	0.97
Middle Triassic tonalite diorite	0.93	0.99	0.99	1.01	0.97	1.00
Mean	0.85	0.93	0.96	0.99	0.96	0.99

Uncertainty analysis

Soil environmental capacity is an important basis for assessing soil quality and formulating soil pollution control strategies. However, it is highly uncertain due to multiple influences.

Currently, the soil environmental capacity calculation relies mainly on mathematical models and empirical formulas. However, these methods are often based on simplified assumptions and fail to fully consider the complexity and dynamics of soil; therefore, the calculation results may contain errors. For example, the elemental contents of Cr, Li, Ni, Cu, W, and Co did not follow a normal distribution in this study. At the same time, owing to the complex environment of the Bardawu area, there are few corresponding studies, and there is a lack of unified local risk reference values. Therefore, the background values established in this study may have exhibited a certain degree of uncertainty.

The soil properties included particle size distribution, organic matter content, pH, and cation exchange capacity³⁸. These properties may change because of location, time, soil type, and land use changes. Soil microorganisms have a degradative effect on certain pollutants that affect the environmental capacity of the soil to some extent³⁹. However, the degradation ability of microorganisms is controlled by many factors, such as the type, activity, and quantity of microorganisms, as well as the degradation rate and pathways of pollutants⁴⁰. These factors may affect the dynamic balance of heavy metals in the soil. This study approximated the K value as 0.9, which may increase the error in predicting the dynamic capacity, because of the lack of soil research in the Bardawu area.

Human activities, such as agricultural management, pollutant emissions, and land-use changes, have a major impact on soil environmental capacity. At the same time, changes in the soil’s environmental capacity may affect the effectiveness and sustainability of human activities. The complexity of this interaction increases the uncertainty in soil environmental capacity assessments. The only human activity in the study area was grazing, which significantly reduced the impact of human activities on environmental capacity. In summary, the uncertainty of the soil environmental capacity in this study mainly comes from changes in soil properties, limitations in calculation methods, migration and transformation of pollutants, degradation ability of microorganisms, changes in land-use patterns, and artificial grazing. To better understand and assess soil environmental capacity, it is necessary to strengthen research on its uncertainty and further explore more precise assessment methods and technologies.

Conclusion

In conclusion, this study used GIS spatial assessment, substance equilibrium linear modeling, and a holistic environmental index approach to comprehensively evaluate the ecological capacity of soil heavy metals in Bardawu, Dulan County, Qinghai Province. These findings indicate that the hierarchy of static ecological capacity for the six soil heavy metals in Bardawu, Dulan County, ranks as Cr, Li, Ni, Cu, W, and Co. The stable ecological potential for Cr, Li, Ni, Cu, and W was comparatively substantial with minimal risk; conversely, the stable ecological potential for the heavy metal Co was comparably limited with elevated risk. In general, the areas with a high remaining environmental capacity were mainly distributed in the southwest and northeast of the study area. In contrast, the remaining environmental capacity in the central part of the study area was low, and there was a risk of contamination. The environmental capacity of the six elements declined to varying degrees, among which Cr had the most obvious decline, followed by Li and Co. The fluctuations were not major and only showed a slight decrease. The three residual elements diminished gradually, suggesting that the intrinsic detoxification properties of Co, Cr, Cu, Li, Ni, and W in the soil were constrained, necessitating the reinforcement of soil contamination deterrence and management.

The mean comprehensive soil ecological capacity index in the Bardawu region of Dulan County, Qinghai Province, was 0.98, indicating a moderate ecological capacity and low associated health hazard. The diminished values predominantly occupied the study area’s central region, leading to localized ecological capacity strain. This study avoids areas with developed economies and intense human activities studied previously, selects the Bardawu area of Dulan County, Qinghai Province, which has a single human activity and few changing policies, as the research area, and systematically discusses the contents of the six soil elements. At the same time, this study fills the knowledge gap regarding the carrying capacity of multiple heavy metals in the soil of the Qinghai-Tibet Plateau. It lays the foundation for further in-depth discussions on the plateau ecosystem’s stability, resource management, and environmental protection. This has guiding significance for the sustainable development of the region but also provides a new reference framework for soil heavy metal environment research in plateau areas worldwide.

It should be noted that owing to the scarcity of studies on the soil’s ecological quality in the Qinghai-Tibet area, additional investigations into alternative sources of trace metal elements are required. More data and multiple analyses are needed to derive local standards suitable for the Qinghai-Tibet region and pay attention to human activities. Regarding the influence of heavy metal geochemical characteristics, the ecological environment of the Qinghai-Tibet Plateau should be protected according to the local conditions.

Author contributions

Zeyong Wang , Yingchun Yang and Haoqi Yang wrote the main manuscript text, Yingchun Yang , Haoqi Yang and Qi Tian prepared figures and tables and Qi Tian, Youning Wei and Yao Niu collected all data.All authors reviewed the manuscript.

Funding

This work was supported by the Provincial Geological Exploration Special Funds Project in Qinghai Province. (Number: 2020021021jc012).

Data availability

No datasets were generated or analysed during the current study.

Declarations

Competing interests

The authors declare no competing interests.