Integrated assessment of soil quality and contaminant risks in salinized farmland adjacent to an oil-exploitation zone: insights from the Yellow River Delta

Abstract

Intensified industrial activities significantly threaten farmland soil integrity, particularly in salinized regions. However, comprehensive evaluations of soil fertility and contamination by polycyclic aromatic hydrocarbons (PAHs) remain limited. In this study, we assessed soil quality in China’s Yellow River Delta (YRD) by quantifying 13 indicators of soil physicochemical and biological properties, along with 11 PAHs. Our findings reveal that the minimum data set approach provides a robust and comprehensive representation of overall soil fertility. Salinity emerged as the primary limiting factor, with strong correlations between salinity and key ions, highlighting its adverse effects on soil structure and function. Additionally, significant PAH contamination was detected, particularly from benzo[a]anthracene (BaA), fluoranthene (Flu), and chrysene (Chr), as indicated by the Nemerov pollution index. A pronounced negative correlation between the soil quality index (SQI) and the soil environmental index (SEI) underscores the substantial role of PAH pollution in soil degradation. Notably, the SQI integrates both SEI and soil fertility, providing a holistic assessment of soil health. These findings highlight the utility of SQI as a diagnostic tool for evaluating soil degradation and emphasize the need for targeted remediation strategies to address salinity and PAH contamination, thereby promoting soil restoration and agricultural sustainability.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-024-80314-4.

Introduction

Soil salinity is a significant contributor to land degradation and the decline of cultivable land, considerably limiting crop yields in arid and semi-arid region¹. Salt stress has been shown to negatively affect key soil properties, including structure and stability, thereby accelerating soil degradation². Recently, the salinization of agricultural soils in coastal regions has become a growing concern, particularly where irrigation practices lead to salt accumulation in the root zone, negatively impacting soil properties, root development, and crop productivity^3–5. China’s Yellow River Delta (YRD) is one such coastal alluvial area, where rapid soil salinization has been driving land degradation since the 1990s⁶. This salinization is influenced by environmental factors, such as climate, soil type, and hydrology, as well as human activities, including oil extraction and saline aquaculture^7,8.

Among these human activities, oil extraction has further degraded soil quality by introducing polycyclic aromatic hydrocarbons (PAHs), which are persistent organic pollutants that pose severe risks to soil ecosystems⁹. PAHs enter nearby farmlands through oil-related activities and the combustion of fossil fuels, potentially entering the food chain and posing significant health risks^10–12. Due to their hydrophobic nature, PAHs strongly bind to soil organic matter, leading to long-term contamination and deleterious effects on soil quality and fertility^13,14. Moreover, PAH toxicity affects also soil microbial communities, which are crucial for nutrient cycling and organic matter decomposition, further impairing nutrient availability and soil productivity. PAH contamination can also directly inhibit root growth and nutrient uptake, exacerbating reductions in crop yields.

Given the combined challenges of soil salinity and PAH contamination in the YRD, understanding the extent of soil quality degradation is critical. Soil quality is a key indicator of ecosystem health and land degradation, and various indices have been developed to assess it in agricultural systems¹⁵. However, current conceptual frameworks and models for assessing soil quality, often based on site-specific and labor-intensive approaches, are limited in broader applicability^16–19. The inherent complexity and variability of soil ecosystems further complicate the development of universally applicable assessment methods. A promising alternative is the minimum data set (MDS) approach, which integrates essential biological, physical, and chemical indicators to evaluate soil fertility and quality²⁰. The MDS has been effectively applied in various studies, including those assessing soil fertility in wetlands and soils contaminated by heavy metals^21,22.

Despite the utility of soil quality indices (SQIs) in evaluating soil under heavy metal stress, there is limited research that comprehensively assesses soil health by integrating soil fertility and PAH contamination through the Nemerov pollution index (NPI). Most studies of PAH contamination have focused on ecological or human health risks^23,24, without offering a complete picture of overall soil quality. The Nemerov Pollution Index (NPI) is broadly applied to assess environmental contamination by PAHs and to quantify their impact on environmental quality^25,26. However, soil quality cannot be accurately assessed through PAH contamination or soil fertility metrics alone. Therefore, a holistic assessment that considers both PAH contamination and soil fertility is essential for evaluating farmland soil health. This study aims to address this gap by evaluating the soil quality of farmland in the YRD, using a combination of biological, physical, and chemical soil properties and assessing PAH contamination. Farmlands near oil extraction sites were selected for this case study, representing areas affected by both salt stress and PAH contamination. Specifically, this study focuses on (a) assessing soil fertility using the MDS approach, (b) evaluating PAH pollution levels with the NPI, and (c) linking PAH contamination with soil fertility to identify the degradation status of farmland in the YRD. The findings will provide valuable insights into soil quality protection and pollution remediation strategies for the YRD.

Results

Soil fertility quality assessment

The analysis of soil fertility properties in salinized farmland is detailed in Table 1. Notably, soil salinity, microbial biomass carbon (MBC), and total nitrogen (TN) exhibited the highest variability among the measured properties. In contrast, total potassium (TK), total phosphorus (TP), and soil pH showed relatively lower levels of variation. Other properties, such as magnesium (Mg), clay content, silt content, sand content, microbial biomass nitrogen (MBN), microbial quotient (MQ), and soil organic carbon (SOC), showed moderate variability, with coefficients of variation (CV) ranging from 15 to 95.39%. The average soil pH was 7.97, with values ranging from 7.50 to 8.48. The average concentrations of TN, TP, TK, and Mg were 0.17, 4.20, 16.90, and 5.70 g kg⁻¹, respectively.

Table 1.

Results of PCA of soil properties and their norm value and groups.

Soil indicators	Factor 1	Factor 2	Factor 3	Factor 4	Factor 5	Group	Norm value	Communalities
Salinity	-0.28	0.002	-0.15	-0.41	0.65	2	1.28	0.69
K	0.03	-0.32	0.71	-0.22	0.41	3	1.18	0.83
Mg	-0.28	-0.19	0.47	-0.33	0.26	3	0.98	0.51
P	0.44	-0.12	0.61	-0.17	-0.14	3	1.19	0.63
Clay	0.88	0.22	-0.14	-0.02	0.31	1	1.72	0.94
Silt	0.92	0.20	-0.10	-0.06	0.20	1	1.78	0.95
Sand	-0.93	-0.21	0.11	0.05	-0.23	1	1.8	0.98
TN	0.56	-0.30	-0.57	0.02	0.42	5	1.4	0.90
MBC	-0.02	0.89	0.31	0.09	-0.09	2	1.5	0.90
MBN	-0.32	0.45	0.33	0.15	-0.02	2	1.05	0.44
MQ	-0.09	0.91	0.29	0.01	-0.05	2	1.52	0.92
SOC	0.42	0.04	0.19	0.61	-0.26	4	1.12	0.66
pH	-0.29	-0.04	0.19	0.73	0.51	4	1.16	0.91
Eigenvalues	3.51	2.61	1.70	1.30	1.11
Variance (%)	27.00	20.10	13.11	9.98	8.53
Cumulative variance (%)	27.00	47.10	60.20	70.19	78.71

SOC: soil organic carbon; TN: total nitrogen; MBC: microbial biomass carbon; MBN: microbial biomass nitrogen; MQ: microbial quotient; TP: total phosphorus; and TK: total potassium.

Assessment of soil salinity and its impact on soil fertility

The average total salt content in the soil was 1.59 g kg^− 1, with substantial variation observed between the maximum (34.92 g kg^− 1) and the minimum (0.15 g kg^− 1) values, resulting in a CV of 189.55%. The total salt content in the farmland soil adjacent to the oil exploration area showed significant positive correlations with Na⁺, Mg²⁺, Ca²⁺, Cl⁻, and SO₄²⁻ (Table S2). Among these, the strongest correlations were with Cl⁻ (correlation coefficient, CC = 0.969 ,P < 0.01) and Mg²⁺ (CC of 0.958, P < 0.01). The Na⁺ content in the soil was 0.30 ± 0.67 g kg^− 1, accounting for 49.1% of the total cations, following the order of abundance: Na⁺ > Ca²⁺ > Mg²⁺ > K⁺. The cation Na⁺ also showed significant positive correlation with Cl⁻ (CC = 0.865, P < 0.01), Mg²⁺ (CC = 0.782, P < 0.01)(), Ca²⁺ (CC = 0.688, P < 0.01). The Cl⁻ content was 0.612 g kg^− 1, accounting for 61.8% of the total anions. Moreover, Cl⁻ content was significantly positively correlated with Mg²⁺ (CC = 0.973, P < 0.01) and Ca²⁺ (CC = 0.876, P < 0.01).

Optimizing soil fertility assessment through MDS

The accuracy of evaluation results can be significantly improved by selecting appropriate indicators. A scientifically reliable method for defining an MDS can reduce the number of indicators and lower evaluation costs when evaluating soil fertility. In this study, 13 soil indicators were screened for the MDS by PCA. The eigenvalues of the first five PCs ranged from 1.11 to 3.51 (Table 2), together, explaining 78.71% of the total variance. Based on factor loadings (≥ 0.50) for each PC, these soil properties were classified into five PCs. The communalities of soil indicators indicated that the factors explained over 90% of the variance in clay, silt, sand, TN, MBC, MQ, and pH values, and more than 80% of the variance of K (Table 2). In comparison, about 51% of the variance of Mg were explained. The factor analysis results employing principal component extraction for the selected soil indices are detailed in Table 2. The soil attributes in this study were diverse, and soil indicators were grouped into five categories according to the high loadings of each factor. Factor 1, referred to as the “texture attribute,” included high loadings for sand (-0.93), silt (0.92) and clay (0.88). Factor 2, named the “microbial attribute,” reflected soil microbial activity with strong positive loadings for MBC (0.89), MBN (0.45), and MQ (0.91). Factor 3, labeled the “nutrition attribute”, was related to TP (0.61), TK (0.71), Mg (0.47), and TN (-0.57) Factor 4, termed the “fertility attribute”, had high factor loadings for SOC (0.61) and pH (0.73). Finally, Factor 5 was dominated by high salinity loadings and was designated the “salt attribute.” Ultimately, the indicators included in the MDS were sand, SOC, pH, salinity, TN, TP, TK, and MQ (%), as these indices were within 10% of the highest norm in their respective factors (Table 2), and did not exhibit significant inter-correlations (Table S3). Pearson correlation analysis confirmed the absence of significant correlations between the selected indicators, a critical step in validating the independence of each indicator, and ensuring the MDS does not contain redundant information. The lack of multicollinearity further strengthens the reliability of the MDS, indicators provide unique and complementary information about soil fertility. The SFI values in this study area ranged from 0.40 to 0.67, with an average of 0.51 (Fig. 1). Salinity was the most significant contributor to SFI values, accounting for an average proportion of 21.9%. Furthermore, SFI_TDS was significantly correlated with SFI_MDS (Fig. 2), indicating that the soil fertility evaluation based on the MDS can effectively represent the full set of soil indicators.

Table 2.

Descriptive statistics of soil indices and 11 PAH concentrations in the study area.

Indicator/Compound	Min	Max	Mean	SD	CV (%)
Salinity (‰)	0.16	35	2.9	5.5	190
TK (g/kg)	14.3	18.8	16.9	1.2	5.6
Mg (g/kg)	2	11.7	5.7	2.3	42
TP (g/kg)	3.8	5.1	4.2	0.4	7.4
Clay (%)	4.4	13	7.9	1.9	25
Silt (%)	15.4	48	31	3.2	23
Sand (%)	40	80	61	0.05	15
TN (g/kg)	0.05	1.05	0.17	40	130
MBC (mg/kg)	13.7	3961	438	65	165
MBN (mg/kg)	2.8	210	57	12.5	91
MQ (%)	0.04	3.5	0.8	8.2	95
SOC (g/kg)	5.0	11.6	7.6	3.4	19
pH	7.5	8.48	7.97	0.6	3.1
Nap (µg/kg)	0.01	229	30.1	47	47
Flu (µg/kg)	0.01	103	17.4	20.7	20.7
Phe (µg/kg)	2.43	147	22.9	27	27
Ant (µg/kg)	0.31	103	12.8	21.1	21.1
Fla (µg/kg)	2.9	216	26.7	39.6	39.6
Pyr (µg/kg)	2.6	289	28.5	50.3	50.3
BaA (µg/kg)	0.01	227	20.8	45.5	45.5
Chr (µg/kg)	0.01	346	31.6	60.6	60.6
BbF (µg/kg)	0.01	253	15.9	38.8	38.8
BkF (µg/kg)	0.04	134	12.6	27.3	27.3
BaP (µg/kg)	0.04	220	21	38.8	38.8
∑11 PAH (µg/kg)	58.67	1729	240	392	392
2 ~ 3 ring PAHs (µg/kg)	23.44	308	83	68.1	68.1
4 ~ 5 ring PAHs (µg/kg)	11.53	1421	157	256	256

TK: Total potassium, Mg: Magnesium, TP: Total phosphorus, TN: Total nitrogen, MBC: Microbial biomass carbon, MBN: Microbial biomass nitrogen, MQ: Microbial quotient, SOC: Soil organic carbon, Nap: Naphthalene, Flu: Fluorene, Phe: Phenanthrene, Ant: Anthracene, Fla: Fluoranthene, Pyr: Pyrene, BaA: Benzo[a]anthracene, Chr: Chrysene, BbF: Benzo[b]fluoranthene, BkF: Benzo[k]fluoranthene, BaP: Benzo[a]pyrene, ∑11 PAH: Sum of 11 polycyclic aromatic hydrocarbons.

Fig. 1.

Soil fertility index and their contributions of MDS indicators in samples. SOC: soil organic carbon; TN: total nitrogen; MBC: microbial biomass carbon; MBN: microbial biomass nitrogen; MQ: microbial quotient; TP: total phosphorus; and TK: total potassium.

Fig. 2.

Linear relationships between the SFI_MDS and SFI_TDS values. SFI: soil fertility index; SFI_MDS: based on the minimum data set; SFI_TDS: based on the total data set.

Assessment of PAH contamination and impact on soil quality

The concentrations of 11 detected polycyclic aromatic hydrocarbons (PAHs) in the farmland soil of the study area are shown in Table 1, while five additional PAHs were not detected. The total PAH content (∑PAHs, dry weight) ranged from 58.7 to 1729.0 µg kg⁻¹, with an average concentration of 141.8 µg kg⁻¹. The concentration of low-molecular-weight (LMW) PAHs), consisting of 2–3 rings, was 83.0 µg kg⁻¹, while high-molecular-weight (HMW) PAHs, comprising 4–5 rings, had a concentration of 157.0 µg kg⁻¹. Pearson correlation analysis revealed that PAH contamination significantly contributed to the low SQI in the study area (Table S5). The NPI was used to assess the level of PAH contamination in the soils, reflecting the impact of pollutants on environmental quality. The average P_i values of individual PAHs ranked as follows: P_N > 5 > BaA > 4 > Flu > Chr > 3 > Pyr > BaP > Nap > 2 > Fla > BbF > Ant > BkF > Phe > 1, indicating a descending order of contamination. Among the detected PAHs, BaA exhibited higher P_i values, indicating that it may be the main contributor to the PAH pollution in farmland soils. The samples were categorized as heavily contaminated with Chr and Flu, and moderately to heavily contaminated with BaP, Pyr, and Nap. Moderate contamination levels were observed for Fla, BbF, Ant, BkF, and Phe.

Integrative assessment of soil quality in salinized farmland

The value of SQI in this study ranged from 0.18 to 0.67, with an average of 0.52, indicating a moderate level of comprehensive soil quality. A linear fitting analysis (Fig. 3) revealed a trend of slightly increasing SFI values, ascending NPI values, and decreasing SQI values. These results suggest that the SQI effectively integrates environmental quality by accounting for both PAH contamination and soil fertility. Furthermore, Pearson correlation analysis (PCA) demonstrated a significant negative correlation between SQI and NPI (P < 0.01) (Table S5). This indicated that the low SQI in the study area were primarily driven by high levels of PAH contamination. In contrast, a significant positive association were observed between SQI and SFI (P < 0.05), emphasizing the importance of addressing factors limiting soil fertility, particularly in salinized farmland neighboring oil exploitation area.

Fig. 3.

The variation in NPI, SFI, and SQI of all farmland soil samples.

NPI: Nemerov pollution index; SFI: soil fertility index; SQI: soil quality index.

Discussion

The results of this study provide several key insights into the soil fertility quality of salinized farmland and the impact of PAH contamination on overall soil quality. The assessment of soil fertility properties showed considerable variability, with indicators such as soil salinity, microbial biomass carbon (MBC), and total nitrogen (TN) showing the greatest fluctuations. This underscores their critical role in determining soil health in salinized environments. In particular, soil salinity, emerged as a major limiting factor for fertility, contributing significantly to the Soil Fertility Index (SFI). The high correlations between total salt content and ions like Cl⁻ and Na⁺ suggest that compounds such as NaCl, MgCl₂, and CaCl₂ are prevalent, potentially exacerbating salinization in the study area. As a result, soil fertility was primarily constrained by soil salinity (Fig. 1).

According to grading standards²³, the comprehensive assessment of the SFI and the NPI indicates that soil quality was predominantly classified between Grade V to Grade III, reflecting overall low quality. The low soil organic carbon (SOC) content further highlighted the degraded state of the soil, and the microbial quotient (MQ) also pointed to reduced biological activity. These findings demonstrate that soil fertility in the study area is closely linked to the degradation of various physicochemical and biological properties. Notably, nutrient levels observed in this study were lower than those typically found in other agricultural systems, such as plastic greenhouse production or farmland soils in wetland areas²². SOC is a widely recognized indicator of soil quality due to its essential role in maintaining soil functions and supporting microbial activity^27–29. In high-quality soils, SOC is balanced with biological and biochemical processes³⁰. However, the low levels of total nitrogen (TN) and total potassium (TK) observed here further underscore the poor nutrient status of these soils.

Oil exploitation and soil salinization have significantly impacted soil quality in the study area, disrupting physical and chemical properties⁷. Two contrasting processes are commonly observed in salt-affected soils: reduced water availability due to increasing salinity, and accelerated mineralization of organic matte^23,31. High electrical conductivity (EC) in saline or saline-sodic soils leads to clay particle flocculation, restricting substrate availability and hindering the decomposition of soil organic matter (SOM)^32,33. Moreover, the accumulation of salts in the rhizosphere imposes water stress and ion toxicity on crops, limiting the uptake of essential nutrients such as K⁺, Ca²⁺, and NO₃⁻, while promoting the buildup of harmful ions like Na⁺ and Cl⁻^34,35. This can adversely affect chloroplasts and other cellular structures, inhibiting photosynthesis and protein synthesis^36,37. Furthermore, high salinity accelerates the mineralization of SOM in sodic soils, further degrading soil fertility and stability. To mitigate moderate salinization and restore saline ecosystems, the cultivation of salt-tolerant crops, such as glasswort (Salicornia europaea), saltbush (Atriplex species), salt-tolerant wheat varieties (Triticum aestivum), and barley (Hordeum vulgare)³⁸, along with appropriate fertilization and irrigation management strategies, is recommended.

PAH contamination has also emerged as a critical factor contributing to soil quality degradation in the study area. As persistent organic pollutants, PAHs disrupt soil microbial communities, reduce biodiversity, and impair microbial functions critical for organic matter decomposition and nutrient cycling^39,40. Furthermore, PAHs negatively affect soil physical properties, such as structure and permeability, leading to reduced water retention and impaired root development⁴¹. Elevated concentrations of high-molecular-weight PAHs, such as benzo[a]anthracene (BaA), fluoranthene (Flu), and chrysene (Chr), which are strongly linked to oil-related activities, indicate that proximity to oil exploration and extraction zones is a major contributor to the contamination observed. High-molecular-weight PAHs persist in soil for extended periods due to their complex molecular structures and low biodegradability⁴², significantly inhibiting the activity and diversity of functional microorganisms involved in nutrient cycling. This disruption directly impacts soil fertility and crop productivity⁴³. Additionally, PAHs interfere with symbiotic relationships between plant roots and soil microorganisms, impairing plants’ ability to absorb water and nutrients, which compromises plant growth and health⁴⁴. In agricultural systems, PAH contamination affects critical soil functions, including enzyme activity and aggregate stability⁴⁵, which weakens soil fertility, reduces crop resilience, and impair the soil’s capacity to provide essential ecosystem services such as water retention, nutrient provision, and carbon sequestration. Over time, PAH accumulation threatens the sustainability of agricultural ecosystems, especially in highly contaminated areas.

The strong inverse relationship between PAH contamination and SQI values underscores the significant role of PAH pollution in degrading soil quality. Effective remediation strategies, particularly for high-molecular-weight PAHs, are essential for mitigating their long-term impacts on soil microbial functions, plant health, and ecosystem services. An integrated analysis of SQI, the soil fertility index (SFI), and the PAH pollution index (NPI) reveals the complex interactions between soil fertility and contamination in salinized farmlands. Addressing both salinity and PAH contamination is crucial for improving soil quality and ensuring the sustainability of agricultural ecosystems in regions affected by oil exploitation. The significant negative correlation between SQI and NPI highlights PAH contamination as a dominant factor driving soil degradation by disrupting microbial communities and impairing essential soil functions. Conversely, the positive correlation between SQI and SFI emphasizes the importance of improving soil fertility to counterbalance contamination effects. Improving soil structure, promoting beneficial microbial activity, and enhancing nutrient availability through targeted fertility management may buffer the negative impacts of PAH contamination. A dual approach that combines contamination mitigation with fertility enhancement is essential for restoring soil health and promoting sustainable agricultural practices. Future research should focus on the effectiveness of remediation techniques, such as bioremediation and phytoremediation, in conjunction with fertility improvement strategies to enhance soil quality in contaminated and salinized farmlands.

Methods

Study area

An oil exploitation area (118.0’E–118.6’E, 37.26’N–37.30’N) in Shandong Province, within the YRD, China, was chosen as a case study site (Fig. 4). The region experiences a temperate monsoon climate, situated in the warm temperate sub-humid region. with an average annual temperature of 13.27℃ and average annual precipitation of 567.7 mm. Geologically, the area is predominantly composed of limestone formations from the Quaternary and Carboniferous periods. The soil in this area is mainly saline-tidal, a main constraint on agricultural productivity due to its high salt content, which contributes to ongoing land salinization challenges.

Fig. 4.

Location of the oil exploitation area in the Yellow River Delta (YRD) and the sampling sites. The map was generated using ArcGIS (version 10.6, https://www.esri.com).

Soil sampling and chemical analysis

In this study, a comprehensive soil sampling approach was employed to assess the environmental conditions surrounding a power plant within a 5 km radius. A total of 42 soil samples from the 0–20 cm topsoil layer were collected with careful consideration given to the distribution of potential pollution sources, land use patterns, and the uniformity of sample distribution. To ensure sample stability, sterilized tools were used during collection to avoid contamination, and samples were immediately placed in sterile, sealed bags immediately after collection. Samples were transported to the laboratory under controlled conditions at 4 °C using ice packs, minimizing microbial activity and preventing chemical alterations during transit. Upon arrival at the lab, the samples were processed promptly to preserve their integrity. GPS was used to accurately record the locations of pollution sources near the sampling sites (Fig. 4). Once in the lab, the soil samples were stored at − 20℃ to maintain stability. Freeze-dried soil samples were sieved through a 2 mm mesh for purification. Some fresh soil samples were stored at 2 °C, while others were air-dried at room temperature. Various soil properties were measured: the soil organic matter (SOM) was determined using the dichromate oxidation method⁴⁶, and pH was measured by a Sartorius PB-10 pH meter (soil: water = 1:2.5). Soil salinity was measured using a Hach conductivity meter (Hach 9500, USA) (soil: water = 1:5). Total nitrogen (TN), microbial biomass carbon (MBC) and microbial biomass nitrogen (MBN) were detected with a CN element analyzer (Elementar Vario MAX N/CN, Germany)⁴⁷. Soil texture was detected with a laser particle size analyzer (Malvern Mastersizer 2000, UK), while The concentrations of K⁺, Na⁺, Mg²⁺, Ca²⁺, Cl⁻, HCO₃⁻ and SO₄²⁻ were determined using ion chromatography (Thermo Fisher Scientific ICS-2100, USA)⁴⁸. Additionally, total phosphorus (TP), total potassium (TK), and magnesium (Mg) were measured by ICP-OES (Optima 5300DV PerkinElmer, USA), and the concentrations of the 11 PAHs were measured by GC-MS (Shimadzu TQ8040, Japan). The external standard method, with p-Terphenyl-d14 as an indicator, ensures precise PAH quantification, with recovery rates ranging from 91 to 116%.

Soil quality assessment

A soil quality assessment was performed in four sequential steps. First, appropriate indicators were selected in line with their relevance to critical soil functions, considering the combined impacts of salt stress and PAH contamination in the YRD. Second, these indicators were standardized to ensure comparability. Third, soil fertility index (SFI) and environment index (EI) were calculated and integrated. Finally, the SFI and EI were combined to derive the overall soil quality index (SQI) (Fig. 5).

Fig. 5.

Framework of this study.

Soil fertility assessment

The 13 indicators selected for the minimum data set (MDS) were specifically chosen to address two significant challenges in the study area: PAH contamination and salinization. Both of these issues are highly relevant in the YRD, where industrial activities and soil degradation are widespread. The selected indicators reflect critical physical, chemical, and biological soil properties, providing a comprehensive approach to evaluate soil quality⁴². This ensures that the MDS effectively captures the complex interactions between contamination and soil degradation. Principal component analysis (PCA), vector norm calculations, and FI results are provided in Supplementary S1. The soil fertility index (SFI) was calculated using the following Eq. (1):

1

where FI_min is the minimum FI value and FI_ave is the average FI value.

Assessment of soil environment quality

In this study, the Nemerow pollution index (NPI) was employed to evaluate PAH contamination in the soil^25,49. The specific steps of calculating the NPI are shown in Table S4. Additionally, we defined the EI to quantify soil environment quality in relation to PAH contamination. It was calculated using the following Eq. (2):

2

where is the NPI. EI is the standardized environment index (0–1), based on the NPI. When the values of NPI less than 1 indicate an uncontaminated level, the EI reaches its highest value. The values of NPI increase with the increase in pollution level, while the values of EI decrease, because of the worsened environmental quality²¹. SEI was calculated by the following Eq. (3):

3

Where EI_min is the minimum EI value and EI_ave is the average EI value.

Comprehensive assessment of soil quality

We used a multi-standard quantitative method to calculate the SQI, which integrates soil fertility and PAH pollution levels. The MDS scoring method was used to evaluate soil fertility, represented as the fertility index (FI) (Eqs. 1–4, Supplementary Text S1). The standardized values of both soil fertility and environmental quality range between 0 and 1. In line with the “minimum rule,” the lowest level and the average levels of soil fertility were combined with the highest level and average levels of PAH pollution into a unified formula, ensuring a comprehensive assessment of agricultural soil quality²¹. Therefore, the SQI is primarily determined by the lowest soil fertility and environmental quality, highlighting the restrictive influence of high PAH pollution and low fertility. The establishment of SQI is as follows:

4

Soil quality is classified into five grades based on the standardized range of Soil Quality Index (SQI) values: very high (Grade I, 0.8 ≤ SQI ≤ 1), high (Grade II, 0.6 ≤ SQI < 0.8), medium (Grade III, 0.4 ≤ SQI < 0.6), low (Grade IV, 0.2 ≤ SQI < 0.4), and very low (Grade V, 0 ≤ SQI < 0.2)²².

Statistical analysis

Pearson correlation analysis was performed to reveal the relationships among the measured soil properties. Additionally, PCA was utilized to select the most suitable indicators for the soil quality index. The KMO value of the PCA analyses was 0.563, while Bartlett’s test score of 846 (df = 78, P < 0.01), confirming the suitability of PCA for data analysis and interpretation, particularly in evaluating the relationships between soil fertility and PAH contamination. All statistical analysis was conducted using SPSS 24.0 (SPSS Inc., Chicago, IL, USA).

Conclusion

This study assessed soil quality in salinized farmland adjacent to an oil exploitation area, integrating evaluations of soil fertility and environmental factors. The analysis identified soil salinity and environmental constraints as the primary factors limiting soil quality. While PAH pollution and fertility indices were central to the evaluation, gaining a deeper understanding of the relationship between soil fertility and PAH contamination could provide valuable insights into soil degradation and inform effective remediation strategies. Future studies should incorporate additional environmental factors such as heavy metals, petroleum hydrocarbons, soil microorganisms, and physical properties, to achieve a more comprehensive assessment. Furthermore, employing weighted methods for the Soil Fertility Index (SFI) and Soil Environmental Index (SEI) could enhance assessment accuracy, although these approaches remain underdeveloped. Agricultural interventions, including the selection of salt-tolerant crops, precise irrigation and fertilization techniques, and biotechnological applications, are essential for mitigating salinity. Strategic efforts to enhance soil fertility and reduce pollution are crucial for preventing further soil degradation.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Author contributions

X.F. X.—designed the study, acquired and analyzed the data, interpreted the data, drafted and revised the manuscript; J.J.C— analyzed the data; X.S.Y.—acquired and analyzed the data, interpreted the data; H.Q.—acquired the data; Y.X. L.—designed the study, acquired and analyzed the data, interpreted the data, drafted and revised the manuscript; Y.X.Z. —Revised the manuscript. All authors listed above have contributed to the work and approved the final version of the manuscript.

Data availability

Correspondence and requests for materials should be addressed to Y.X.L. Data sets generated during the current study are available from the corresponding author on reasonable request.

Declarations

Competing interests

The authors declare no competing interests.