Multivariate Analysis of Heavy Metal Pollution and Salinity Interactions in Saline Soils for Agricultural Management

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Multivariate statistical methods such as regression analysis, principal component analysis (PCA), and cluster analysis (CA) were applied to characterize anthropogenic and natural factors affecting HMs in soils. Close relationships were found for electrical conductivity (EC) and cadmium (Cd), cobalt (Co), chromium (Cr), copper (Cu), lead (Pb), and zinc (Zn) concentrations, but correlation strength differed among soil samples. Bulk Saline Soil (BSS) samples had Cd (1.01–3.62 µg/g), Co (18.6–29.1 µg/g), Cr (5.07–13.6 µg/g), Cu (122–191 µg/g), Pb (4.01–9.99 µg/g), and Zn (170–193 µg/g). Regression analysis was also used to demonstrate the need for EC as well as the measurement of HMs concentration in agricultural management. PCA indicated that variables under PC2 were generally orthogonal to PC1, with a third factor (PC3) accounting for 19.7% data variance, demonstrating the presence of other independent factors influencing HMs accumulation aside from the main components described. The results emphasize the significance of integrated EC and HMs monitoring in successful salt-affected farmland management. Salty Soil Heavy metals PCA CA Environmental monitoring Figures Figure 1 Figure 2 Figure 3 Introduction Salt occurs naturally in water and soil. However, when there is too much salt, the soil is described as saline, meaning that the soil does not meet our economic, aesthetic, and environmental needs. This needs to be solved for our landscapes and ecosystems to be productive and healthy (Waris et al., 2023 ). Solution to such issues are crucial for the environmental well-being of the area and sustainable agriculture (JAMPASRI & SAENG-NGAM, 2019 ). Soil salinization threatens the biochemical, hydrologic, and erosion processes that favor Earth's environments and natural biological procedures that are indispensable for life. Multiple studies demonstrate that heavy metals like Cd, Cr, and Pb contaminate soil, water, and air through both natural and anthropogenic sources. Their toxicity depends on exposure dose, route, and individual factors like age and genetics (Hassan et al., 2024 ; Ishak et al., 2025 ). Severe health consequences including neurological disorders, developmental delays, and increased cancer risks. specifically highlights molecular toxicity mechanisms like DNA damage, oxidative stress, and disrupted cellular processes (Ogwu et al., 2025 ). The evidence is robust, with studies spanning terrestrial and aquatic organisms showing consistent oxidative stress mechanisms. Over a dozen environmental pollutants capable of ROS overproduction, including ozone, nitrogen oxides, particulate matter, and pesticides. Critically, the oxidative stress potential varies by pollutant type and concentration, with ultrafine particles and transition metals being particularly potent (Ahandani et al., 2022 ; Talas et al., 2008 ). The most salinized soils are found in several Central and Western Asian nations, such as Pakistan, China, America, India, Argentina, Sudan (Kumar & Sharma, 2020 ; Tahir & Khaliq, 2018 ). To maintain the agricultural legacy and ensure food security, this trend demands utmost attention (Ayangbenro & Babalola, 2017 ; Chaoua et al., 2019 ). Low-quality groundwater is used for irrigation because there is a lack of high-quality water sources. One of the main ways that saltwater enters the Indus Basin and causes issues with salt and water salinization is through the overuse of such low-quality groundwater (Caglar et al., 2019 ; Qureshi et al., 2008 ). Salinity is another crucial factor that affects metal toxicity, mobility, and transport. According to research, HMs have a high level of environmental resistance and may bioaccumulate over time. The chemical characteristics, concentration, and soil availability largely define toxic metal toxicity and mobility of toxic metals. Compared to higher salinity, the biologic impact of HMs is much greater at low salinity (Bai JunHong et al., 2019 ). The mobility and toxicity of HMs in soils depend on their chemical characteristics, concentration, and availability. Lower salinity levels can have a more significant biological impact on HMs (Bai JunHong et al., 2019 ). Soil contamination with HMs is exacerbated using wastewater, pesticides, fertilizers, and improper disposal of industrial and urban waste. Addressing these sources is critical for environmental protection and public health (Waris et al., 2022 ). The global issue of HMs contamination in saline soils, particularly concerning metals such as Cd, Co, Cr, Cu, Pb, and Zn, poses a serious ecological challenge (Bartkowiak et al., 2017 ). Multivariate statistical analysis is essential for understanding and managing soil contamination, facilitating the efficient processing of data to uncover patterns and relationships. Techniques such as PCA and regression analysis have proven effective in revealing the underlying trends and complex correlations in environmental data (Baig et al., 2010 ; Chudasama et al., 2024 ). This study aimed to analyze HMs concentrations in saline soils from four sites in Sukkur, Sindh, Pakistan. By employing multivariate analysis, we can differentiate between anthropogenic and natural sources of HMs pollution, providing crucial information for targeted remediation efforts and enhancing environmental health. Materials and methods Sampling of BSS To obtain spatial variation, a stratified random sampling technique resulted in a total of 200 saline soil samples from four Sukkur sites (50 samples each). Following surface debris removal, samples were collected from the 0–15 cm soil layer using a stainless-steel auger. Clean, identified plastic bags held each sample (~ 500–1000 g). Triplicate and blank samples were included for quality control, and equipment was washed between collections. Before physico-chemical analysis, air-dried and sieved (2 mm) all samples were cooled and transported to the laboratory. Chemical reagents and glassware Deionized water was obtained using a purification system (Bedford, MA, USA). Merck (Darmstadt, Germany) purchased analytical-grade hydrogen peroxide and nitric acid. Filter paper (0.45 µm) was purchased from Biotech (Germany). Standard solutions (1,000 mg/L) were diluted to create working standards for Cd, Co, Cr, Pb, Cu, and Zn. The analysis involved the use various equipment, including a conductivity meter, flame atomic absorption spectrometer (FAAS), and graphite furnace for evaluating HMs. Physical-chemical evaluations of salty soil The EC, pH, and organic matter (OM) quality features of each soil sample were evaluated using recognized methods (Waris et al., 2022 ). A 1:2.5 soil-to-water ratio was used to evaluate characteristics, and a conductivity meter measured EC. The OM content was determined by heating the soil samples in a muffle furnace at 540°C for six hours, with calculations based on weight differences before and after ashing (ASTM, 2007 ). Samples preparation procedure HMs in BSS Soil samples (5 g each) were combined with an acid solution (6 ml HNO 3 and 2 ml H 2 O 2 ) to break down organic material. The mixtures were heated on an electric hot plate for two-three hours. Finally, the digested samples were analyzed using FAAS and graphite furnace atomic absorption spectroscopy (GFAAS). Chemometric analysis Multivariate statistical analysis was employed to identify hidden patterns in large datasets that could indicate HMs contamination sources. PCA and cluster analysis were used to differentiate metal sources in soils (Young & Hammer, 2000 ). Regression analysis was used to evaluate the metal-soil property relationships, and all analysis was conducted using OriginPro statistical software to achieve accuracy (Hou et al., 2017 ). Results and discussion Physico‑chemical analysis of BSS The relationship between soil salinity and HMs mobility remains unclear. The increased mobility of HMs due to salinization is a significant concern that must be considered in environmental risk assessments (Acosta et al., 2011 ). In addition, OM content is very important for maintaining soil integrity; however, our findings revealed abysmally low rates of between 0.620% and 1.45%. This indicates that the land is not sufficient for agriculture, thus the necessity of taking prompt restoration measures. Soil pollution with HMs is a severe environmental issue owing to the high toxicity and persistence of the pollutants. This problem negatively impacts soil quality, and it is difficult to identify areas with fertile soils (Waris et al., 2023 ). Several aspects of biological life are threatened by non-biodegradable chemicals and dangerous chemicals that are beyond safety thresholds. Cd concentrations in BSS samples range from 1.11 to 3.71 µg/g, Co concentrations range from 17.8 to 23.9 µg/g, Cr concentrations range from 6.07 to 23.9 µg/g, Cu concentrations range from 108 to 193 µg/g, Pb concentrations range from 4.33 to 11.1 µg/g, and Zn concentrations range from 118 to 181 µg/g (Table 1 ). Heavy metal contamination in soils frequently exceeds international safety standards, posing significant risks to crop production and human health. WHO target values for heavy metals follow the order Cr > Pb>Zn > Cu>Cd, with Cd presenting the highest potential risk (target value of 0.8 µg/g. The soils surrounding industrial companies were contaminated with Cd and Pb, exceeding USEPA and European Union standards, while some heavy metal concentrations were within acceptable thresholds, Cd levels consistently exceeded the permissible limit of 3.00 µg/g (Caglar et al., 2019 ; Tóth et al., 2016 ; Yang et al., 2018 ). The occurrence of these HMs in BSS is of considerable concern because they are bioavailable, transportable, and ecotoxic to ecosystems. Thus, the determination of the total concentration of these metals does not entirely indicate environmental pollution, hence the need to conduct additional studies are required (Waris et al., 2021 ). Table 1 The concentration of HMs (µg/g) in BSS samples is based on physicochemical parameters. Parameters BSS1 BSS2 BSS3 BSS4 pH Mean ± SD Range 7.33 ± 0.05 7.28–7.38 7.78 ± 0.06 7.72–7.84 7.89 ± 0.11 7.78-8.00 7.12 ± 0.69 6.45–7.81 EC (dS/m) Mean ± SD Range 4.89 ± 0.44 4.45–5.33 4.91 ± 0.72 4.19–5.63 5.11 ± 0.61 4.50–5.72 5.10 ± 0.62 4.48–5.72 OM (%) Mean ± SD Range 1.67 ± 0.33 1.34-2.00 0.86 ± 0.13 0.73–0.99 0.72 ± 0.09 0.63–0.81 0.97 ± 0.11 0.86–1.08 Cd Mean ± SD Range 1.89 ± 0.29 1.60–2.18 1.33 ± 0.17 1.16–1.50 3.71 ± 0.29 3.42-4.00 1.11 ± 0.17 0.94–1.28 Co Mean ± SD Range 19.3 ± 1.36 18.9–20.7 23.9 ± 2.31 21.6–26.2 17.8 ± 2.65 15.2–20.5 19.9 ± 1.98 17.9–21.9 Cr Mean ± SD Range 6.09 ± 1.18 4.91–7.27 14.9 ± 2.61 12.3–17.5 9.09 ± 1.76 7.33–10.9 10.9 ± 1.51 9.39–12.4 Cu Mean ± SD Range 108 ± 4.81 104–112 112 ± 5.54 106–118 119 ± 4.00 115–123 193 ± 5.79 187–199 Pb Mean ± SD Range 4.33 ± 0.93 3.40–5.26 5.41 ± 0.87 4.54–6.28 11.1 ± 1.23 9.87–12.3 9.89 ± 1.23 8.66–11.1 Zn Mean ± SD Range 133 ± 4.56 128–138 118 ± 4.11 114–122 119 ± 3.09 116–122 181 ± 4.19 177–185 Principal Component Analysis PCA was used to investigate the concentrations of HMs in BSS, revealing their presence and spatial distribution. These data are crucial for assessing potential risks and formulating management strategies in contaminated regions (Deljomanesh et al., 2024 ). Additionally, PCA is a useful tool for assessing how human activity affects river water quality, emphasizing the need to use a variety of approaches to pinpoint HMs sources in river basin regions areas (Ogwu et al., 2025 ). In this study, a dataset of physicochemical parameters and HMs content dataset from different soil samples was subjected to PCA. This was performed to decrease the number of descriptors that may be responsible for the total variance in the experimental data. Independent analyses were performed on normalised datasets of BSS with nine variables for four different sampling locations (n = 24) (Table 2 ). The largest variances in the data were accounted for by the first principal component (PC1), 53% explained. PC1 in most datasets can be used to define an overall trend or prevailing factor, such as a contamination zone or fertility gradient, according to data context. The second principal component (PC2) accounted for a further 27.4% unexplained variability by PC1. Values closer to PC2 than to PC1 indicate a trend or pattern in development, presumably of a different factor than that accounted for by PC1. A further 19.7% of the unexplained variability that neither PC1 nor PC2 could explain was described by the third main component, PC3. A secondary gradient, such as industrial pollution, is created when variables that point with long arrows on the PC3 axis contribute significantly to this third axis of variation and may indicate a common source or type of behavior in the soil (Fig. 1 ). Table 2 Loadings of experimental variables (36) on key principle components for BSS samples. Variables PC 1 PC 2 PC 3 pH 0.410 -0.213 -0.009 EC (dS/m) 0.299 0.430 0.121 OM % -0.401 -0.250 0.230 Cd (µg/g) 0.90 0.370 0.607 Co (µg/g) 0.234 -0.118 0.080 Cr (µg/g) 0.250 0.110 -0.580 Cu (µg/g) -0.027 -0.490 0.420 Pb (µg/g) 0.409 -0.021 0.233 Zn (µg/g) -0.152 0.550 0.200 Eigenvalue 5.208 2.740 2.000 Total variance % 54.0 28.4 20.0 Cumulative % 54.0 81.0 100 The first principal component, the X-axis (PC1), explained 48.32% of the overall variation in the data. The second principal component, which explained 33.68% of the variation, was the Y-axis (PC2). 82% of the data variance may be explained by these two factors (Fig. 1 ). This pattern is observed in cases effected by industrial processes, mining, or agricultural runoff, ehre the same forces affect all three readings. EC within a system is most often affected by dissolved salts, nutrients like nitrogen, and HMs (Ogwu et al., 2025 ). The correlation values in the matrix indicate that soils with high EC values are likely to have greater dissolved concentration of metals and nutrients, and EC can thus be used as an indicator of soil contamination or fertility. High pH levels precipitate Cu compounds or enhance their adsorption by soil particles. These modifications can decrease the mobility and bioavailability of Cu but can sustain a higher total soil concentration (Rees et al., 2014 ). Salinity can affect both the chemical speciation and pH of Cu. The presence of salt in the soil can influence the activity of Cu ions, which could result in a situation where an elevated pH corresponds to enhanced Cu retention (Wu et al., 2022 ). Cluster analysis (CA) was applied to a database of HMs composition and EC for the four saline soil sampling points. The objective was to identify spatial differences and similarities among these sites (Fig. 2 ). HMs such as Cd, Cr, Cu, Pb, and Zn may occur in various oxidation states and interact with soil constituents. The strong binding of EC to Cd and Pb implies a probable correlation (Khan et al., 2024 ). This is evidence to prove the fact that regions with high EC also exhibit high concentrations of HMs due to human interventions or natural chemical weathering (Ali et al., 2019 ). The findings would indicate a relationship between HMs pollution and EC, and probable implication of industrial effluent or dissolved salt (Mokarram et al., 2020 ). Regression analysis of physicochemical properties and HMs BSS Regression analysis was applied to measure the direction and magnitude of the linear relationship between two continuous variables. In the present study, it was used to determine whether changes in soil EC influence HMs release. An interesting effect of an increase in EC on mobilization of HMs was indicated (see Table 3 ). The most dramatic and statistically significant alterations were found in the mobilizing of Cd, Co, and Cr in BSS1 and BSS2. An increase in EC was associated with a much higher release of Cd than of other metals. Cd and Cr were the most mobile, implying that they are more displaced by salts or are more likely to react with chloride than the other metals (Tangahu et al., 2011 ). The % of Zn released showed a minor increase with rising EC, but this was less pronounced than the changes observed of Cd and Cr. These actions can be explained by the chemical interactions between soil particles and by salinity effects. These interactions will regulate in large measure the mobility and bioavailability of these metals in the environment. Therefore, these elements will have minimal mobility and leaching potential, and their relative stability in the soil (Porter et al., 2004 ). Table 3 Regression Analysis of HMs and EC in BSS BSS1 BSS2 BSS3 BSS4 Cd y = 0.0564x + 1.750 R² = 0.929 y = 0.2125x + 0.289 R² = 0.913 y = 0.1970x + 2.544 R² = 0.223 y = -0.479x + 3.111 R² = 0.491 Co y = 1.265x + 11.90 R² = 0.851 y = 1.419x + 13.901 R² = 0.400 y = 1.164x + 22.298 R² = 0.535 y = 1.496x + 20.580 R² = 0.0702 Cr y = -0.219x + 6.007 R² = 0.801 y = -7.271x + 49.675 R² = 0.798 y = -3.506x + 25.308 R² = 0.178 y = 0.401x + 10.212 R² = 0.0131 Cu y = -12.130x + 239.91 R² = 0.105 y = -40.560x + 330.27 R² = 0.670 y = 36.793x − 24.901 R² = 0.391 y = -20.301x + 287.18 R² = 0.178 Pb y = -0.567x + 7.501 R² = 0.0820 y = -0.276x + 5.999 R² = 0.021 y = 0.242x + 8.401 R² = 0.037 y = 2.011x − 0.0101 R² = 0.403 Zn y = -5.021x + 217.00 R² = 0.0450 y = -42.010x + 411.0 R² = 0.705 y = 46.401x − 38.111 R² = 0.740 y = 32.001x + 27.010 R² = 0.307 Heat map correlations study of physicochemical properties and HMs BSS Heatmaps will facilitate the identification of connections between pollutants and soil chemistry parameters. Finding likely connections or impacts of variables may be aided by strong positive or negative correlations. With an increase in the pH of the soil, Zn solubility in soil declined (Fig. 3 ) Such a trend is typically observed in the common chemical behaviour of most metals, which have higher solubility in acids and reduced solubility in alkalis. High EC generally reflects the makeup of more soluble ions, such as metals like Cd. Industrial pollution, fertilizer application, and wastewater irrigation can result in high EC along with elevated HMs composition. Cu and Pb generally have the same origins such as industrial effluent, mining, or urban pollution and can co-exist in polluted soils (Sahay et al., 2019 ). A high positive correlation implies that whenever Cu content is higher, the Pb content will also be higher. Both metals can be carried by the same medium, as there is a common source of contamination, and the same soil conditions may favour their co-occurrence in solution. This type of information facilitates making informed decisions about liming, salinity control (for EC reduction) and mitigation of HMs contamination. Existence of strong correlation between polluters may imply a shared source of pollution with remediation implications (Selvam et al., 2024 ). Conclusion PCA indicated that the first three principal components account for 100% of soil variation, whereas EC has a strong correlation with HMs concentration. CA confirmed the correlation, especially between EC, Cd, and Pb, indicating shared sources or chemical transformation in saline environments. This analysis is interesting, especially with the concerning the third main component (19.7% of residual variation). Heatmaps describe key correlations between metal solubility and soil chemistry parameters and pollutants and demonstrate EC and pH as being important in metal solubility. Salinity controls and chemical reactivity with soil solids governs metal bioavailability, although most metals are relatively immobile with low leaching and substitution affinities. These correlations remediation of metal-polluted saline environments more efficient. Salinity reduction minimizes HMs pollution and promotes workplace health and agricultural productivity. Further studies on these parameters are necessary to ensure soil health. Declarations Conflict of Interest All authors have agreed to the submission to the current study and have no conflict of interest. It is confirmed that the proposed study is not submitted elsewhere for publication. On behalf of all authors, the corresponding author states that there is no conflict of interest. Meanwhile, there are no institutional, venders or public financial of non-financial conflict of interest. Funding The authors received no specific grant from any funding agency in the public, commercial, or non-profit sectors for this work. Consent to Publish Not applicable. This study does not include any individual person’s data in any form. Ethics and Consent to Participate Ethics approval and consent to participate: Not applicable. The study involved physicochemical/analytical experiments only and did not require ethical approval or participant consent. Author Contribution Muhammad Waris conceptualized the study, designed the methodology, performed data analysis, and drafted the original manuscript. Nadir Hussain comtributed to validation of results and critical revision of the manuscript. Summan Urooge contributed to data collection, experimental work. All authors reviewed and approved the final version of the manuscript. Competing Interests It is declared that there are no competing interests. References Acosta J, Jansen B, Kalbitz K, Faz A, Martínez-Martínez S. Salinity increases mobility of heavy metals in soils. Chemosphere. 2011;85(8):1318–24. Ahandani EA, Terepoei ZA, Sheydaei M, Akram M, Selamoglu Z. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8692320","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":597095702,"identity":"e7f29407-2b33-417e-9098-f899d517e6be","order_by":0,"name":"Dr Muhammad Waris","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFUlEQVRIie3PsUrDQBzH8d8hxOWk67m0TyCkBBqKVV8lIZAuHQQhOEmm69LuN/gS4gtcOTDLma6FDGZycqi4dIhgmgh1SGNHkftC+B+Bz/E/wGT6q3lApxxEAqPvX1b50XZyGpdTSoQHEuyI+p2cieB1nRdgnam21Ttf9tzpPMc6UnCpbCSDVegIn4MxPbHlgmf9e53YRKQKw3ncTDLPgR/jDitaESJYiKMTrmAvmxcbZOMPeOVivZqkVxX5bCUTB54FZtdE+hUhW/K8Z7GXtxv4nLG+Dq+lToNA0CcsZumYDmd7nq+TR7IpRqybqIf8Nrq8EMec5JvovOtSr3mzOlYPYtVzez2128Cu4sf5QGIymUz/vy/qu2OA3OjJswAAAABJRU5ErkJggg==","orcid":"","institution":"Aror University of Art, Architecture, Design and Heritage","correspondingAuthor":true,"prefix":"Dr","firstName":"Muhammad","middleName":"","lastName":"Waris","suffix":""},{"id":597095703,"identity":"681b0444-abe7-4445-aba2-092cf57dd41b","order_by":1,"name":"Nadir Hussain","email":"","orcid":"","institution":"Aror University of Art, Architecture, Design and Heritage, Sukkur, Pakistan","correspondingAuthor":false,"prefix":"","firstName":"Nadir","middleName":"","lastName":"Hussain","suffix":""},{"id":597095704,"identity":"e1ae1dcc-8764-4981-abb2-b5e4d9257234","order_by":2,"name":"Summan Urooge","email":"","orcid":"","institution":"University of Poonch Rawalakot","correspondingAuthor":false,"prefix":"","firstName":"Summan","middleName":"","lastName":"Urooge","suffix":""}],"badges":[],"createdAt":"2026-01-25 12:23:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8692320/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8692320/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103535418,"identity":"eeda6973-a935-411c-8176-92a57da2310d","added_by":"auto","created_at":"2026-02-26 18:15:04","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":239394,"visible":true,"origin":"","legend":"\u003cp\u003e3D loading plot from Principal Component Analysis (PCA) showing the contribution of soil physicochemical parameters and HMs.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8692320/v1/2045d37f619b690a2b5c6e2d.jpeg"},{"id":104398312,"identity":"5496728c-0ea2-40f2-82cc-fa5b7f46cdca","added_by":"auto","created_at":"2026-03-11 12:01:37","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":119595,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal Component Analysis (PCA) biplot showing the relationships between sampling sites and environmental variables.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8692320/v1/62889a14bb880520ac2cb748.jpeg"},{"id":103535419,"identity":"4e0f3314-46f0-4736-b442-7927966e983c","added_by":"auto","created_at":"2026-02-26 18:15:04","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":771097,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation matrix of soil parameters and HMs\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8692320/v1/0f8218c56428e80996c86197.jpeg"},{"id":104407389,"identity":"df2d67c5-b982-474c-9928-445dd308365f","added_by":"auto","created_at":"2026-03-11 12:37:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1829050,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8692320/v1/ef5fca81-dbbf-4ce7-b46d-f7efd2ec68c8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Multivariate Analysis of Heavy Metal Pollution and Salinity Interactions in Saline Soils for Agricultural Management","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSalt occurs naturally in water and soil. However, when there is too much salt, the soil is described as saline, meaning that the soil does not meet our economic, aesthetic, and environmental needs. This needs to be solved for our landscapes and ecosystems to be productive and healthy (Waris et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Solution to such issues are crucial for the environmental well-being of the area and sustainable agriculture (JAMPASRI \u0026amp; SAENG-NGAM, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Soil salinization threatens the biochemical, hydrologic, and erosion processes that favor Earth's environments and natural biological procedures that are indispensable for life. Multiple studies demonstrate that heavy metals like Cd, Cr, and Pb contaminate soil, water, and air through both natural and anthropogenic sources. Their toxicity depends on exposure dose, route, and individual factors like age and genetics (Hassan et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Ishak et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Severe health consequences including neurological disorders, developmental delays, and increased cancer risks. specifically highlights molecular toxicity mechanisms like DNA damage, oxidative stress, and disrupted cellular processes (Ogwu et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The evidence is robust, with studies spanning terrestrial and aquatic organisms showing consistent oxidative stress mechanisms. Over a dozen environmental pollutants capable of ROS overproduction, including ozone, nitrogen oxides, particulate matter, and pesticides. Critically, the oxidative stress potential varies by pollutant type and concentration, with ultrafine particles and transition metals being particularly potent (Ahandani et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Talas et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe most salinized soils are found in several Central and Western Asian nations, such as Pakistan, China, America, India, Argentina, Sudan (Kumar \u0026amp; Sharma, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Tahir \u0026amp; Khaliq, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). To maintain the agricultural legacy and ensure food security, this trend demands utmost attention (Ayangbenro \u0026amp; Babalola, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Chaoua et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Low-quality groundwater is used for irrigation because there is a lack of high-quality water sources. One of the main ways that saltwater enters the Indus Basin and causes issues with salt and water salinization is through the overuse of such low-quality groundwater (Caglar et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Qureshi et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Salinity is another crucial factor that affects metal toxicity, mobility, and transport. According to research, HMs have a high level of environmental resistance and may bioaccumulate over time. The chemical characteristics, concentration, and soil availability largely define toxic metal toxicity and mobility of toxic metals. Compared to higher salinity, the biologic impact of HMs is much greater at low salinity (Bai JunHong et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The mobility and toxicity of HMs in soils depend on their chemical characteristics, concentration, and availability. Lower salinity levels can have a more significant biological impact on HMs (Bai JunHong et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Soil contamination with HMs is exacerbated using wastewater, pesticides, fertilizers, and improper disposal of industrial and urban waste. Addressing these sources is critical for environmental protection and public health (Waris et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The global issue of HMs contamination in saline soils, particularly concerning metals such as Cd, Co, Cr, Cu, Pb, and Zn, poses a serious ecological challenge (Bartkowiak et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Multivariate statistical analysis is essential for understanding and managing soil contamination, facilitating the efficient processing of data to uncover patterns and relationships. Techniques such as PCA and regression analysis have proven effective in revealing the underlying trends and complex correlations in environmental data (Baig et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Chudasama et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study aimed to analyze HMs concentrations in saline soils from four sites in Sukkur, Sindh, Pakistan. By employing multivariate analysis, we can differentiate between anthropogenic and natural sources of HMs pollution, providing crucial information for targeted remediation efforts and enhancing environmental health.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSampling of BSS\u003c/h2\u003e \u003cp\u003eTo obtain spatial variation, a stratified random sampling technique resulted in a total of 200 saline soil samples from four Sukkur sites (50 samples each). Following surface debris removal, samples were collected from the 0\u0026ndash;15 cm soil layer using a stainless-steel auger. Clean, identified plastic bags held each sample (~\u0026thinsp;500\u0026ndash;1000 g). Triplicate and blank samples were included for quality control, and equipment was washed between collections. Before physico-chemical analysis, air-dried and sieved (2 mm) all samples were cooled and transported to the laboratory.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eChemical reagents and glassware\u003c/h3\u003e\n\u003cp\u003eDeionized water was obtained using a purification system (Bedford, MA, USA). Merck (Darmstadt, Germany) purchased analytical-grade hydrogen peroxide and nitric acid. Filter paper (0.45 \u0026micro;m) was purchased from Biotech (Germany). Standard solutions (1,000 mg/L) were diluted to create working standards for Cd, Co, Cr, Pb, Cu, and Zn. The analysis involved the use various equipment, including a conductivity meter, flame atomic absorption spectrometer (FAAS), and graphite furnace for evaluating HMs.\u003c/p\u003e\n\u003ch3\u003ePhysical-chemical evaluations of salty soil\u003c/h3\u003e\n\u003cp\u003eThe EC, pH, and organic matter (OM) quality features of each soil sample were evaluated using recognized methods (Waris et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). A 1:2.5 soil-to-water ratio was used to evaluate characteristics, and a conductivity meter measured EC. The OM content was determined by heating the soil samples in a muffle furnace at 540\u0026deg;C for six hours, with calculations based on weight differences before and after ashing (ASTM, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eSamples preparation procedure HMs in BSS\u003c/h3\u003e\n\u003cp\u003eSoil samples (5 g each) were combined with an acid solution (6 ml HNO\u003csub\u003e3\u003c/sub\u003e and 2 ml H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e) to break down organic material. The mixtures were heated on an electric hot plate for two-three hours. Finally, the digested samples were analyzed using FAAS and graphite furnace atomic absorption spectroscopy (GFAAS).\u003c/p\u003e\n\u003ch3\u003eChemometric analysis\u003c/h3\u003e\n\u003cp\u003eMultivariate statistical analysis was employed to identify hidden patterns in large datasets that could indicate HMs contamination sources. PCA and cluster analysis were used to differentiate metal sources in soils (Young \u0026amp; Hammer, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Regression analysis was used to evaluate the metal-soil property relationships, and all analysis was conducted using OriginPro statistical software to achieve accuracy (Hou et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e"},{"header":"Results and discussion","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003ePhysico‑chemical analysis of BSS\u003c/h2\u003e \u003cp\u003eThe relationship between soil salinity and HMs mobility remains unclear. The increased mobility of HMs due to salinization is a significant concern that must be considered in environmental risk assessments (Acosta et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). In addition, OM content is very important for maintaining soil integrity; however, our findings revealed abysmally low rates of between 0.620% and 1.45%. This indicates that the land is not sufficient for agriculture, thus the necessity of taking prompt restoration measures. Soil pollution with HMs is a severe environmental issue owing to the high toxicity and persistence of the pollutants. This problem negatively impacts soil quality, and it is difficult to identify areas with fertile soils (Waris et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Several aspects of biological life are threatened by non-biodegradable chemicals and dangerous chemicals that are beyond safety thresholds. Cd concentrations in BSS samples range from 1.11 to 3.71 \u0026micro;g/g, Co concentrations range from 17.8 to 23.9 \u0026micro;g/g, Cr concentrations range from 6.07 to 23.9 \u0026micro;g/g, Cu concentrations range from 108 to 193 \u0026micro;g/g, Pb concentrations range from 4.33 to 11.1 \u0026micro;g/g, and Zn concentrations range from 118 to 181 \u0026micro;g/g (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Heavy metal contamination in soils frequently exceeds international safety standards, posing significant risks to crop production and human health. WHO target values for heavy metals follow the order Cr\u0026thinsp;\u0026gt;\u0026thinsp;Pb\u0026gt;Zn\u0026thinsp;\u0026gt;\u0026thinsp;Cu\u0026gt;Cd, with Cd presenting the highest potential risk (target value of 0.8 \u0026micro;g/g. The soils surrounding industrial companies were contaminated with Cd and Pb, exceeding USEPA and European Union standards, while some heavy metal concentrations were within acceptable thresholds, Cd levels consistently exceeded the permissible limit of 3.00 \u0026micro;g/g (Caglar et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; T\u0026oacute;th et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Yang et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The occurrence of these HMs in BSS is of considerable concern because they are bioavailable, transportable, and ecotoxic to ecosystems. Thus, the determination of the total concentration of these metals does not entirely indicate environmental pollution, hence the need to conduct additional studies are required (Waris et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe concentration of HMs (\u0026micro;g/g) in BSS samples is based on physicochemical parameters.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBSS1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBSS2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBSS3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBSS4\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003cp\u003e7.28\u0026ndash;7.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003cp\u003e7.72\u0026ndash;7.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e \u003cp\u003e7.78-8.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69\u003c/p\u003e \u003cp\u003e6.45\u0026ndash;7.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEC (dS/m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.44\u003c/p\u003e \u003cp\u003e4.45\u0026ndash;5.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.72\u003c/p\u003e \u003cp\u003e4.19\u0026ndash;5.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61\u003c/p\u003e \u003cp\u003e4.50\u0026ndash;5.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.62\u003c/p\u003e \u003cp\u003e4.48\u0026ndash;5.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOM (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33\u003c/p\u003e \u003cp\u003e1.34-2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e \u003cp\u003e0.73\u0026ndash;0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003cp\u003e0.63\u0026ndash;0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e \u003cp\u003e0.86\u0026ndash;1.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29\u003c/p\u003e \u003cp\u003e1.60\u0026ndash;2.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e \u003cp\u003e1.16\u0026ndash;1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.71\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29\u003c/p\u003e \u003cp\u003e3.42-4.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e \u003cp\u003e0.94\u0026ndash;1.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.36\u003c/p\u003e \u003cp\u003e18.9\u0026ndash;20.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.31\u003c/p\u003e \u003cp\u003e21.6\u0026ndash;26.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.65\u003c/p\u003e \u003cp\u003e15.2\u0026ndash;20.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.98\u003c/p\u003e \u003cp\u003e17.9\u0026ndash;21.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.09\u0026thinsp;\u0026plusmn;\u0026thinsp;1.18\u003c/p\u003e \u003cp\u003e4.91\u0026ndash;7.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.61\u003c/p\u003e \u003cp\u003e12.3\u0026ndash;17.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.09\u0026thinsp;\u0026plusmn;\u0026thinsp;1.76\u003c/p\u003e \u003cp\u003e7.33\u0026ndash;10.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.51\u003c/p\u003e \u003cp\u003e9.39\u0026ndash;12.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e108\u0026thinsp;\u0026plusmn;\u0026thinsp;4.81\u003c/p\u003e \u003cp\u003e104\u0026ndash;112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e112\u0026thinsp;\u0026plusmn;\u0026thinsp;5.54\u003c/p\u003e \u003cp\u003e106\u0026ndash;118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e119\u0026thinsp;\u0026plusmn;\u0026thinsp;4.00\u003c/p\u003e \u003cp\u003e115\u0026ndash;123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e193\u0026thinsp;\u0026plusmn;\u0026thinsp;5.79\u003c/p\u003e \u003cp\u003e187\u0026ndash;199\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.93\u003c/p\u003e \u003cp\u003e3.40\u0026ndash;5.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.87\u003c/p\u003e \u003cp\u003e4.54\u0026ndash;6.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.23\u003c/p\u003e \u003cp\u003e9.87\u0026ndash;12.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.89\u0026thinsp;\u0026plusmn;\u0026thinsp;1.23\u003c/p\u003e \u003cp\u003e8.66\u0026ndash;11.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e133\u0026thinsp;\u0026plusmn;\u0026thinsp;4.56\u003c/p\u003e \u003cp\u003e128\u0026ndash;138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e118\u0026thinsp;\u0026plusmn;\u0026thinsp;4.11\u003c/p\u003e \u003cp\u003e114\u0026ndash;122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e119\u0026thinsp;\u0026plusmn;\u0026thinsp;3.09\u003c/p\u003e \u003cp\u003e116\u0026ndash;122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e181\u0026thinsp;\u0026plusmn;\u0026thinsp;4.19\u003c/p\u003e \u003cp\u003e177\u0026ndash;185\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePrincipal Component Analysis\u003c/h3\u003e\n\u003cp\u003ePCA was used to investigate the concentrations of HMs in BSS, revealing their presence and spatial distribution. These data are crucial for assessing potential risks and formulating management strategies in contaminated regions (Deljomanesh et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Additionally, PCA is a useful tool for assessing how human activity affects river water quality, emphasizing the need to use a variety of approaches to pinpoint HMs sources in river basin regions areas (Ogwu et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this study, a dataset of physicochemical parameters and HMs content dataset from different soil samples was subjected to PCA. This was performed to decrease the number of descriptors that may be responsible for the total variance in the experimental data. Independent analyses were performed on normalised datasets of BSS with nine variables for four different sampling locations (n\u0026thinsp;=\u0026thinsp;24) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The largest variances in the data were accounted for by the first principal component (PC1), 53% explained. PC1 in most datasets can be used to define an overall trend or prevailing factor, such as a contamination zone or fertility gradient, according to data context. The second principal component (PC2) accounted for a further 27.4% unexplained variability by PC1. Values closer to PC2 than to PC1 indicate a trend or pattern in development, presumably of a different factor than that accounted for by PC1. A further 19.7% of the unexplained variability that neither PC1 nor PC2 could explain was described by the third main component, PC3. A secondary gradient, such as industrial pollution, is created when variables that point with long arrows on the PC3 axis contribute significantly to this third axis of variation and may indicate a common source or type of behavior in the soil (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLoadings of experimental variables (36) on key principle components for BSS samples.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003ePC 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003ePC 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003ePC 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.410\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e-0.213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e-0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eEC (dS/m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.430\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eOM %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e-0.401\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e-0.250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0.230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCd (\u0026micro;g/g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.370\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0.607\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCo (\u0026micro;g/g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e-0.118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0.080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCr (\u0026micro;g/g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e-0.580\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCu (\u0026micro;g/g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e-0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e-0.490\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0.420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePb (\u0026micro;g/g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.409\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e-0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0.233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eZn (\u0026micro;g/g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e-0.152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eEigenvalue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e5.208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e2.740\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e2.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTotal variance %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e54.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e28.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e20.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCumulative %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e54.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e81.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe first principal component, the X-axis (PC1), explained 48.32% of the overall variation in the data. The second principal component, which explained 33.68% of the variation, was the Y-axis (PC2). 82% of the data variance may be explained by these two factors (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This pattern is observed in cases effected by industrial processes, mining, or agricultural runoff, ehre the same forces affect all three readings. EC within a system is most often affected by dissolved salts, nutrients like nitrogen, and HMs (Ogwu et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The correlation values in the matrix indicate that soils with high EC values are likely to have greater dissolved concentration of metals and nutrients, and EC can thus be used as an indicator of soil contamination or fertility. High pH levels precipitate Cu compounds or enhance their adsorption by soil particles. These modifications can decrease the mobility and bioavailability of Cu but can sustain a higher total soil concentration (Rees et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Salinity can affect both the chemical speciation and pH of Cu. The presence of salt in the soil can influence the activity of Cu ions, which could result in a situation where an elevated pH corresponds to enhanced Cu retention (Wu et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCluster analysis (CA) was applied to a database of HMs composition and EC for the four saline soil sampling points. The objective was to identify spatial differences and similarities among these sites (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). HMs such as Cd, Cr, Cu, Pb, and Zn may occur in various oxidation states and interact with soil constituents. The strong binding of EC to Cd and Pb implies a probable correlation (Khan et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This is evidence to prove the fact that regions with high EC also exhibit high concentrations of HMs due to human interventions or natural chemical weathering (Ali et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The findings would indicate a relationship between HMs pollution and EC, and probable implication of industrial effluent or dissolved salt (Mokarram et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eRegression analysis of physicochemical properties and HMs BSS\u003c/h2\u003e \u003cp\u003eRegression analysis was applied to measure the direction and magnitude of the linear relationship between two continuous variables. In the present study, it was used to determine whether changes in soil EC influence HMs release. An interesting effect of an increase in EC on mobilization of HMs was indicated (see Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The most dramatic and statistically significant alterations were found in the mobilizing of Cd, Co, and Cr in BSS1 and BSS2. An increase in EC was associated with a much higher release of Cd than of other metals. Cd and Cr were the most mobile, implying that they are more displaced by salts or are more likely to react with chloride than the other metals (Tangahu et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The % of Zn released showed a minor increase with rising EC, but this was less pronounced than the changes observed of Cd and Cr. These actions can be explained by the chemical interactions between soil particles and by salinity effects. These interactions will regulate in large measure the mobility and bioavailability of these metals in the environment. Therefore, these elements will have minimal mobility and leaching potential, and their relative stability in the soil (Porter et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2004\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRegression Analysis of HMs and EC in BSS\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBSS1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBSS2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBSS3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBSS4\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ey\u0026thinsp;=\u0026thinsp;0.0564x\u0026thinsp;+\u0026thinsp;1.750\u003c/p\u003e \u003cp\u003eR\u0026sup2; = 0.929\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ey\u0026thinsp;=\u0026thinsp;0.2125x\u0026thinsp;+\u0026thinsp;0.289\u003c/p\u003e \u003cp\u003eR\u0026sup2; = 0.913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ey\u0026thinsp;=\u0026thinsp;0.1970x\u0026thinsp;+\u0026thinsp;2.544\u003c/p\u003e \u003cp\u003eR\u0026sup2; = 0.223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ey = -0.479x\u0026thinsp;+\u0026thinsp;3.111\u003c/p\u003e \u003cp\u003eR\u0026sup2; = 0.491\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ey\u0026thinsp;=\u0026thinsp;1.265x\u0026thinsp;+\u0026thinsp;11.90\u003c/p\u003e \u003cp\u003eR\u0026sup2; = 0.851\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ey\u0026thinsp;=\u0026thinsp;1.419x\u0026thinsp;+\u0026thinsp;13.901\u003c/p\u003e \u003cp\u003eR\u0026sup2; = 0.400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ey\u0026thinsp;=\u0026thinsp;1.164x\u0026thinsp;+\u0026thinsp;22.298\u003c/p\u003e \u003cp\u003eR\u0026sup2; = 0.535\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ey\u0026thinsp;=\u0026thinsp;1.496x\u0026thinsp;+\u0026thinsp;20.580\u003c/p\u003e \u003cp\u003eR\u0026sup2; = 0.0702\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ey = -0.219x\u0026thinsp;+\u0026thinsp;6.007\u003c/p\u003e \u003cp\u003eR\u0026sup2; = 0.801\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ey = -7.271x\u0026thinsp;+\u0026thinsp;49.675\u003c/p\u003e \u003cp\u003eR\u0026sup2; = 0.798\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ey = -3.506x\u0026thinsp;+\u0026thinsp;25.308\u003c/p\u003e \u003cp\u003eR\u0026sup2; = 0.178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ey\u0026thinsp;=\u0026thinsp;0.401x\u0026thinsp;+\u0026thinsp;10.212\u003c/p\u003e \u003cp\u003eR\u0026sup2; = 0.0131\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ey = -12.130x\u0026thinsp;+\u0026thinsp;239.91\u003c/p\u003e \u003cp\u003eR\u0026sup2; = 0.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ey = -40.560x\u0026thinsp;+\u0026thinsp;330.27\u003c/p\u003e \u003cp\u003eR\u0026sup2; = 0.670\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ey\u0026thinsp;=\u0026thinsp;36.793x\u0026thinsp;\u0026minus;\u0026thinsp;24.901\u003c/p\u003e \u003cp\u003eR\u0026sup2; = 0.391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ey = -20.301x\u0026thinsp;+\u0026thinsp;287.18\u003c/p\u003e \u003cp\u003eR\u0026sup2; = 0.178\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ey = -0.567x\u0026thinsp;+\u0026thinsp;7.501\u003c/p\u003e \u003cp\u003eR\u0026sup2; = 0.0820\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ey = -0.276x\u0026thinsp;+\u0026thinsp;5.999\u003c/p\u003e \u003cp\u003eR\u0026sup2; = 0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ey\u0026thinsp;=\u0026thinsp;0.242x\u0026thinsp;+\u0026thinsp;8.401\u003c/p\u003e \u003cp\u003eR\u0026sup2; = 0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ey\u0026thinsp;=\u0026thinsp;2.011x\u0026thinsp;\u0026minus;\u0026thinsp;0.0101\u003c/p\u003e \u003cp\u003eR\u0026sup2; = 0.403\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ey = -5.021x\u0026thinsp;+\u0026thinsp;217.00\u003c/p\u003e \u003cp\u003eR\u0026sup2; = 0.0450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ey = -42.010x\u0026thinsp;+\u0026thinsp;411.0\u003c/p\u003e \u003cp\u003eR\u0026sup2; = 0.705\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ey\u0026thinsp;=\u0026thinsp;46.401x\u0026thinsp;\u0026minus;\u0026thinsp;38.111\u003c/p\u003e \u003cp\u003eR\u0026sup2; = 0.740\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ey\u0026thinsp;=\u0026thinsp;32.001x\u0026thinsp;+\u0026thinsp;27.010\u003c/p\u003e \u003cp\u003eR\u0026sup2; = 0.307\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eHeat map correlations study of physicochemical properties and HMs BSS\u003c/h2\u003e \u003cp\u003eHeatmaps will facilitate the identification of connections between pollutants and soil chemistry parameters. Finding likely connections or impacts of variables may be aided by strong positive or negative correlations. With an increase in the pH of the soil, Zn solubility in soil declined (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) Such a trend is typically observed in the common chemical behaviour of most metals, which have higher solubility in acids and reduced solubility in alkalis. High EC generally reflects the makeup of more soluble ions, such as metals like Cd. Industrial pollution, fertilizer application, and wastewater irrigation can result in high EC along with elevated HMs composition. Cu and Pb generally have the same origins such as industrial effluent, mining, or urban pollution and can co-exist in polluted soils (Sahay et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). A high positive correlation implies that whenever Cu content is higher, the Pb content will also be higher. Both metals can be carried by the same medium, as there is a common source of contamination, and the same soil conditions may favour their co-occurrence in solution. This type of information facilitates making informed decisions about liming, salinity control (for EC reduction) and mitigation of HMs contamination. Existence of strong correlation between polluters may imply a shared source of pollution with remediation implications (Selvam et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003ePCA indicated that the first three principal components account for 100% of soil variation, whereas EC has a strong correlation with HMs concentration. CA confirmed the correlation, especially between EC, Cd, and Pb, indicating shared sources or chemical transformation in saline environments. This analysis is interesting, especially with the concerning the third main component (19.7% of residual variation). Heatmaps describe key correlations between metal solubility and soil chemistry parameters and pollutants and demonstrate EC and pH as being important in metal solubility. Salinity controls and chemical reactivity with soil solids governs metal bioavailability, although most metals are relatively immobile with low leaching and substitution affinities. These correlations remediation of metal-polluted saline environments more efficient. Salinity reduction minimizes HMs pollution and promotes workplace health and agricultural productivity. Further studies on these parameters are necessary to ensure soil health.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have agreed to the submission to the current study and have no conflict of interest. It is confirmed that the proposed study is not submitted elsewhere for publication. On behalf of all authors, the corresponding author states that there is no conflict of interest. Meanwhile, there are no institutional, venders or public financial of non-financial conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors received no specific grant from any funding agency in the public, commercial, or non-profit sectors for this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. This study does not include any individual person’s data in any form.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics and Consent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate: Not applicable. The study involved physicochemical/analytical experiments only and did not require ethical approval or participant consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMuhammad Waris\u003c/strong\u003e conceptualized the study, designed the methodology, performed data analysis, and drafted the original manuscript. \u003cstrong\u003eNadir Hussain\u003c/strong\u003e comtributed to validation of results and critical revision of the manuscript.\u003cstrong\u003e\u0026nbsp;Summan Urooge\u003c/strong\u003e contributed to data collection, experimental work. All authors reviewed and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIt is declared that there are no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAcosta J, Jansen B, Kalbitz K, Faz A, Mart\u0026iacute;nez-Mart\u0026iacute;nez S. Salinity increases mobility of heavy metals in soils. Chemosphere. 2011;85(8):1318\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhandani EA, Terepoei ZA, Sheydaei M, Akram M, Selamoglu Z. Evaluation of Some Toxic Elements in the Soil of Langerud: Northern Iran. Am J Biomedical Sci Res. 2022;17:84\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAli H, Khan E, Ilahi I. Environmental chemistry and ecotoxicology of hazardous heavy metals: environmental persistence, toxicity, and bioaccumulation. 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Arab J Geosci. 2023;16(10):573.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWaris M, Kazi TG, Baig JA. (2021). Evaluation and speciation of cobalt, copper, and zinc in saline soil by microwave-assisted single extraction. Environ Prog Sustain Energy, 40(4), e13610.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu L, Yue W, Zheng N, Guo M, Teng Y. Assessing the impact of different salinities on the desorption of Cd, Cu and Zn in soils with combined pollution. Sci Total Environ. 2022;836:155725.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang Q, Li Z, Lu X, Duan Q, Huang L, Bi J. A review of soil heavy metal pollution from industrial and agricultural regions in China: Pollution and risk assessment. Sci Total Environ. 2018;642:690\u0026ndash;700.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYoung F, Hammer R. Soil\u0026ndash;landform relationships on a loess-mantled upland landscape in Missouri. Soil Sci Soc Am J. 2000;64(4):1443\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-geoscience","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Geoscience](https://www.springer.com/journal/44288)","snPcode":"44288","submissionUrl":"https://submission.nature.com/new-submission/44288","title":"Discover Geoscience","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Salty Soil, Heavy metals, PCA, CA, Environmental monitoring","lastPublishedDoi":"10.21203/rs.3.rs-8692320/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8692320/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study examined heavy metal (HMs) pollution in saline soils of Sukkur Sindh, Pakistan, and determined the relationship between salinity and metal content. Multivariate statistical methods such as regression analysis, principal component analysis (PCA), and cluster analysis (CA) were applied to characterize anthropogenic and natural factors affecting HMs in soils. Close relationships were found for electrical conductivity (EC) and cadmium (Cd), cobalt (Co), chromium (Cr), copper (Cu), lead (Pb), and zinc (Zn) concentrations, but correlation strength differed among soil samples. Bulk Saline Soil (BSS) samples had Cd (1.01\u0026ndash;3.62 \u0026micro;g/g), Co (18.6\u0026ndash;29.1 \u0026micro;g/g), Cr (5.07\u0026ndash;13.6 \u0026micro;g/g), Cu (122\u0026ndash;191 \u0026micro;g/g), Pb (4.01\u0026ndash;9.99 \u0026micro;g/g), and Zn (170\u0026ndash;193 \u0026micro;g/g). Regression analysis was also used to demonstrate the need for EC as well as the measurement of HMs concentration in agricultural management. PCA indicated that variables under PC2 were generally orthogonal to PC1, with a third factor (PC3) accounting for 19.7% data variance, demonstrating the presence of other independent factors influencing HMs accumulation aside from the main components described. The results emphasize the significance of integrated EC and HMs monitoring in successful salt-affected farmland management.\u003c/p\u003e","manuscriptTitle":"Multivariate Analysis of Heavy Metal Pollution and Salinity Interactions in Saline Soils for Agricultural Management","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-26 18:14:59","doi":"10.21203/rs.3.rs-8692320/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-31T10:38:20+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-25T05:56:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"239251027308987639007634136754137035430","date":"2026-03-15T13:40:41+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-12T20:13:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"224231843855966250950295564002585492433","date":"2026-03-12T12:25:15+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-08T09:12:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"61080795366509360386700915626700937035","date":"2026-03-05T10:56:14+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-25T11:26:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"183837337990970940780406469025345937813","date":"2026-02-23T18:00:13+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-23T17:44:32+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-23T04:44:21+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-18T06:05:06+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Geoscience","date":"2026-02-18T05:33:14+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-geoscience","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Geoscience](https://www.springer.com/journal/44288)","snPcode":"44288","submissionUrl":"https://submission.nature.com/new-submission/44288","title":"Discover Geoscience","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"464a4665-4868-4293-bce7-a55789a10934","owner":[],"postedDate":"February 26th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-10T13:10:44+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-26 18:14:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8692320","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8692320","identity":"rs-8692320","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}