
==== Front
Sci Rep
Sci Rep
Scientific Reports
2045-2322
Nature Publishing Group UK London

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72346
10.1038/s41598-024-72346-7
Article
Risk assessment and soil heavy metal contamination near marble processing plants (MPPs) in district Malakand, Pakistan
Khan Asghar asgharmkd35@gmail.com

12
Khan Muhammad Saleem 1
Hadi Fazal 3
Khan Qaisar 4
Ali Kishwar 5
Saddiq Ghulam 6
1 https://ror.org/02p2c1595 grid.459615.a 0000 0004 0496 8545 Department of Botany, Islamia College, Peshawar, Pakistan
2 grid.11173.35 0000 0001 0670 519X Department of Botany, Government Degree College, Totakan, District Malakand, Pakistan
3 https://ror.org/012xdha97 grid.440567.4 0000 0004 0607 0608 Department of Biotechnology, University of Malakand, Chakdara, Pakistan
4 https://ror.org/012xdha97 grid.440567.4 0000 0004 0607 0608 Material Chemistry Laboratory, University of Malakand, Chakdara, Pakistan
5 https://ror.org/041ddxq18 grid.452189.3 0000 0000 9023 6033 College of General Education, University of Doha for Science and Technology, Arab League Street, P.O. Box 24449, Doha, Qatar
6 https://ror.org/02p2c1595 grid.459615.a 0000 0004 0496 8545 Department of Physics, Islamia College, Peshawar, Pakistan
15 9 2024
15 9 2024
2024
14 2153323 7 2024
5 9 2024
© The Author(s) 2024
2024
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Soil heavy metals (HMs) pollution is a growing global concern, mainly in regions with rapid industrial growth. This study assessed the concentrations, potential sources, and health risks of HMs in agricultural soils near marble processing plants in Malakand, Pakistan. A total of 21 soil samples were analyzed for essential and toxic HMs via inductively coupled plasma‒optical emission spectrometry (ICP‒OES), and probabilistic health risks were evaluated via Monte Carlo simulation. The concentrations (mg/kg) of Ca (29,250), P (805.5) and Cd (4.5) exceeded the average shale limits of 22,100, 700, and 3.0 mg/kg, respectively, and indices such as Nemerow’s synthetic contamination index (NSCI) and the geoaccumulation index (Igeo) categorized the soil sites as moderately polluted. The potential ecological risk index (PERI) indicated considerable to high ecological risk for As and Cd. The deterministic analysis indicated non-carcinogenic risks for children (HI > 1), whereas the probabilistic analysis suggested no significant risk (HI < 1) for both adults and children. Both methods indicated that the total cancer risk for Cr, Ni, Cd, and As exceeded the USEPA safety limits of 1.0E-06 and 1.0E-04. Sensitivity analysis identified heavy metal concentration, exposure duration, and frequency as key risk factors. The study suggested that HM contamination is mainly anthropogenic, poses a threat to soil and human health, and highlights the need for management strategies and surveillance programs to mitigate these risks.

Keywords

Soil heavy metal pollution
Health risk assessment
Monte Carlo simulation
Compositional data analysis (CoDa)
Marble processing plants (MPPs)
Subject terms

Biogeochemistry
Environmental sciences
Health occupations
issue-copyright-statement© Springer Nature Limited 2024
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pmcIntroduction

Soil is an indispensable natural resource, fundamental to food production and biodiversity maintenance, and plays an important role in national security and global ecological stability1,2. However, the integrity of this resource is increasingly threatened by the pervasive issue of soil pollution, which has become a pressing global environmental concern3. The contamination of soil with hazardous substances such as radioactive elements, nanomaterials, and heavy metals significantly undermines soil resilience and disrupts ecosystems4,5. These pollutants primarily originate from anthropogenic activities, including industrialization, urbanization, mining, and the excessive use of agrochemicals, as well as natural events such as volcanic eruptions and erosion of mineral deposits6,7. The global significance of soil pollution lies in its far-reaching impacts on agricultural productivity, human health, and environmental sustainability8,9. Contaminated soils can lead to the accumulation of toxic elements in the food chain, posing severe risks to food security and public health on a global scale10,11. Moreover, soil degradation due to pollution exacerbates the loss of arable land, which is already under pressure from a growing global population12,13. Addressing soil pollution is therefore critical to ensuring the long-term viability of global food systems, the health of populations, and the preservation of biodiversity14,15.

Millions of tons of marble waste, consisting of fine powder and fragmented rock, are generated worldwide in marble quarries and marble processing plants (MPPs)16,17. During the quarrying and refining processes, nearly 70% of the valuable mineral resources of marble are lost, with approximately 40% of marble waste being released into the surrounding atmosphere, leading to widespread environmental pollution18,19. MPPs, particularly in Khyber Pakhtunkhwa, Pakistan, contribute significantly to wastewater and environmental pollution, as large amounts of waste are released as sludge and sludge containing harmful marble particles20–24. This waste, which is often disposed of improperly in water bodies and land, poses severe ecological risks, including damage to aquatic ecosystems, soil, and human health20,25,26. Despite the introduction of pollution control programs by the government, poor enforcement has hindered their effectiveness27,28. Although the marble industry plays a crucial role in socioeconomic development worldwide, growing concern about its impact on the environment has led to an increasing focus on recycling waste and byproducts to align with sustainable development goals19,29. Recently, modified biochar composites (Mar-BC800 and Sep-BC800) were developed from marble waste, calcium-rich sepiolite, and agricultural waste, and have been shown to be highly effective, eco-friendly, and cost-efficient for phosphate removal from waste streams30. Moreover, their potential for repurposing as fertilizers presents a sustainable solution for enhancing soil fertility and improving soil properties31.

In developing countries without prior treatment, farmers use marble wastewater as an alternative to irrigating crops because it is a low-cost and readily available resource32–34. In the short term, marble wastewater can have a positive effect on plant growth as it contains high quality nutrients such as calcium, potassium, iron and copper24,25. However, farmers may not be fully aware of the potential long-term consequences of this practice, as marble waste also contains heavy metals35–37. Heavy metals such as Al, As, Cd, Cr, Co, Ni, Pb, and Hg are toxic to plants even at low concentrations38–40. The accumulation of these elements at relatively high concentrations in agroecosystems poses a major threat to soil microbes and disrupts the distribution and structure of the microbial community4,41,42. Similarly, their toxic behavior in soil disrupts the morphological and physiological processes of crops by reducing their growth rate, nutrient imbalance, stomatal movement, and inhibition of photosynthesis43. In human and animal body tissues, soil heavy metals accumulate through the food chain44,45 and cause serious effects such as nephrotoxicity, neurotoxicity, hypertension, infertility, and carcinogenicity20,46,47. Moreover, prolonged exposure to marble waste through inhalation and dermal contact poses serious health risks to local populations and workers, including conditions such as urolithiasis and abnormal lung function16,17,22,35,48. Therefore, it is crucial to assess the risk and extent of soil heavy metal contamination near MPPs49. Such assessments are essential for developing effective mitigation strategies to protect both the environment and public health6,50.

Geochemical factors in soil, water and sediments contain valuable information on element ratios and should be treated as compositional data51. Using classical statistical tools directly on the input concentrations can lead to spurious results because different geometric rules govern compositional data52. Traditional methods such as Euclidean distance and covariance are unsuitable for geochemical concentrations, resulting in spurious elements53,54. Geochemical data are positive vectors, represented by a constant sum, that are distinct from real Euclidean space55. Log-ratio transformations such as the centered log-ratio (CLR), isometric log-ratio (ILR) and additive log-ratio (ALR) can handle compositional element concentration data56–58. CLR provides an alternative to raw data but introduces collinearity, whereas ILR avoids collinearity57,59. The application of CLR and CoDa principles, such as variation matrices, biplots, and dendrograms, allows the establishment of linear relationships between CLR variables and the differentiation of geochemical sources42,60. Identifying unusual patterns related to heavy metal exposure in the area of interest is crucial26.

Similarly, the evaluation of soil pollution status, identification of potential sources of contamination and appraisal of the prospective risks to human populations are prerequisites for the successful implementation of any action plan61. Soils near the marble industry are most likely polluted by marble effluents, marble slurry dumping and the production of marble powder from marble processing plants25. Numerous studies have been conducted in Pakistan on heavy metal water pollution related to the marble industry20,37. These studies have shown that marble industry effluents strongly affect water and sediment quality and cause the bioaccumulation of heavy metals in aquatic fauna and flora. Similarly, with respect to health risk assessment, most studies have typically focused on deterministic risk quantification of exposure to soil contaminated with heavy metals, which could either underestimate or overestimate the risk62,63 In addition, owing to the uncertainty of heavy metal concentrations and the specific variability between individuals, identifying the most hazardous element for the population via a deterministic method is difficult64,65. Fortunately, as the most common method of probabilistic risk analysis, Monte Carlo simulation can determine the likelihood of a risk being exceeded and help identify the priority metal for risk control66–68. Therefore, a comprehensive investigation that considers the metal enrichment status and probabilistic risk assessment of heavy metals at the national level is valuable for understanding the contamination traits and contributions of various sources prior to taking efficient measurements to improve soil quality and human health61,69. In addition, for the production of healthy crops and ensuring the health security of farmers, researchers, soil managers, livestock and common people, it is essential to study agricultural soils contaminated with marble waste25,70–72. Therefore, the current study aimed to (i) evaluate the extent of heavy metals (HMs) contamination in agricultural soils withassociated human health risks using both deterministic and probabilistic health risk assessment modelsand (ii) identify potential sources of heavy metal contamination in soil via a compositional multivariate technique.

Materials and methods

Background of the study area

The present study was conducted between September and December 2019 on agricultural soil near MPPs in the district of Malakand (Fig. 1).Fig. 1 The study area map showing sampling sites was generated via ArcGIS software version 10.2.2.

Geographically, District Malakand is located between 34.3718° N latitude and 72.2420° E longitude and is the gateway to Dir, Chitral, Swat, Shangla, and Buner Districts and the currently merged, federally administered, tribal area of Mohmand and Bajaur. Malakand District covers an area of 952 km2 with a population density of 475 inhabitants per km273. The land of Malakand District is fertile and surrounded by mountains rich in biodiversity. A large part of the agricultural land in the study area is irrigated by the Swat River (Fig. 1). Winters are very cold, and the temperature can decrease to – 2 °C in January. Summers are hot, and the temperature reaches 41 °C from June to July74. The average annual rainfall is between 600 and 650 mm75. Rice is an important cash crop with a total irrigated area of 4991 hectares76. The valuable mineral reserves reported from several locations in the Malakand district are mica, quartz, marble, Malakand granite, and Dargai chromite77. The coordinates of the individual sampling points were recorded by a global positioning system (Garmin eTrex 10). The study area map (Fig. 1) was generated via ArcGIS version 10.2.2.

Soil sampling and analytical methods

The agricultural soils in the study area are sandy loam. Soil samples were collected from 21 sites (Fig. 1) in triplicate at a depth of 0–20 cm via a hand auger following a quincunx sampling pattern78. Finally, one kilogram composite sample for each site was prepared by thoroughly mixing the five sub soil samples and sealed in clean, labeled polyethylene bags79,80. The soil samples were air dried, mechanically ground, sieved through 2 mm sieve and stored for further analysis81. 0.5 g of the dried soil sample was added in triplicate to a 50 mL Erlenmeyer flask with 26 mL of a triacid mixture of HNO3, HCl, and HF at a ratio of 9:3:1 and stored overnight82. The samples were completely digested on a hot plate by gently increasing the temperature to 155 °C until white dense fumes emerged83. After cooling, the digested sample was diluted to 50 mL with distilled water, filtered through Whatman filter paper (0.45 μm) in a clean volumetric flask and stored at room temperature84. The concentrations of essential elements and heavy metals in the extracted soil solutions were analyzed using inductively coupled plasma optical emission spectrometry (ICP‒OES; Thermo Scientific, iCAP 6000, UK).

Physicochemical analysis of the soil samples

A soil suspension was prepared using a soil to water ratio of 1:2.5 in double deionized water, then stirred and centrifuged at 3000 rpm for 4 h. The pH, electrical conductivity (EC), and total dissolved solids (TDS) of the soil samples were determined via a portable meter (Model PH-2012, WT01 China)85.

Quality assurance and quality control

For quality assurance and quality control, special attention was given to sampling, preservation, transportation, storage and each experimental technique used in laboratory analysis86. Analytical-grade acids and reagents provided by Merck (Germany) were used in this study. The standard reference material (SRM) used to construct calibration curves of the tested elements was IAEA-Soil-7. The reference soil material (IAEA-Soil-7) was prepared and analyzed according to the certified analytical standard quality control procedure87. Eight different linear concentration standards were prepared. Each element with a good linear plot and a correlation coefficient greater than 0.999 was observed in the construction of standard curves. The traceability of the standards was verified via high-performance ICP‒OES against the corresponding SRM (ISO 2009). Each sample was analyzed in triplicate, and the mean value was used to interpret the results. The value of the relative standard deviation (RSD) for the analyzed elements was ≤ 2.0% (Table S1). The limit of detection (LOD) for each element was determined by digesting five blanks (Table S1). The recoveries were within the EPA limits of 92–123% (Table S1).

Assessment methods for soil heavy metal pollution

In the present study, the soil heavy metal pollution status was assessed through pollution indices developed by Muller87,88; Hakanson90 and Tomlinson et al.91.

Pollution Index and Nemerow Synthetic Contamination Index

The pollution index (Pi) reflects the pollutant level and primary exposure factors, serving as a tool and is used to assess heavy metal pollution in soil92. Pi can be calculated via the following equation.1 Pi=CiBi

where Ci is the measured concentration of the ith element and Bi is the background value of the ith element in the world average shale93. The Nemerow synthetic contamination index (NSCI) is used to represent the overall level of soil contamination by heavy metals. Soil HM contamination is a complicated process triggered by the synchronized action of numerous heavy metals94. The NSCI can be calculated via Eq. 2.2 NSCI=Piave2+Pimax22

where Piave is the mean value of Pi for the analyzed HM and Pimax is the maximum Pi value of the HM in a sample. The soil risk categories based on the Pi and NSCI are listed in Table S2.

Index of geo-accumulation (Igeo)

The index of geo-accumulation (Igeo), originally defined by Muller88 is used to estimate the natural and anthropogenic factors of soil contamination95. This method assesses the environmental impact while considering preindustrial and existing metal concentrations. The Igeo is calculated using the following equation, adapted from El Azhari et al.96.3 Igeo=log2Cn1.5Bn

where Cn signifies the measured concentration (mg/kg) of metals in the soil sample and Bn is the geochemical background (mg/kg) of the tested metal. A factor of 1.5 compensates for background values of the respective metal owing to minor environmental and anthropogenic influences. On the basis of Igeo, Muller88 proposed seven risk categories, as shown in Table S2.

Ecological risk index (Eri)

Eri measures the potential ecological risk of a single element in soils90 and can be calculated as follows:4 Eri=Tri×Pii

where Eri is the potential ecological risk factor for the ith trace element and where Tri is the coefficient of the ith element, which is 10 for As; 30 for Cd; 05 for Co, Cu and Ni; 01 for Mn and Zn; and 02 for Cr and V90,97–100. Pii is the pollution index of trace elements taken from Eq. (1). The evaluations of Eri are listed in Table S2.

Potential ecological risk index (PERI)

The comprehensive description of the ecological risk from a wide range of potentially toxic metals was determined through the application of the potential ecological risk index (PERI)90,101. The PERI results were calculated via the following equation.5 PERI=∑i=1nEri=∑i=1nTri×Pii

where PERI designates the cumulative ecological risk of trace elements, Eri is the potential ecological risk factor for the ith trace element and ni is the number of trace elements, which is 09 in this study. Tri is the coefficient of the ith element, and Pii is the pollution index of the trace elements taken from Eq. 1. The evaluations of PERI are listed in Table S2.

Assessment of health risk

Exposure assessment (noncarcinogenic)

Human exposure to soil heavy metals (HMs) typically occurs through accidental ingestion, inhalation, and dermal contact with soil particles from water and air102,103. In the present study, the noncarcinogenic health risks from individual heavy metal across the three exposures routes were calculated via the following equations 104.6 ADDing=Csoil×Ring×EF×EDBW×AT×CF

7 ADDinh=Csoil×IRinh×EF×EDBW×AT×PEF×CF

8 ADDdermal=Csoil×SA×AF×ABF×EF×EDBW×AT×CF

where ADDing, ADDinh, and ADDdermal are the average daily doses (mg/kg/day) of HM through ingestion, inhalation, and dermal contact, respectively. All other factors and values used in Eqs. 6–8 to estimate the exposure level and risks are listed in Table S3.

HQ is the ratio of the average daily dose (ADD; mg/kg/day) and the reference dose (RfD; mg/kg/day) for a given toxicant. HQ values > 1 signify the occurrence of a noncarcinogenic risk105 and can be calculated via Eq. (9).9 HQ=HQing+HQinh+HQdermal=ADDingRFDing+ADDinhRFDinh+ADDdermalRFDdermal

where HQing, HQinh, and HQdermal are hazard quotients (HQs) for the exposure routes, and RFDing, RFDinh, and RFDdermal represent the corresponding reference doses listed in Table S4.

Hazard index

The hazard index (HI) estimates the overall noncarcinogenic health risks from exposure to multiple potentially toxic HMs. HI is the sum of all HQs computed for individual HMs for a given route of exposure106. The HI can be calculated via the following equation.10 HI=∑K=1nHQ=HQ1+HQ2+⋯....+HQn

where 1, 2, …, n is the HQ calculated for individual toxic HMs. The population is considered safe if HI < 1 and vulnerable if HI > 1107.

Carcinogenic risk assessment

The carcinogenic risk (CR) assessment estimates the likelihood of an increased risk of cancer for a person due to lifetime exposure to potentially toxic elements. The cancer risk from lifetime exposure to As, Cr, Cd, and Ni was calculated across the three exposure routes via the following Eqs. 105.11 CR=ADD(ingestion,inhalation,dermal)×CSF

12 TCR=∑Cancer risk=CRing+CRinh+CRdermal

where CR is the cancer risk from the individual exposure pathway, and TCR is a cumulative cancer risk. CSF is the cancer susceptibility factor (mg/kg/day) of the HMs listed in Table S4. According to the USEPA108, cancer risk values below 1 × 10−6 are considered insignificant, while those higher than 1 × 10−4 are likely to be detrimental. According to the classification of the WHO109, As, Ni, Cd, and Cr (VI) compounds are being studied as Group 1 carcinogens; Pb compounds as Group 2 A; and Co and its compounds as Group 2B. In contrast, Cu, Zn, and Mn are not classified as carcinogenic, and their cancer risk has not been studied.

Probabilistic risk assessment

Uncertainty is inherent in risk assessments because of limited precise information, variability in environmental systems, and differences in individual human characteristics110,111. To address these uncertainties, Monte Carlo simulation, a random sampling method based on probability theory, is commonly used to quantify variability and uncertainty in risk assessment112. To achieve this, the probability distributions of the variables listed in Table S3 were used as input parameters to evaluate the probability functions for the health risks113. After performing 10,000 iterations, stable exposure distribution results were derived, and the values at quantiles 5th and 95th of the exposure distribution were used to assess the probabilistic risk114. A sensitivity analysis was conducted to evaluate the degrees of contribution of the exposure factors to the results. A positive value indicates a positive correlation between the exposure factor and health risks, while a negative value indicates a negative correlation115,116.

Statistical analysis

Descriptive statistical characteristics, including the mean, standard deviation (SD), standard error (SE), skewness, kurtosis, and coefficient of variation (CV), of the 19 elements were calculated via SPSS software (version 25 for Windows). Monte Carlo simulation, sensitivity analysis and the best-fitting distribution for each factor were performed via Oracle Crystal Ball version 11.1.3.0. Compositional data analysis approaches such as centered log-ratio (clr) transformation, variation matrix, clr-biplot, coda-dendrogram, and factor analysis were applied to establish a linear relationship between the variables and differentiate the source of contamination in agricultural soils42,59,60,117. The variation matrix (Table 4), clr-biplots (Fig. 14a,b), and Coda-dendrogram (Fig. 15) were generated via Coda-Pack software118 and the R package ‘‘compositions’’119. The factor analysis (Table 5) was conducted using the statistical software JMP version 16.0120.

Ethical approval and consent to participate.

The current research study was approved by the Institutional Research Committee of the Department of Botany at Islamia College Peshawar. The article does not include any humans or animals as research objects. All methods were conducted following the appropriate guidelines outlined in the methods section.

Results and discussion

Assessment of physicochemical properties and heavy metal concentrations

The assessment of physicochemical properties and heavy metal concentrations in soils near marble processing plants showed significant variation in the studied parameters (Table 1). The soil pH, ranging from 7.3 to 9.0, with a mean of 8.3, indicates slightly alkaline conditions. Similarly, EC and TDS values ranging from 213.3 to 556.6 µs/cm and 2231 to 1072.67 mg/L, with mean values of 374.60 and 635.80, respectively, indicate variability in soil salinity levels. These variations in soil pH and salinity may be attributed to differences in water quality, soil composition, and the influence of industrial activities, such as marble processing, on the surrounding environment23,121. High salinity levels could lead to soil degradation, impacting agricultural productivity122. Similarly, soil alkalinity impacts both physical and chemical properties, including texture, structure, color, infiltration, porosity, surface crusting, swelling, pH, and nutrient availability123. Managing soil alkalinity and salinity through proper irrigation practices, soil amendments, and crop selection is crucial for maintaining soil health and ensuring successful agricultural productivity124,125. Similar results were also reported by Eman et al.126, which is consistent with our current results. Table 1 Descriptive statistics of physicochemical properties and heavy metal (HM) concentration (mg/kg) in agricultural soils near MPPs in District Malakand.

	Statistics	Background concentration	
Variables	Mean	Min	Max	SE	SD	CV (%)	Skewness	Kurtosis	Average Shale	FAO	
pH	8.3	7.3	9.0	0.18	0.48	5.80	 − 1.20	3.10	 − 	6–8.5	
EC (µs/cm)	374.60	213.3	556.6	31.33	84.25	29.20	0.16	0.71	 − 	400	
TDS (mg/L)	635.80	223.0	1072.67	100.50	266.0	41.80	0.15	0.88	 − 	450	
Al	10,914.30	5100	23,250	1067.8	4893.3	44.8	1.20	1.10	80,000	 − 	
As	5.30	2.0	10.0	0.50	2.30	42.0	0.70	 − 0.48	13	20	
Ca	19,881.50	12,650	29,250	1163.5	5331.7	26.8	0.4	 − 1.0	22,100	 − 	
Cd	2.0	0.5	4.50	0.30	1.40	74.50	0.74	 − 0.86	0.3	3	
Co	6.0	2.0	11.0	0.50	2.40	41.30	0.46	 − 0.21	19	50	
Cr	27.0	13.0	40.0	1.60	7.50	27.90	 − 0.51	 − 0.35	90	100	
Cu	20.10	6.50	40.50	2.30	10.50	52.30	0.60	 − 0.66	45	100	
Fe	4699.71	1075	13,200	555.2	2544.0	54.10	2.0	5.50	47,200	50,000	
K	2105.70	734.5	3990.5	207.6	951.6	45.20	1.0	 − 0.90	26,600	 − 	
Mg	5420.71	2438.5	8050	365.8	1676.1	30.90	0.5	 − 0.80	15,000	 − 	
Mn	149.30	68.50	315.5	16.0	73.40	49.20	0.96	 − 0.17	850	2000	
Ni	18.40	12.50	27.0	0.90	4.10	22.40	0.40	 − 0.56	68	50	
P	292.0	65.5	805.5	45.2	207.3	71.10	1.1	0.60	700	 − 	
Si	12.50	1.50	25.50	1.50	7.0	55.90	0.3	 − 0.50	7300	 − 	
Sr	35.14	13.50	60.50	2.90	13.40	38.0	0.47	 − 0.57	300	 − 	
Ti	600.50	125.50	1471.0	82.70	378.9	63.10	1.16	0.32	4600	 − 	
V	39.21	16.50	59.0	2.70	12.50	32.0	 − 0.27	 − 0.45	130	 − 	
Zn	5.80	0.50	19.0	1.30	6.10	106.70	1.21	0.08	95	300	
Zr	3.0	1.0	4.50	0.20	1.10	37.50	 − 0.01	 − 1.01	160	 − 	
SE; standard error, SD: standard deviation; CV: coefficient of variance.

The highest mean concentration (mg/kg) was observed for Ca (19881.50), followed by Al (10914.30), Mg (5420.71), Fe (4,699.71) and K (2105.70) (Table 1). Titanium (Ti) had a mean concentration of 600.50 (mg/kg), whereas phosphorous (P) recorded a mean concentration of 292.0 (mg/kg). The maximum concentrations (mg/kg) of Ca (29250) and P (805.5) exceeded the world average shale concentration (Table 1). These findings are consistent with those of Aukour and Al-Qinna33, Ozcelik127, Ahmad et al.16 and Noreen et al.128, who reported elevated levels of these elements in soils and water impacted by industrial activities such as marble processing and mining. The high Ca concentrations likely reflect the presence of calcareous materials, which are common in regions with intensive marble processing129. Elevated P levels could be linked to industrial discharge or the accumulation of phosphate-rich dust130. Such elevated concentrations may have ecological implications, including potential disruptions to soil nutrient balances and risks of eutrophication in nearby water bodies131. The findings underscore the need for monitoring and managing these elements to mitigate potential environmental and agricultural impacts.

The mean concentrations (mg/kg) of Mn (149.30), V (39.21), Sr (35.14), Cr (27.0), Cu (20, 10) and Ni (18.40) were found to be within the limits of world average shale (Table 1), indicating relatively lower contamination levels105. Similarly, the mean concentrations (mg/kg) of Si (12.50) Co (6.0), Zn (5.80), As (5.30), Zr (3.0), and Cd (2.0) were below the FAO permissible limits (Table 1), suggesting a lower risk to soil quality and ecosystem health132. However, the maximum concentration of Cd (4.50 mg/kg) exceeded the FAO limit of 3.0 mg/kg (Table 1), raising concerns about potential soil and human health risks133. Similar results were also reported by Khan et al.36 for Cd concentrations in different marble industries of Khyber Pakhtunkhwa, Pakistan, highlighting consistent patterns of cadmium accumulation in soils associated with marble processing activities in the region. Elevated cadmium levels have been associated with industrial emissions and waste, particularly in areas affected by mining and manufacturing activities134. Cadmium is a toxic metal that can accumulate in the food chain, posing long-term health hazards135.

The coefficient of variation (CV) quantifies the spatial variation in geochemical variables136. In the present study, the coefficient of variation (CV) values varied from 22.40 to 106.70% (Table 1). Low variations were obtained for Ni, Ca, Cr, Mg, V, and Zr; moderate variations were obtained for Si, Fe, Cu, Mn, K, Al, As, and Co; and high variations were obtained for Ti, P, Cd and Zn (Table 1). According to Zuo137, small CV values are associated with areas that have similar geochemical backgrounds, while large CV values characterize areas that have different geochemical backgrounds. The irregular distribution of elements in the current study area seems to be influenced by anthropogenic rather than natural influences138. The statistical skewness and kurtosis indices were used to ascertain the normality or abnormality of the heavy metal distribution139. The kurtosis values of Al, Fe, P, Ti and Zn were greater than zero, indicating steeper than normal distributions of these elements (Table 1). The skewness values of Al, K, Fe, P, Ti and Zn were greater than one, indicating that these elements were positively skewed to lower concentrations, highlighting potential environmental concerns140,141. Understanding these associations helps in identifying the sources of soil contamination and in developing strategies to mitigate their impact on soil health and agricultural productivity142.

Assessment of soil heavy metal pollution

Pollution indices are among the most effective tools for assessing heavy metal contamination in soils, offering more insight than simply measuring total metal concentrations143. In this study, single indices were calculated to evaluate the pollution levels of individual heavy metals in the soil. These indices are based on the total metal concentrations, along with geochemical background levels or preindustrial baselines, to provide a comprehensive assessment of soil contamination. Similarly, the Nemerow synthetic contamination index (NSCI) is employed to assess contamination levels across different sites144. The results of the pollution index (Pi) (Table S5) show that the Pi values (1 ≤ 2) for As are indicative of slight pollution at sites S1-S3 and S14; larger variations in Pi values (2 ≤ 3) for As indicate moderate pollution at soil sites S6-S9 and S15–S18; and the Pi values > 4 are indicative of heavy pollution at soil sites S4, S11-S14, and S19-S21. Similarly, Pi values between 1 and 2 for Cd indicate slight pollution at soil sites S1, S6, and S8-S10; moderate pollution (2 ≤ Pi < 3) at sites S2–S5, S7, and S17; and heavy pollution (Pi > 4) at soil sites S11–S16 and S18–21. The Nemerow synthetic contamination index (NSCI) values for heavy metals varied from 1.20 to 4.11, with a mean of 2.55 (Table S5). The results indicate that 38% of the soil samples were slightly contaminated, 29% were moderately contaminated, and 33% were heavily contaminated. Overall, the soil exhibited moderate pollution with a mean NSCI value of 2.55 (Table S5). Contamination of the soil sites may be associated with emissions from road traffic, marble processing plants (MPPs), and car repair workshops, which release pollutants into the surrounding atmosphere25,145,146. The results of the current study are consistent with the findings of Yüksel et al.147, Weissmannova et al.143, Wang et al.144 and Chen et al.148 who reported varying levels of soil and sediment contamination linked to industrial activities, traffic emissions, and other anthropogenic sources. These patterns align with established knowledge about the impact of such activities on environmental pollution, particularly in areas with similar sources of contamination.

The index of geoaccumulation (Igeo) originally proposed by Muller88 provides valuable insights into the severity, range, and abundance of heavy metal contents in the soils of the studied sites149. The Igeo results indicated that cadmium (Cd) had the highest value of 1.274, suggesting significant pollution, followed by arsenic (As) with a value of 0.663, indicating substantial contamination. Copper (Cu) and cobalt (Co), with Igeo values of 0.09 and 0.062, respectively, indicate moderate pollution. Vanadium (V), chromium (Cr), and nickel (Ni) exhibited relatively lower Igeo values of 0.061, 0.060, and 0.054, indicating comparatively lower contamination levels. Manganese (Mn), titanium (Ti), and strontium (Sr) had even lower Igeo values of 0.035, 0.026, and 0.024, suggesting minimal pollution. Likewise, Zinc (Zn) and zirconium (Zr) exhibited the lowest Igeo values of 0.012 and 0.004, indicating very low levels of contamination. The Igeo values of As, Cu, Co, V, Cr, Ni, Mn, Ti, Sr, Zn, and Zr categorized the soil sites as slightly to moderately polluted. The Igeo values between 1 and 2 for Cd are indicative of moderate pollution at soil sites S11 and S14–S16, whereas Igeo values between 2 and 3 at soil sites S12–S13 and S18–S21 are indicative of moderate to strong pollution. The Igeo values of Cd between 0 and 1 in the soil samples (S1–S10, S17) indicate unpolluted to moderate pollution. Similarly, the Igeo values of As (1 ≤ Igeo ≤ 2) indicate a moderate level of pollution at soil sites S13 and S19–S21. On the other hand, Igeo values of As between 0 and 1 classify the soil sites (S1–S12 and S14–S18) as uncontaminated to moderately contaminated (Table S6). The pollution pattern indicates that Cd and As are the primary contaminants of concern, with their levels varying from unpolluted to highly contaminated, depending on the site. Other metals, such as Cu, Co, V, Cr, and Ni, contribute to moderate pollution, while Mn, Ti, Sr, Zn, and Zr pose minimal environmental risk. The variation in Igeo values across different sites highlights the spatial heterogeneity in soil contamination, suggesting that targeted remediation efforts may be needed in specific areas with relatively high pollution levels144.

Evaluation of the potential ecological risk of heavy metals

The potential ecological risk index provides an accurate assessment of heavy metal contamination in agricultural soil by considering metal toxicity relative to their reference concentrations in the Earth’s crust150. Table S7 presents the results of the ecological risk index (Eri) and the potential ecological risk index (PERI) assessments for heavy metals (HMs), showing substantial variation in the mean Eri values for individual heavy metals across the soil sites. The range of Eri values for Cd was (10–450) > As (2.5–65.5) > Cu (0.72–4.5) > Co (0.53–2.89) > Ni (0.92–1.99) > V (0.25–0.91) > Cr (0.29–0.89) > Mn (0.08–0.37) > Zn (0.01–0.2) (Table S7). The Eri values (< 15) for Cu, Co, Ni, V, Cr, Mn, and Zn at the soil sites indicated a low ecological risk associated with these metals. The Eri values for As, ranging from 15 to 30, indicate a moderate ecological risk at soil sites S2-S3, S6-S9, and S14-S18. Eri values between 30 and 60 signify considerable risk at sites S4-S5, S10-S13, and S19-S20. Site (S21) poses a high ecological risk for As (60 ≤ Eri < 120), whereas site (S1) is associated with a low ecological risk (Eri < 15). Furthermore, considerable ecological risk of Cd was characterized for S6 and S8-S10, while a high ecological risk was characterized for S4–S5, S7, and S17. Very high ecological risk was identified at sites S11–S16 and S18–S21, whereas sites S1–S3 exhibited lower ecological risk for Cd (Table S7). The current findings suggest that cadmium is the most concerning heavy metal, with several sites facing very high ecological risks, followed by arsenic, which presents moderate to high risks at various sites. Other metals such as Cu, Co, Ni, V, Cr, Mn, and Zn, generally pose low ecological risks across the studied areas. These results are strongly supported by the studies of Din et al.149, Chen et al.151 and Ajah et al.152, who also highlighted the significant ecological risks associated with cadmium and arsenic, while noting lower risks from other metals.

The PERI values reflect how different biological communities react to toxic substances, highlighting the potential ecological risks associated with hazardous elements153. The potential ecological risk values in the analyzed soil samples ranged from 18.52 to 511.35, with a mean value of 217.49 (Table S7). Almost 42.85% of the soil sites were at very high ecological risk, while 23.80% were at considerable ecological risk. In addition, approximately 14.28% of the sites were associated with moderate ecological risk, and 14.28% were classified as low ecological risk sites. The main contributor to the PERI appeared to be Cd (83.8%), followed by As (14.4%) (Fig. 2). In contrast, the cumulative contribution of Co, Mn, V, Cr, Zn, Cu and Ni to the PERI was 3.0%. The contribution rate of heavy metals to the PERI correlates not only with their concentration but also with their toxicity response factors138. In the current study, the pattern observed highlights Cd and As as the dominant hazardous elements in the soil, which pose the greatest ecological threats owing to their high toxicity and significant presence154. This finding is consistent with the results of Lu et al.155, Long et al.156 and Kumar et al.157, who also identified Cd and As as major contributors to potential ecological risks, especially in soils and sediments affected by human activities like mining. This study underscores the need for increased attention to Cd and As levels in agricultural soils because of their substantial ecological impact. The results also align with broader research, such as studies on sediment contamination in large freshwater lakes in China, where Cd was found to pose a moderate national-level risk due to significant emissions from human activities158.Fig. 2 Contribution percentage (%) of heavy metals to potential ecological risk.

Assessment of potential health risk

A major global concern is human exposure to heavy metals due to environmental pollution. Heavy metals such as As, Cd, Cr, Pb, and Ni are highly toxic, classified as priority metals of significant public health concern159. Even at minimal exposure levels, they cause damage to multiple organs and are classified as human carcinogens160. Therefore, this study presents a health risk assessment of heavy metals for individuals working in marble processing plants (MPPs) and in surrounding agricultural soils, addressing both potential non-carcinogenic and carcinogenic health risks.

Noncarcinogenic health risk

Table 2 presents the average daily dose (ADD, mg/kg/day), hazard quotient (HQ), and hazard index (HI) for non-carcinogenic risks of heavy metals (HMs) through ingestion, inhalation, and dermal contact for both children and adults. A comparatively greater trend in ADD values (mg/kg/day) was observed for children than for adults for both the ingestion and inhalation routes (Table 2). The ADDingestion values (mg/kg/day) for the children decreased in the order of Al (1.40E-01) > Fe (6.01E-02) > Mn (1.91E-03) > V (5.01E-04) > Cr (3.44E-04) > Cu (2.57E-04) > Ni (2.35E-04) > Co (7.49E-05) > Zn (7.37E-05) > As (6.76E-05) > Zr (3.59E-05) > Cd (2.44E-05). Compared to children, adults exhibited similar patterns of average daily dose (ADD) for ingestion and inhalation of the studied heavy metals, but with lower intake levels. Conversely, higher values of ADDdermal were obtained for adults than for children in the pattern of Al > Fe > As > Mn > V > Cr > Cu > Ni > Zn > Zr > Co > Cd (Table 2). According to this study, children appear to be more susceptible to health risks than adults are through the ingestion and inhalation routes. On the other hand, adults are more susceptible to such risks through the dermal route. Table 2 Mean values of average daily dose (ADD), hazard quotient (HQ), total hazard quotient (THQ), and cumulative hazard index (HI) of non-carcinogenic risks for adults and children.

Elements	ADD ingestion	ADD inhalation	ADD dermal		HQ ingestion	HQ inhalation	HQ dermal	HQ total	
Adults	
Al	1.50E-02	2.20E-06	1.70E-04		1.50E-02	1.83E-01	1.67E-02	2.15E-01	
As	7.24E-06	1.06E-09	2.48E-06		2.41E-02	3.55E-06	5.26E-03	2.94E-02	
Cd	2.61E-06	3.84E-10	2.97E-08		2.61E-02	3.84E-06	1.04E-03	2.71E-02	
Co	8.02E-06	1.18E-09	3.66E-08		4.01E-04	2.07E-04	2.26E-03	2.87E-03	
Cr	3.68E-05	5.42E-09	4.20E-07		1.23E-02	1.89E-04	4.77E-02	6.02E-02	
Cu	2.75E-05	4.05E-09	3.14E-07		7.42E-04	1.01E-07	1.07E-02	1.15E-02	
Fe	6.44E-03	9.47E-07	7.34E-05		9.20E-03	4.30E-03	1.21E-02	2.56E-02	
Mn	2.05E-04	3.01E-08	2.33E-06		1.46E-03	2.10E-03	2.92E-03	6.48E-03	
Ni	2.52E-05	3.71E-09	5.75E-08		1.26E-03	1.80E-07	1.47E-03	2.73E-03	
V	5.37E-05	7.90E-09	6.12E-07		7.67E-03	1.13E-06	5.26E-03	1.29E-02	
Zn	7.89E-06	1.16E-09	5.40E-08		2.63E-05	3.87E-09	6.06E-02	6.07E-02	
Zr	3.85E-06	5.66E-10	4.39E-08		4.81E-02	7.07E-06	2.53E-04	4.84E-02	
Total	2.18E-02	3.20E-06	2.50E-04	HI	1.46E-01	1.90E-01	1.66E-01	5.03E-01	
Children	
Al	1.40E-01	1.03E-05	1.37E-04		1.40E-01	8.55E-01	1.37E-03	9.96E-01	
As	6.76E-05	4.97E-09	1.99E-06		2.25E-01	1.66E-05	1.62E-02	2.41E-01	
Cd	2.44E-05	1.79E-09	2.39E-08		2.44E-01	1.79E-05	2.39E-03	2.46E-01	
Co	7.49E-05	5.51E-09	2.94E-08		3.74E-03	9.64E-04	1.83E-06	4.71E-03	
Cr	3.44E-04	2.53E-08	3.37E-07		1.15E-01	8.84E-04	5.61E-02	1.72E-01	
Cu	2.57E-04	1.89E-08	2.52E-07		6.93E-03	4.70E-07	2.10E-05	6.95E-03	
Fe	6.01E-02	4.42E-06	5.89E-05		8.58E-02	2.01E-02	8.41E-04	1.07E-01	
Mn	1.91E-03	1.40E-07	1.87E-06		1.36E-02	9.81E-03	1.04E-03	2.45E-02	
Ni	2.35E-04	1.73E-08	4.61E-08		1.18E-02	8.40E-07	8.54E-06	1.18E-02	
V	5.01E-04	3.69E-08	4.91E-07		7.16E-02	5.27E-06	7.02E-03	7.86E-02	
Zn	7.37E-05	5.42E-09	4.33E-08		2.46E-04	1.81E-08	7.22E-07	2.46E-04	
Zr	3.59E-05	2.64E-09	3.52E-08		4.49E-01	3.30E-05	4.40E-04	4.49E-01	
Total	2.03E-01	1.49E-05	2.01E-04	HI	1.37E + 00	8.87E-01	8.54E-02	2.34E + 00	

The hazard quotient (HQ) values of Al, As, Cd, Cr, Co, Ni, Cu, V, Fe, Mn, Zr, and Zn for adults and children were < 1.0 (Table 2). Therefore, no apparent health risk from exposure to a single HM is indicated. However, in children, the cumulative HI value from ingestion, inhalation and dermal exposure to heavy metals was > 1.0 (Table 2), indicating that prolonged exposure of children to soils near the marble industry is harmful to their health25. In addition, the probability distributions of total hazard index (HItotal) from ingestion, inhalation, and dermal exposure to heavy metals for adults (Fig. 3a) and children (Fig. 3b) were assessed via a probabilistic approach with Monte Carlo simulation. HI total scores were found to be less than 1.0 at the mean, 5th, and 95th percentiles for both children and adults, indicating no apparent risk for the target population. The deterministic approach estimated higher HItotal scores for both children and adults (Table 2) compared to the probabilistic approach shown in Fig. 3a,b. Similar results were also recorded by Sanaei et al.114, where the HI values calculated through the deterministic approach were higher than those from probabilistic values. This disparity can be attributed to the overestimation of risk caused by the inherent uncertainties of deterministic analysis115.Fig. 3 Probability distribution of total hazard index (HI) for soil heavy metals presented in Tabe 2 for (a) adults and (b) children from ingestion, inhalation and dermal contact route.

Ingestion, inhalation, dermal and total cancer risk (TCR)

Cancer risk represents the probability of developing cancer from prolonged exposure to carcinogens161. The potential cancer risk from ingestion, inhalation, and dermal exposure to soil heavy metals (As, Cd, Co, Cr, and Ni) for adults and children is shown in Table 3. Commonly referenced acceptable risk levels range from 1.0E-06 to 1.0E-04 (i.e., one in 1,000,000 to one in 10,000)162. This study follows the USEPA162 guidelines, which define carcinogenic risk levels at 1.0E-04 and 1.0E-06, and considers these levels as potential indicators of adverse health risks for the exposed population. Through deterministic approach, the total carcinogenic risk (TCR) from soil heavy metal exposure ranged from 5.6E-10 to 6.9E-04 for children and from 1.2E-10 to 7.4E-05 for adults (Table 3). The carcinogenic risk from Cr, Ni, Cd and As ingestion was slightly higher in children than in adults and exceeded the USEPA limits of 1.0E-06 and 1.0E-04. The carcinogenic risk from inhalation and dermal exposure to these elements in children and adults was below the USEPA limits (Table 3). However, the TCR for Cr, Ni, Cd, and As was consistently higher in children than in adults across all exposure pathways (Table 3). Furthermore, the contribution of each element to the TCR decreased in the order of Cr > Ni > Cd > As, suggesting greater potential for the carcinogenic risk of Cr. The TCR for Co fell within the safety limits of 1.0E-04, indicating no apparent carcinogenic risk. Table 3 shows that Cr and Ni contribute significantly to the carcinogenic risk in children, and consequently, children are at higher risk than adults. Table 3 Estimated carcinogenic risks through ingestion, inhalation, and dermal pathways for children and adults.

	Adult			Children			
Carcinogenic	CRingestion	CRinhalation	CRdermal	TCR	CRingestion	CRinhalation	CRdermal	TCR	
As	4.8E-06	2.5E-07	6.8E-07	5.8E-06	4.5E-05	1.2E-07	5.4E-07	4.7E-05	
Cd	6.9E-06	6.1E-11	–	6.9E-06	6.4E-05	2.8E-10	–	6.4E-05	
Co	–	1.2E-10	–	1.2E-10	–	5.6E-10	–	5.6E-10	
Cr	7.4E-05	1.3E-10	3.5E-08	7.4E-05	6.9E-04	6.0E-10	2.8E-08	6.9E-04	
Ni	1.7E-05	4.4E-09	6.8E-08	1.7E-05	1.6E-04	2.1E-08	5.5E-08	1.6E-04	
Total	1.0E-04	2.5E-07	7.8E-07	1.0E-04	9.5E-04	1.2E-06	6.3E-07	9.6E-04	

In addition to point estimation of carcinogenic risk, a probabilistic approach was applied to assess the carcinogenic risk in the exposed groups. This approach considers the proper distribution of parameters, including the metal concentration, ingestion rate (IR), exposure duration (ED), exposure frequency (EF), and body weight (BW) (Table S3). The simulated carcinogenic risk results for As, Cr, Cd, Ni, and Co from ingestion, inhalation, and dermal exposure for adults and children are shown in Figs. 4, 5, 6, 7, 8, 9, 10 and 11. In adults, the probabilistic cancer risk values from ingestion for As (Fig. 4a), Cr (Fig. 4b), and Ni (Fig. 4c) at the 5th, mean and 95th percentiles exceeded the minimum acceptable limit of 1.0E-06. The Cd level at the 95th percentile from the ingestion route also exceeded the minimum threshold of 1.0E-06 (Fig. 4d). The probabilistic cancer risk from dermal exposure to As (Fig. 5a) and Cr (Fig. 5b) in adults was slightly higher than from ingestion at the mean, 5th, and 95th percentiles. In adults, the potential cancer risk for As (Fig. 6a), Cd (Fig. 6b), Cr (Fig. 6c), Co (Fig. 6d), and Ni (Fig. 6e) via inhalation route was below the USEPA limits of 1.0E-04 and 1.0E-06. However, the total cancer risk for adults (Fig. 7d) from ingestion (Fig. 7a), inhalation (Fig. 7b), and dermal contact (Fig. 7c) route at the 95th percentile, exceeded the upper limit of 1.0E-04. This finding indicates a potential cancer risk for the adult group.Fig. 4 Shows the probability of cancer risk for (a) As (b) Cr (c) Ni (d) Cd in adults via ingestion route.

Fig. 5 Probability of cancer risk for (a) As (b) Cr and (c) Ni in adults through dermal route.

Fig. 6 Probability distribution of cancer risk for (a) As (b) Cr (c) Co (d) Cd and (e) Ni in adults through inhalation route.

Fig. 7 Cumulative probability of cancer risk for adults from (a) ingestion, (b) inhalation (c) dermal and (d) total cancer risk from the three pathways.

Fig. 8 Cumulative probability of cancer riskk for (a) As (b) Cr (c) Cd (d) Ni in children through ingestion route.

Fig. 9 Probability distribution of cancer risk for (a) As (b) Cr (c) Cd (d) Co (e) Ni in children through inhalation route.

Fig. 10 Cumulative probability of cancer risk for (a) As (b) Cr (c) Ni in chidren through dermal route.

Fig. 11 Cumulative probability of cancer risk for children from (a) ingestion (b) inhalation (c) dermal route and (d) total cancer risk from the three exposure pathways.

For children, at the 95th percentile, the probabilities of cancer risk resulting from soil ingestion exposure to As (Fig. 8a), Cr (Fig. 8b) Cd (Fig. 8c), and Ni (Fig. 8d) were 1.83E-04, 2.52E-04, 1.99E-05, and 5.27E-04, respectively. These values exceeded the safety limit of 1.0E-04 and were relatively higher than the corresponding risks for the target group. Similarly, the probability of cancer risk due to inhalation exposure to soil Cr at the mean and 95th percentiles (Fig. 9b) was greater in children than in adults and exceeded the threshold of 1.0E-04. In addition, the probability of cancer risk in children due to inhalation exposure to soil As (Fig. 9a), Cd (Fig. 9c), Co (Fig. 9d) and Ni (Fig. 9e) at the mean and 95th percentiles was within the permissible limits of USEPA. In children, the potential cancer risks of As (Fig. 10a) and Cr (Fig. 10b) through the dermal pathway were lower than in adults but exceeded the lower safety limits of 1.0E-06. For Ni, the dermal exposure risk both in children (Fig. 10c) and adults (Fig. 5c) was within the permissible limits. In addition, the total cancer risk in children (Fig. 11d) from the combined pathways of ingestion (Fig. 11a), inhalation (Fig. 11b) and dermal contact (Fig. 11c) at the mean, 5th, and 95th percentiles were 6.95E-04, 5.49E-04, and 8.62E-04, respectively, and were above the upper safety limit of 1.0E-04, suggesting a greater carcinogenic hazard for children than adults. The slightly elevated risk in children can be attributed to their higher intake rates relative to body weight, increased absorption efficiency, and developmental susceptibility163. The exceedance of USEPA thresholds suggests that the levels of these metals pose a significant carcinogenic threat, necessitating urgent measures to mitigate exposure, particularly in vulnerable populations such as children164. Wu et al.153 also reported a high carcinogenic risk for children in the soil around electronics manufacturing sites.

In a previous study, the carcinogenic risk higher than 1.0E-04 through soil ingestion for adults and children was shown to predict health hazards for the target group165. In the current study, the slightly higher risk value in adults through the dermal pathway aligns with findings from a similar study conducted in the mining areas of central China165. Similarly, the cancer risk via the inhalation route was negligible for adults (Fig. 7b) and unacceptable at the 95th percentile for children (Fig. 11b). Cao et al.166 reported that consuming food grown on contaminated soil and direct skin contact with soil are significant human exposure pathways. Moreover, industrial and agricultural activities have led to the significant discharge of heavy metals into the surrounding soil. In accordance with Wang et al.168 and Yang et al.65, the use of pesticides and fertilizers significantly contributes to the occurrence of As and Cd in soil. Moreover, polluted water, smoke, and metal contaminated dust from smelting and metal processing can migrate into agricultural soils and increase the risk of cancer development65.

In the current study, the carcinogenic risks from As, Cr, Cd, and Ni are probably linked to the combustion of fossil fuel, inorganic fertilizers, battery workshops, and the emission of marble dust17,22,25,167. In addition, excessive accumulation of Cr in human tissues triggers gastric and lung cancer168. Similarly, exposure to nickel can produce a variety of human health side effects, such as pulmonary fibrosis, nasal and lung cancer, and kidney and cardiovascular diseases167. Similarly, soil arsenic exposure significantly associated with mortality rates from colon, stomach, kidney, lung and nasopharyngeal cancers169. According to Suwatvitayakorn et al.170, Cd has the potential to accumulate in the human body, resulting in acute and chronic effects on various organs and systems, with a particular impact on the kidneys and skeletal system. Therefore, it is equally important to carefully treat and monitor extremely noxious pollutants such as Cd, Cr, As, and Ni, even at very low concentrations in soil, air, and water.

Despite the carcinogenic or noncarcinogenic risk, children are more vulnerable to the potential health risk attributed to the existence of HMs in the soils surrounding MPPs. This suggests that children are more susceptible to adverse health effects from exposure to HMs, as they are more likely to ingest HMs orally from hand to mouth171. Among the different routes of exposure, the ingestion route contributes the most to the overall cancer risk in both children and adults, followed by the dermal route, and the inhalation route contributes very little to the overall cancer risk. Similar results were also reported by Saha et al.172, Kusin et al.171, Cai et al.173 and Hao et al.165.These findings strongly support our current results. Our study underscores the significant health risks from heavy metal exposure in agricultural soils near industries, emphasizing the need for focused risk control measures, as also suggested by Butt et al.48, Khan et al.25, Muhammad et al.174 and Noreen et al.128 in their respective studies.

Sensitivity analysis

A sensitivity analysis was performed to determine the importance of the variables involved in calculating noncarcinogenic and carcinogenic risk175. The total hazard index (HI) < 1 at the mean, 5th, and 95th percentiles for both children (Fig. 3b) and adults (Fig. 3a) suggests that there is no apparent noncancerous risk for these two groups. However, sensitivity analysis of the total HI in adults indicated that heavy metal concentration (60.4%), exposure duration (30.4%) and exposure frequency (8.4%) were the most predictive noncancer risk factors (Fig. 12b).These results are in agreement with those of the study conducted by Fallahzadeh et al.175. Similarly, for children, exposure duration (67.3%), heavy metal concentration (26.5%) and exposure frequency (6.4%) were important factors for noncancer risk (Fig. 12a). Sanaei et al.114 reported that the ingestion rate is the main factor influencing health risk, followed by exposure duration and metal concentration, while body weight is a less influential variable, partially supporting our study results. In addition, the assessment of the contributions of HMs to adult cancer risk through ingestion, inhalation, and dermal contact (Fig. 13b) indicated that As had the most significant impact, accounting for 83.6% of the total risk, followed by Cr (12.4%) and Ni (9%). For children, greatest contribution to the total cancer risk was simulated for Ni (59.5%), followed by Cr (21.6%) and As (18.3%) from the ingestion, inhalation and dermal exposure routes (Fig. 13a). The findings of this study align with those of Chen et al.169, Yang et al.65 and Mallongi et al.66, who identified As, Cd, Cr, and Ni as the main contributors to total risk variance.Fig. 12 Sensitivity analysis of HI total for (a) chidren (b) adults.

Fig. 13 Sensitivity analysis of total cancer risk for (a) chidren (b) adults.

Looking for associations and heavy metal sources

A normalized variation matrix used to show the linear association for the chemical components42. The analysis of the normalized variation matrix (Table 4) for the chemical parts (Table 1) reveals key associations between soil pH, EC, TDS, and the studied elements in agricultural soils near marble industries. These associations provide insights into the geochemical behavior of these elements and their interactions within the soil matrix176. According to Boente et al.42 variations less than 0.2 suggest a proportional or linear relationship, while variations greater than 1.0 indicate a lack of proportionality or linear connection. In the current study, strong linear relationships were observed between soil pH and elements such as Ca (0.04), Ni (0.04), Mg (0.07), Cr (0.10), Al (0.12), V (0.10), Sr (0.12), K (0.13), As (0.14) and Mn (0.18). These values suggest a proportional or linear relationship, indicating that changes in soil pH significantly affect the solubility and availability of these elements177. Therefore, soil pH plays a crucial role in determining the solubility and mobility of elements178. The very strong relationship of calcium with pH indicates that calcium carbonate plays a crucial role in buffering the pH of the soil and stabilizing pH levels179. However, acidic conditions often increase the solubility of these elements which can lead to higher concentrations in water and soil180. EC is an important indicator of soil salinity and nutrient availability181. The strong linear relationships of EC with Cr (0.07), Mg (0.09), V (0.09), Ni (0.11), Ca (0.12), Sr (0.14), K (0.15), As (0.15) and Al (0.17) (Table 4) suggest the impacts of nutrient availability and soil chemical processes182. The relationships of EC with Ca, K, Mg, and Sr contribute significantly to the ionic balance, reflecting their role in soil structure and maintaining soil health and fertility182,183. The presence of these elements in a balanced proportion ensures that the soil remains fertile and capable of supporting healthy plant growth184. However, an imbalance, often indicated by a high EC, could suggest salinity issues, which can hinder plant growth and lead to soil degradation over time185. The TDS is a measure of the total concentration of dissolved substances in soil water, which directly impacts soil water quality and its suitability for plant growth186. The linear proportions of TDS with Ni (0.15) and V (0.16) highlight how increases in TDS can affect soil and water quality187. Elevated TDS levels can indicate the presence of excessive salts or other dissolved substances, which may reduce the availability of water to plants, potentially leading to water stress and reduced plant growth188,189. The associations between pH, EC, TDS, and the studied elements provide valuable insights into the potential sources of these elements and their environmental implications12,26. For example, the strong relationship between pH and Ca suggests that calcium carbonate from marble industries could be a significant source, contributing to the observed soil chemistry23. The relationships involving EC and TDS with elements such as Ni, Cr, and V may indicate industrial or agricultural inputs, such as fertilizers or industrial emissions, as potential sources190. Similarly, values below 0.2 for Al versus K (0.07), Mn (0.12), Ni (0.17) and Cr (0.18) (Table 4) indicate that these element pairs are considered proportional and can be easily explained by the fact that where Al is present, it is likely to be accompanied by relatively high concentrations of K, Mn, Ni and Cr. Furthermore, the smaller contribution values for pairs such as Cr to V (0.09), Sr (0.14), Ni (0.16), and K (0.17), along with Sr to V (0.12), Ca to Ni (0.17), and K to Sr (0.18), underscore the existence of a linear binary relationship among these elements. This suggests that the concentration of one element in a pair can strongly influence the concentration of the other. For example, the linear relationship between Cr and V or Sr implies that fluctuations in chromium levels could directly impact vanadium or strontium concentrations. Similarly, the associations of Ca with Ni and K with Sr suggest a strong geochemical linkage between these pairs, likely due to co-deposition or similar environmental factors that influence their distribution. The observed associations may also indicate shared sources or pathways of these elements, which is consistent with the patterns identified in other studies by Boente et al.42, Khan et al.23; and Khorshidi et al.191. Table 4 Normalized variation matrix for the chemical elements of Table 1: Bold values show linear association.

	pH	EC	TDS	Al	As	Ca	Cd	Co	Cr	Cu	Fe	K	Mg	Mn	Ni	P	Si	Sr	Ti	V	Zn	Zr	
pH	0																						
EC	0.06	0.00																					
TDS	0.31	0.41	0																				
Al	0.12	0.17	0.27	0																			
As	0.14	0.15	0.25	0.89	0																		
Ca	0.04	0.12	0.24	0.45	0.33	0																	
Cd	2.19	2.17	1.65	1.8	0.89	1.29	0																
Co	4.21	4.41	3.03	0.28	0.82	0.45	1.29	0															
Cr	0.10	0.07	0.22	0.18	0.43	0.22	1.54	0.35	0														
Cu	0.29	0.23	0.36	0.66	0.72	0.51	2.22	0.72	0.3	0													
Fe	0.27	0.32	0.22	0.54	1.31	0.83	1.56	0.86	0.79	1.58	0												
K	0.13	0.15	0.51	0.07	0.89	0.46	2.12	0.43	0.17	0.52	0.79	0											
Mg	0.07	0.09	0.25	0.36	0.59	0.24	1.3	0.31	0.32	0.48	0.69	0.5	0										
Mn	0.18	0.28	0.21	0.12	1.06	0.58	1.72	0.47	0.39	0.98	0.25	0.28	0.38	0									
Ni	0.04	0.11	0.15	0.17	0.51	0.17	1.3	0.2	0.16	0.45	0.55	0.25	0.08	0.24	0								
P	0.49	0.38	0.92	0.65	1.51	1.33	2.53	1.19	0.83	1.23	0.92	0.58	1.1	0.68	0.87	0							
Si	0.59	0.95	0.85	1.02	1.77	1.16	1.96	0.82	1.41	2.09	1.12	1.14	1.11	0.93	0.93	1.64	0						
Sr	0.12	0.14	0.32	0.23	0.61	0.23	1.69	0.26	0.14	0.38	1.09	0.18	0.34	0.55	0.2	1.06	1.17	0					
Ti	0.33	0.38	0.56	0.42	1.57	1.07	1.93	0.8	0.74	1.54	0.25	0.68	0.89	0.33	0.68	0.93	1.59	1.01	0				
V	0.10	0.09	0.16	0.3	0.45	0.27	1.47	0.28	0.09	0.42	1.07	0.34	0.39	0.63	0.25	1.17	1.57	0.12	0.9	0			
Zn	1.04	0.97	1.09	3.11	1.85	2.31	1.48	3.19	2.82	3.83	2.27	3.17	2.74	2.7	2.51	2.93	2.6	3.10	3.33	3.11	0		
Zr	0.27	0.26	0.24	0.88	0.32	0.44	0.85	0.93	0.61	1.2	0.94	1.0	0.66	0.87	0.52	1.61	1.75	0.83	1.22	0.66	1.44	0	

In addition, appropriate measures to find sources of HM contamination in agricultural soils are of paramount importance for developing sustainable management approaches and protecting soil quality192. Therefore, a clr-biplots (Fig. 14a, b) based on a centered logarithmic transformation (clr) was constructed to interpret the inter-elemental associations and source identification of HMs in agricultural soils42,193. Figure 14a explains 62% of the variation between PC1 and PC2 and Fig. 14b explains 53% of the variation between PC1 and PC3 of the studied elements, indicating multiple element relationships related to natural geology and anthropogenic activities194. Additionally, strong element associations between Mn-P-Ti, Al, and K are explained by the vicinity of their vertices and their rays pointing in a similar direction, indicating a combination of geogenic and anthropogenic origins195. The vertices of Mn, P, and Ti are superimposed, and Ti was found to have the highest vector length, which could be associated with the co-precipitation of TiO2, MnO, and P2O5 found in marble powder and fertilizers25. Similarly, Cu, V, Sr, Cr, Mg, Ni, and Co form another association due to the close proximity of their vertices and the alignment of their rays in the same direction, as illustrated in Fig. 14a,b. This association is likely linked to vehicle emissions and industrial activities194. A close relationship between Zn, As, Cd, and Zr forms a separate group and could be associated with smelter activities and the regular use of inorganic fertilizers196,197. Fe and Si form another association because their rays also point in the same direction. However, in Fig. 15, Fe, Mn, Ti, and Ca shifted their positions and entered the elemental associations of Zn–Cd–As–Zr–Fe, Cu–Ti–Cr, Al–K–Mn, and Mg–Ca–Sr–Ni, indicating their affinity for these elements and sharing both natural and anthropogenic sources.Fig. 14 clr-biplot (a) PC1 vs PC2 (b) PC1 vs PC3 of the elements in study area.

Fig. 15 CoDa-dendrogram shows association of the data set of the geochemical composition of the soil generated by the clustering algorithm (Wards method).

Factor analysis (Varimax rotation) of the centered log-ratio transform (clr) dataset was applied to discriminate potential geochemical source patterns and the associations and interrelationships between variables in the topsoil191. The three-factor model (Eigen > 1) explained 70.32% of the variance overall, with the contributions of each factor being F1 (30.76%), F2 (27.11%), and F3 (12.45%) (Table 5). A variable with factor loadings greater than 0.5 was included to explain the main components of each factor. The three-factor model captured a good correlation between the variables and their potential sources, showing a communality greater than 0.6 (60% variability). Factor one (F1) was dominated by Al, Mn, K, and Ti with strong positive loadings of 0.89, 0.80, 0.75, and 0.68, respectively, which could be attributed mainly to road transport and industrial activities198. For factor two (F2), the Cr, V, Cu, and Sr loadings were 0.92, 0.84, 0.80, and 0.78, respectively, indicating that the marble industry was the source of origin25. Factor three (F3) showed strong positive loadings, mainly with Ni (0.81), Mg (0.77), and Co (0.71), suggesting that river water could be another important source of pollution145. Table 5 Factor analysis (Varimax rotation) of the centered log ratio (Clr) transformed data set.

Variables	Factors		
	F1	F2	F3	Communality	
Al	0.89	0.27	0.15	0.908	
As	 − 0.81	0.37	 − 0.24	0.860	
Ca	 − 0.52	0.40	0.33	0.611	
Cd	 − 0.70	 − 0.41	 − 0.13	0.844	
Co	0.17	0.10	0.71	0.918	
Cr	0.19	0.92	 − 0.07	0.916	
Cu	 − 0.01	0.80	0.27	0.821	
Fe	0.43	 − 0.71	 − 0.18	0.891	
K	0.75	0.54	 − 0.01	0.925	
Mg	 − 0.13	0.04	0.77	0.870	
Mn	0.80	 − 0.37	0.06	0.861	
Ni	0.12	0.20	0.81	0.879	
P	0.61	0.00	 − 0.44	0.738	
Si	0.14	 − 0.55	0.38	0.902	
Sr	0.12	0.78	0.38	0.880	
Ti	0.68	 − 0.31	 − 0.19	0.930	
V	 − 0.06	0.84	0.15	0.945	
Zn	 − 0.57	 − 0.51	 − 0.52	0.933	
Zr	 − 0.70	 − 0.08	 − 0.33	0.726	
Eigen value	5.84	5.15	2.37	–	
% of total explained Variance	30.76	27.11	12.45	–	
% of cumulative Variance	30.76	57.87	70.32	–	

Cluster analysis (CA) is commonly used with PCA to verify the results of associations and HMs sources193. For this purpose, the coda dendrogram (Fig. 15) is chosen as a visual method to identify possible relationships, pairwise or involving larger groups199. The short vertical bars in the coda dendrogram correspond to linear associations between the clustering variables. Therefore, three groups of elements can be identified: (i) Al, K, Mn, P, Mn, Si, Fe, and Ti, which are not highly toxic and are of geogenic origin, and (ii) Cr, Ni, V, Cu, Co, Mg, Ca, and Sr, which become toxic in excess quantity, indicating anthropogenic and lithogenic sources200. The third group consists of As, Cd, Zn, and Zr, which are potentially toxic and derived from the extensive application of chemical fertilizers197. According to Weissmannová et al.143, different elements grouped in the same cluster originate from a common source. The results of the coda dendrogram (Fig. 15) fully agreed with the results of the clr-biplots (Fig. 14a,b), variation matrix analysis (Table 4), and factor analysis (Table 5). These observations reinforce that industrial effluents, intensive fertilizer use, vehicle emissions, and irrigation with contaminated water are key sources of toxic elements, verifying findings from Jehan et al.145, Khan et al.25, and Sarwar et al.201.Therefore, these findings strongly support our current study results (Fig. 16).Fig. 16 Pollution sources of heavy metals and potential health risk assessment.

Conclusion

In the current study, the concentration of macro elements and heavy metals in soil near marble processing plants (MPPs) in district Malakand, was assessed to determine the extent of contamination and potential health risks. The concentrations of macro elements and heavy metals in the soil decreased in the following order: Al > Mg > Fe > K > Ti > P > Mn > V > Sr > Cr > Cu > Ni > Si > Co > Zn > As > Zr > Cd. The maximum concentrations of Ca, P and Cd exceeded the acceptable limits of the FAO and the world average shale. The Pollution Index (Pi) and Nemerow Synthetic Contamination Index (NSCI), indicated different levels of contamination from slight to heavy contamination. The Geoaccumulation Index (Igeo) indicated significant contamination of the soil sites by Cd and As, moderate contamination by Cu, Co, V, Cr, and Ni, and minimal contribution from Mn, Ti, Sr, Zn, and Zr. The potential ecological risk assessments revealed that Cd poses the highest ecological risk, followed by As. The pollution indices and compositional exploratory data analysis revealed that the spatial variability of soil contamination is primarily influenced by factors including industrial activities, ro ad traffic, and marble processing plants. The health risk assessments indicated elevated non-carcinogenic risks for children, with the total cancer risk fromthe potential toxic metals above safe levels, particularly for children. The key factors influencing health risks included metal concentration, exposure duration, and frequency, with As and Cr being significant contributors to cancer risk. The long-term monitoring of heavy metal contamination and its health impacts should be implemented, enforcing regulations on emissions from industrial and vehicular sources should be enforced, and soil remediation techniques such as phytoremediation and biochar application should be employed to reduce contamination levels. Additionally, public health programs should be developed to limit children’s exposure to contaminated soils, and alternative agricultural practices and land-use policies should be explored to minimize exposure and risks.

Supplementary Information

Supplementary Information.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-024-72346-7.

Acknowledgements

The authors are thankful and acknowledge the extensive help and support provided by the Center of Analytical Facility Division (CAFD) of the Pakistan Institute of Nuclear Sciences and Technology (PINSTCH) Islamabad during the laboratory work.

Author contributions

Asghar Khan: Conceptualization, investigation, methodology, data curation, formal analysis, writing—original draft, software. Muhammad Saleem Khan: Conceptualization, Project administration, Resources, Supervision, Validation, Writing—review & editing. Qaisar Khan: Data curation, Writing—review & editing. Kishwar Ali: Formal analysis, Writing—review & editing. Fazal Hadi: Formal analysis, Validation, Writing—review & editing. Ghulam Saddiq: Validation, resources, Writing—review & editing.

Funding

No grant for the current research was awarded by any organization or research institute.

Data Availability

All the data are included in the article.

Ethics approval and consent to participate

We, the authors of this manuscript, hereby declare that the content presented in this research article is original and has not been published elsewhere. All contributions made by coauthors are appropriately acknowledged.

Consent for publication

We declare that this manuscript is original, has not been published before and is not currently being considered for publication elsewhere.

Competing interests

The authors declare no competing interests.

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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References

1. Wu J Lu J Li L Min X Luo Y Pollution, ecological-health risks, and sources of heavy metals in soil of the northeastern Qinghai-Tibet Plateau Chemosphere 2018 201 234 242 10.1016/j.chemosphere.2018.02.122 29524824
Wu, J., Lu, J., Li, L., Min, X. & Luo, Y. Pollution, ecological-health risks, and sources of heavy metals in soil of the northeastern Qinghai-Tibet Plateau. Chemosphere 201, 234–242 (2018).29524824 10.1016/j.chemosphere.2018.02.122
2. Abrahams PW Soils: Their implications to human health Sci. Total Environ. 2002 291 1 32 10.1016/S0048-9697(01)01102-0 12150429
Abrahams, P. W. Soils: Their implications to human health. Sci. Total Environ. 291, 1–32 (2002).12150429 10.1016/S0048-9697(01)01102-0
3. Yüksel B Appraisal of metallic accumulation in the surface sediment of a fish breeding dam in Türkiye: a stochastical approach to ecotoxicological risk assessment Mar. Pollut. Bull. 2024 203 116488 10.1016/j.marpolbul.2024.116488 38759467
Yüksel, B. et al. Appraisal of metallic accumulation in the surface sediment of a fish breeding dam in Türkiye: a stochastical approach to ecotoxicological risk assessment. Mar. Pollut. Bull. 203, 116488 (2024).38759467 10.1016/j.marpolbul.2024.116488
4. Fazekašová, D. & Fazekaš, J. Soil quality and heavy metal pollution assessment of iron ore mines in Nizna Slana (Slovakia). Sustain. 12, (2020).
5. Hussain CM Keçili R Environmental pollution and environmental analysis Mod. Environ. Anal. Tech. Pollutants 2020 10.1016/b978-0-12-816934-6.00001-1
Hussain, C. M. & Keçili, R. Environmental pollution and environmental analysis. Mod. Environ. Anal. Tech. Pollutants10.1016/b978-0-12-816934-6.00001-1 (2020).10.1016/b978-0-12-816934-6.00001-1
6. Topaldemir H Taş B Yüksel B Ustaoğlu F Potentially hazardous elements in sediments and Ceratophyllum demersum: An ecotoxicological risk assessment in Miliç Wetland, Samsun Türkiye Environ. Sci. Pollut. Res. 2023 30 26397 26416 10.1007/s11356-022-23937-2
Topaldemir, H., Taş, B., Yüksel, B. & Ustaoğlu, F. Potentially hazardous elements in sediments and Ceratophyllum demersum: An ecotoxicological risk assessment in Miliç Wetland, Samsun Türkiye. Environ. Sci. Pollut. Res. 30, 26397–26416 (2023).10.1007/s11356-022-23937-2
7. Aradpour S Metal contamination assessment in water column and surface sediments of a warm monomictic man-made lake: Sabalan Dam Reservoir, Iran Hydrol. Res. 2020 51 799 814 10.2166/nh.2020.160
Aradpour, S. et al. Metal contamination assessment in water column and surface sediments of a warm monomictic man-made lake: Sabalan Dam Reservoir, Iran. Hydrol. Res. 51, 799–814 (2020).10.2166/nh.2020.160
8. Brevik, E. C. et al. Soil and human health: current status and future needs. Air, Soil Water Res. 13, 117862212093444 (2020).
9. Nodefarahani M Metal pollution assessment in surface sediments of Namak Lake, Iran Environ. Sci. Pollut. Res. 2020 27 45639 45649 10.1007/s11356-020-10298-x
Nodefarahani, M. et al. Metal pollution assessment in surface sediments of Namak Lake, Iran. Environ. Sci. Pollut. Res. 27, 45639–45649 (2020).10.1007/s11356-020-10298-x
10. Sarker A Heavy metals contamination and associated health risks in food webs: A review focuses on food safety and environmental sustainability in Bangladesh Environ. Sci. Pollut. Res. 2022 29 3230 3245 10.1007/s11356-021-17153-7
Sarker, A. et al. Heavy metals contamination and associated health risks in food webs: A review focuses on food safety and environmental sustainability in Bangladesh. Environ. Sci. Pollut. Res. 29, 3230–3245 (2022).10.1007/s11356-021-17153-7
11. Ustaoğlu F Yüksel B Tepe Y Aydın H Topaldemir H Metal pollution assessment in the surface sediments of a river system in Türkiye: Integrating toxicological risk assessment and source identification Mar. Pollut. Bull. 2024 203 116514 10.1016/j.marpolbul.2024.116514 38788275
Ustaoğlu, F., Yüksel, B., Tepe, Y., Aydın, H. & Topaldemir, H. Metal pollution assessment in the surface sediments of a river system in Türkiye: Integrating toxicological risk assessment and source identification. Mar. Pollut. Bull. 203, 116514 (2024).38788275 10.1016/j.marpolbul.2024.116514
12. Virto I Soil Degradation and soil quality in Western Europe: Current situation and future perspectives Sustainability 2014 7 313 365 10.3390/su7010313
Virto, I. et al. Soil Degradation and soil quality in Western Europe: Current situation and future perspectives. Sustainability 7, 313–365 (2014).10.3390/su7010313
13. Prăvălie R Exploring the multiple land degradation pathways across the planet Earth-Sci. Rev. 2021 220 103689 10.1016/j.earscirev.2021.103689
Prăvălie, R. Exploring the multiple land degradation pathways across the planet. Earth-Sci. Rev. 220, 103689 (2021).10.1016/j.earscirev.2021.103689
14. Timmis K Ramos JL The soil crisis: the need to treat as a global health problem and the pivotal role of microbes in prophylaxis and therapy Microb. Biotechnol. 2021 14 769 797 10.1111/1751-7915.13771 33751840
Timmis, K. & Ramos, J. L. The soil crisis: the need to treat as a global health problem and the pivotal role of microbes in prophylaxis and therapy. Microb. Biotechnol. 14, 769–797 (2021).33751840 10.1111/1751-7915.13771
15. Rojas RV Achouri M Maroulis J Caon L Healthy soils: A prerequisite for sustainable food security Environ. Earth Sci. 2016 75 180 10.1007/s12665-015-5099-7
Rojas, R. V., Achouri, M., Maroulis, J. & Caon, L. Healthy soils: A prerequisite for sustainable food security. Environ. Earth Sci. 75, 180 (2016).10.1007/s12665-015-5099-7
16. Ahmad Z Khan SM Ali MI Fatima N Ali S Pollution indicandum and marble waste polluted ecosystem; role of selected indicator plants in phytoremediation and determination of pollution zones J. Clean. Prod. 2019 236 117709 10.1016/j.jclepro.2019.117709
Ahmad, Z., Khan, S. M., Ali, M. I., Fatima, N. & Ali, S. Pollution indicandum and marble waste polluted ecosystem; role of selected indicator plants in phytoremediation and determination of pollution zones. J. Clean. Prod. 236, 117709 (2019).10.1016/j.jclepro.2019.117709
17. Khoiroh U Marble dust exposure relationship to workers’ lung conditions in marble industries J. Kesehat. Lingkung. 2020 12 285 10.20473/jkl.v12i4.2020.285-291
Khoiroh, U. Marble dust exposure relationship to workers’ lung conditions in marble industries. J. Kesehat. Lingkung. 12, 285 (2020).10.20473/jkl.v12i4.2020.285-291
18. Shqiarat M Alnawafleh H Abudanah F Characterization and provenance study of marble from Udhruh, Southern Jordan Mediterr. Archaeol. Archaeom. 2019 19 1 8
Shqiarat, M., Alnawafleh, H. & Abudanah, F. Characterization and provenance study of marble from Udhruh, Southern Jordan. Mediterr. Archaeol. Archaeom. 19, 1–8 (2019).
19. Tazzini A Gambino F Casale M Dino GA Managing marble quarry waste: Opportunities and challenges for circular economy implementation Sustainability 2024 16 3056 10.3390/su16073056
Tazzini, A., Gambino, F., Casale, M. & Dino, G. A. Managing marble quarry waste: Opportunities and challenges for circular economy implementation. Sustainability 16, 3056 (2024).10.3390/su16073056
20. Mulk S Azizullah A Korai AL Khattak MNK Impact of marble industry effluents on water and sediment quality of Barandu River in Buner District, Pakistan Environ. Monit. Assess. 2015 187 8 10.1007/s10661-014-4221-8 25616784
Mulk, S., Azizullah, A., Korai, A. L. & Khattak, M. N. K. Impact of marble industry effluents on water and sediment quality of Barandu River in Buner District, Pakistan. Environ. Monit. Assess. 187, 8 (2015).25616784 10.1007/s10661-014-4221-8
21. Ali, S. et al. Effect of marble mines on physichochemical properties of soil in District Buner. 3, 312–318 (2017).
22. Khan, J., et al. Burden of marble factories and health risk assessment of kidney (renal) stones development in district Buner, Khyber Pakhtunkhwa, Pakistan. Expert Opin. Environ. Biol. (2014).
23. Khan A Irrigation suitability, health risk assessment and source apportionment of heavy metals in surface water used for irrigation near marble industry in Malakand Pakistan. PLoS One 2022 17 1 26
Khan, A. et al. Irrigation suitability, health risk assessment and source apportionment of heavy metals in surface water used for irrigation near marble industry in Malakand. Pakistan. PLoS One 17, 1–26 (2022).
24. Neelab Effects of marble industry effluents on soil quality, growth and productivity of tomato (Lycopersicon esculentum) in district Swat, Khyber Pakhtunkhwa, Pakistan Polish J. Environ. Stud. 2022 31 3263 3270 10.15244/pjoes/144659
Neelab, et al. Effects of marble industry effluents on soil quality, growth and productivity of tomato (Lycopersicon esculentum) in district Swat, Khyber Pakhtunkhwa, Pakistan. Polish J. Environ. Stud. 31, 3263–3270 (2022).10.15244/pjoes/144659
25. Khan, A., Khan, M. S., Hadi, F., Khan, A. N. & Saddiq, G. Energy-dispersive X-ray (EDX) Fluorescence Based Analysis of Heavy Metals in Marble Powder, Paddy Soil and Rice (Oryza sativa L.) With Potential Health Risks in District Malakand, Khyber Pakhtunkhwa, Pakistan. Environ. Pollut. Bioavailab. (2021) 10.1080/26395940.2021.1986427.
26. Khan, A., Khan, M. S., Shafique, M. A., Khan, Q. & Saddiq, G. Assessment of potentially toxic and mineral elements in paddy soils and their uptake by rice (Oryza sativa L.) with associated health hazards in district Malakand, Pakistan. Heliyon 10, e28043 (2024).
27. Azizullah A Khattak MNK Richter P Häder D-P Water pollution in Pakistan and its impact on public health: A review Environ. Int. 2011 37 479 497 10.1016/j.envint.2010.10.007 21087795
Azizullah, A., Khattak, M. N. K., Richter, P. & Häder, D.-P. Water pollution in Pakistan and its impact on public health: A review. Environ. Int. 37, 479–497 (2011).21087795 10.1016/j.envint.2010.10.007
28. Khan M Chaudhry MN Ahmad SR Saif S Mehmood A Performance of EIA authority and effectiveness of EIA system in Pakistan Environ. Impact Assess. Rev. 2020 81 106357 10.1016/j.eiar.2019.106357
Khan, M., Chaudhry, M. N., Ahmad, S. R., Saif, S. & Mehmood, A. Performance of EIA authority and effectiveness of EIA system in Pakistan. Environ. Impact Assess. Rev. 81, 106357 (2020).10.1016/j.eiar.2019.106357
29. Migliore M Lavagna M Talamo C Strategies for the environmental management of the marble districts through the integrated contribution of life cycle assessment and Gis Ital. J. Eng. Geol. Environ. 2017 2 79 87
Migliore, M., Lavagna, M. & Talamo, C. Strategies for the environmental management of the marble districts through the integrated contribution of life cycle assessment and Gis. Ital. J. Eng. Geol. Environ. 2, 79–87 (2017).
30. Deng W Adsorption recovery of phosphate from waste streams by Ca/Mg-biochar synthesis from marble waste, calcium-rich sepiolite and bagasse J. Clean. Prod. 2021 288 125638 10.1016/j.jclepro.2020.125638
Deng, W. et al. Adsorption recovery of phosphate from waste streams by Ca/Mg-biochar synthesis from marble waste, calcium-rich sepiolite and bagasse. J. Clean. Prod. 288, 125638 (2021).10.1016/j.jclepro.2020.125638
31. Rostocki A Unyay H Ławińska K Obraniak A Granulates based on bio and industrial waste and biochar in a sustainable economy Energies 2022 16 56 10.3390/en16010056
Rostocki, A., Unyay, H., Ławińska, K. & Obraniak, A. Granulates based on bio and industrial waste and biochar in a sustainable economy. Energies 16, 56 (2022).10.3390/en16010056
32. Nafees, M., Saeed, T., Zuljajal, F., Akbar, H. & Rehman, A. Marble production and environmental pressure : A case study of Hayatabad industrial estate methodology (2023) 10.21203/rs.3.rs-3244303/v1.
33. Aukour, F. & Al-Qinna, M. Marble production and environmental constrains: Case study from Zarqa Governorate, Jordan. In (2008).
34. Salem HS Yihdego Y Muhammed HH The status of freshwater and reused treated wastewater for agricultural irrigation in the occupied palestinian territories J. Water Health 2021 19 120 158 10.2166/wh.2020.216
Salem, H. S., Yihdego, Y. & Muhammed, H. H. The status of freshwater and reused treated wastewater for agricultural irrigation in the occupied palestinian territories. J. Water Health 19, 120–158 (2021).10.2166/wh.2020.216
35. Noreen U Water pollution and occupational health hazards caused by the marble industries in district Mardan, Pakistan Environ. Technol. Innov. 2019 16 100470 10.1016/j.eti.2019.100470
Noreen, U. et al. Water pollution and occupational health hazards caused by the marble industries in district Mardan, Pakistan. Environ. Technol. Innov. 16, 100470 (2019).10.1016/j.eti.2019.100470
36. Said, A. K., & Muhammad Suleman, M. A. Assessment of pollution load in marble waste water in Khairabad, District. 8, 35–39 (2017).
37. Mulk S Korai AL Azizullah A Shahi L Khattak MNK Marble industry effluents cause an increased bioaccumulation of heavy metals in Mahaseer (Tor putitora) in Barandu River, district Buner, Pakistan Environ. Sci. Pollut. Res. Int. 2017 24 23039 23056 10.1007/s11356-017-9921-5 28825217
Mulk, S., Korai, A. L., Azizullah, A., Shahi, L. & Khattak, M. N. K. Marble industry effluents cause an increased bioaccumulation of heavy metals in Mahaseer (Tor putitora) in Barandu River, district Buner, Pakistan. Environ. Sci. Pollut. Res. Int. 24, 23039–23056 (2017).28825217 10.1007/s11356-017-9921-5
38. Zulfiqar U Cadmium toxicity in plants: Recent progress on morpho-physiological Effects and Remediation Strategies J. Soil Sci. Plant Nutr. 2022 22 212 269 10.1007/s42729-021-00645-3
Zulfiqar, U. et al. Cadmium toxicity in plants: Recent progress on morpho-physiological Effects and Remediation Strategies. J. Soil Sci. Plant Nutr. 22, 212–269 (2022).10.1007/s42729-021-00645-3
39. Nagajyoti PC Lee KD Sreekanth TVM Heavy metals, occurrence and toxicity for plants: A review Environ. Chem. Lett. 2010 8 199 216 10.1007/s10311-010-0297-8
Nagajyoti, P. C., Lee, K. D. & Sreekanth, T. V. M. Heavy metals, occurrence and toxicity for plants: A review. Environ. Chem. Lett. 8, 199–216 (2010).10.1007/s10311-010-0297-8
40. Rahman Z Singh VP The relative impact of toxic heavy metals (THMs) (arsenic (As), cadmium (Cd), chromium (Cr)(VI), mercury (Hg), and lead (Pb)) on the total environment: an overview Environ. Monit. Assess. 2019 191 419 10.1007/s10661-019-7528-7 31177337
Rahman, Z. & Singh, V. P. The relative impact of toxic heavy metals (THMs) (arsenic (As), cadmium (Cd), chromium (Cr)(VI), mercury (Hg), and lead (Pb)) on the total environment: an overview. Environ. Monit. Assess. 191, 419 (2019).31177337 10.1007/s10661-019-7528-7
41. Keshavarzi A Kumar V Ecological risk assessment and source apportionment of heavy metal contamination in agricultural soils of Northeastern Iran Int. J. Environ. Health Res. 2019 29 544 560 10.1080/09603123.2018.1555638 30525957
Keshavarzi, A. & Kumar, V. Ecological risk assessment and source apportionment of heavy metal contamination in agricultural soils of Northeastern Iran. Int. J. Environ. Health Res. 29, 544–560 (2019).30525957 10.1080/09603123.2018.1555638
42. Boente C Albuquerque MTD Gallego JR Pawlowsky-Glahn V Egozcue JJ Compositional baseline assessments to address soil pollution: An application in Langreo Spain. Sci. Total Environ. 2022 812 152383 10.1016/j.scitotenv.2021.152383 34952083
Boente, C., Albuquerque, M. T. D., Gallego, J. R., Pawlowsky-Glahn, V. & Egozcue, J. J. Compositional baseline assessments to address soil pollution: An application in Langreo. Spain. Sci. Total Environ. 812, 152383 (2022).34952083 10.1016/j.scitotenv.2021.152383
43. Rai PK Lee SS Zhang M Tsang YF Kim KH Heavy metals in food crops: Health risks, fate, mechanisms, and management Environ. Int. 2019 125 365 385 10.1016/j.envint.2019.01.067 30743144
Rai, P. K., Lee, S. S., Zhang, M., Tsang, Y. F. & Kim, K. H. Heavy metals in food crops: Health risks, fate, mechanisms, and management. Environ. Int. 125, 365–385 (2019).30743144 10.1016/j.envint.2019.01.067
44. Gabarrón M Faz A Acosta JA Use of multivariable and redundancy analysis to assess the behavior of metals and arsenic in urban soil and road dust affected by metallic mining as a base for risk assessment J. Environ. Manage. 2018 206 192 201 10.1016/j.jenvman.2017.10.034 29065360
Gabarrón, M., Faz, A. & Acosta, J. A. Use of multivariable and redundancy analysis to assess the behavior of metals and arsenic in urban soil and road dust affected by metallic mining as a base for risk assessment. J. Environ. Manage. 206, 192–201 (2018).29065360 10.1016/j.jenvman.2017.10.034
45. Wang, S. et al. Source and health risk assessment of heavy metals in soil–ginger system in the jing river basin of Shandong province, North China. Int. J. Environ. Res. Public Health 18, (2021).
46. Nduka JK Kelle HI Amuka JO Health risk assessment of cadmium, chromium and nickel from car paint dust from used automobiles at auto-panel workshops in Nigeria Toxicol. Rep. 2019 6 449 456 10.1016/j.toxrep.2019.05.007 31193556
Nduka, J. K., Kelle, H. I. & Amuka, J. O. Health risk assessment of cadmium, chromium and nickel from car paint dust from used automobiles at auto-panel workshops in Nigeria. Toxicol. Rep. 6, 449–456 (2019).31193556 10.1016/j.toxrep.2019.05.007
47. Anyimah E Isaac A Ofosu W Erick H Godfred L Exposures and health risks associated with elements in diets from a gold mining area Biol. Trace Elem. Res. 2021 10.1007/s12011-021-02777-0
Anyimah, E., Isaac, A., Ofosu, W., Erick, H. & Godfred, L. Exposures and health risks associated with elements in diets from a gold mining area. Biol. Trace Elem. Res.10.1007/s12011-021-02777-0 (2021).10.1007/s12011-021-02777-0
48. Butt, I. M., Mustafa, T., Rauf, S., Razzaq, A. & Anwer, J. Pulmonary function parameters among marble industry workers in Lahore, Pakistan. F1000Research 10, 938 (2021).
49. Fawad M Pollution hotspots and potential impacts on land use in the Mohmand Marble Zone, Pakistan Environ. Earth Sci. 2021 80 1 12 10.1007/s12665-021-09677-5
Fawad, M. et al. Pollution hotspots and potential impacts on land use in the Mohmand Marble Zone, Pakistan. Environ. Earth Sci. 80, 1–12 (2021).10.1007/s12665-021-09677-5
50. Alizadeh M Noori R Omidvar B Nohegar A Pistre S Human health risk of nitrate in groundwater of Tehran-Karaj plain, Iran Sci. Rep. 2024 14 7830 10.1038/s41598-024-58290-6 38570538
Alizadeh, M., Noori, R., Omidvar, B., Nohegar, A. & Pistre, S. Human health risk of nitrate in groundwater of Tehran-Karaj plain, Iran. Sci. Rep. 14, 7830 (2024).38570538 10.1038/s41598-024-58290-6
51. Pawlowsky-Glahn, V. & Buccianti, A. Compositional Data Analysis: Theory and Applications. Compositional Data Analysis: Theory and Applications (John Wiley & Sons, Ltd, 2011). 10.1002/9781119976462.
52. Fačevicová K Bábek O Hron K Kumpan T Element chemostratigraphy of the Devonian/Carboniferous boundary: A compositional approach Appl. Geochem. 2016 75 211 221 10.1016/j.apgeochem.2016.10.002
Fačevicová, K., Bábek, O., Hron, K. & Kumpan, T. Element chemostratigraphy of the Devonian/Carboniferous boundary: A compositional approach. Appl. Geochem. 75, 211–221 (2016).10.1016/j.apgeochem.2016.10.002
53. Aitchison, J. The Statistical Analysis of Compositional Data. Monographs on Statistics and Applied Probability. (Chapman and Hall Ltd, London, UK, 1986).
54. Pawlowsky-Glahn, V. & Egozcue, J. J. Compositional data and their analysis: an introduction. Geol. Soc. London Spec. Publ. 264, 1–10 (2006).
55. Pawlowsky-Glahn, V., Egozcue, J. J. & Tolosana-Delgado, R. Modelling and Analysis of Compositional Data. (John Wiley & Sons, Ltd, 2015). 10.1002/9781119003144.
56. Coenders, G., Hlebec, V. & Kogovsšek, T. Measurement quality in indicators of compositions. A compositional multitrait-multimethod approach. Surv. Res. Methods 5, 63–74 (2011).
57. Egozcue JJ Pawlowsky-Glahn V Mateu-Figueras G Barceló-Vidal C Isometric logratio transformations for compositional data analysis Math. Geol. 2003 35 279 300 10.1023/A:1023818214614
Egozcue, J. J., Pawlowsky-Glahn, V., Mateu-Figueras, G. & Barceló-Vidal, C. Isometric logratio transformations for compositional data analysis. Math. Geol. 35, 279–300 (2003).10.1023/A:1023818214614
58. Pawlowsky-Glahn V Egozcue JJ Geometric approach to statistical analysis on the simplex Stoch. Environ. Res. Risk Assess. 2001 15 384 398 10.1007/s004770100077
Pawlowsky-Glahn, V. & Egozcue, J. J. Geometric approach to statistical analysis on the simplex. Stoch. Environ. Res. Risk Assess. 15, 384–398 (2001).10.1007/s004770100077
59. Templ M Filzmoser P Reimann C Cluster analysis applied to regional geochemical data: Problems and possibilities Appl. Geochem. 2008 23 2198 2213 10.1016/j.apgeochem.2008.03.004
Templ, M., Filzmoser, P. & Reimann, C. Cluster analysis applied to regional geochemical data: Problems and possibilities. Appl. Geochem. 23, 2198–2213 (2008).10.1016/j.apgeochem.2008.03.004
60. Thiombane M Exploratory analysis of multi-element geochemical patterns in soil from the Sarno River Basin (Campania region, southern Italy) through compositional data analysis (CODA) J. Geochem. Explor. 2018 195 110 120 10.1016/j.gexplo.2018.03.010
Thiombane, M. et al. Exploratory analysis of multi-element geochemical patterns in soil from the Sarno River Basin (Campania region, southern Italy) through compositional data analysis (CODA). J. Geochem. Explor. 195, 110–120 (2018).10.1016/j.gexplo.2018.03.010
61. Xiao R Accumulation, ecological-health risks assessment, and source apportionment of heavy metals in paddy soils: A case study in Hanzhong, Shaanxi, China Environ. Pollut. 2019 248 349 357 10.1016/j.envpol.2019.02.045 30818114
Xiao, R. et al. Accumulation, ecological-health risks assessment, and source apportionment of heavy metals in paddy soils: A case study in Hanzhong, Shaanxi, China. Environ. Pollut. 248, 349–357 (2019).30818114 10.1016/j.envpol.2019.02.045
62. Ren W Reconsidering brownfield redevelopment strategy in China’s old industrial zone: A health risk assessment of heavy metal contamination Environ. Sci. Pollut. Res. 2015 22 2765 2775 10.1007/s11356-014-3548-6
Ren, W. et al. Reconsidering brownfield redevelopment strategy in China’s old industrial zone: A health risk assessment of heavy metal contamination. Environ. Sci. Pollut. Res. 22, 2765–2775 (2015).10.1007/s11356-014-3548-6
63. Islam MS Ahmed MK Habibullah-Al-Mamun M Heavy metals in cereals and pulses: Health implications in Bangladesh J. Agric. Food Chem. 2014 62 10828 10835 10.1021/jf502486q 25307424
Islam, M. S., Ahmed, M. K. & Habibullah-Al-Mamun, M. Heavy metals in cereals and pulses: Health implications in Bangladesh. J. Agric. Food Chem. 62, 10828–10835 (2014).25307424 10.1021/jf502486q
64. Albanese S Potentially toxic elements in the soils of Campi Flegrei (south Italy) and the immediate surroundings: Spatial distribution, origin and probabilistic human health risk Chemosphere 2023 313 137297 10.1016/j.chemosphere.2022.137297 36410516
Albanese, S. et al. Potentially toxic elements in the soils of Campi Flegrei (south Italy) and the immediate surroundings: Spatial distribution, origin and probabilistic human health risk. Chemosphere 313, 137297 (2023).36410516 10.1016/j.chemosphere.2022.137297
65. Yang S Status assessment and probabilistic health risk modeling of metals accumulation in agriculture soils across China: A synthesis Environ. Int. 2019 128 165 174 10.1016/j.envint.2019.04.044 31055203
Yang, S. et al. Status assessment and probabilistic health risk modeling of metals accumulation in agriculture soils across China: A synthesis. Environ. Int. 128, 165–174 (2019).31055203 10.1016/j.envint.2019.04.044
66. Mallongi, A. et al. Health risk assessment of potentially toxic elements in Maros karst groundwater: a Monte Carlo simulation approach. Geom. Nat. Hazards Risk 13, 338–363 (2022).
67. Wang C Monte carlo simulation Springer Ser. Reliab. Eng. 2021 10.1007/978-3-030-62505-4_3
Wang, C. Monte carlo simulation. Springer Ser. Reliab. Eng.10.1007/978-3-030-62505-4_3 (2021).10.1007/978-3-030-62505-4_3
68. Moazamnia M Probabilistic human health risk assessment for arsenic, nickel and lead exposures based on two-dimensional Monte Carlo simulation Groundw. Sustain. Dev. 2024 27 101312 10.1016/j.gsd.2024.101312
Moazamnia, M. et al. Probabilistic human health risk assessment for arsenic, nickel and lead exposures based on two-dimensional Monte Carlo simulation. Groundw. Sustain. Dev. 27, 101312 (2024).10.1016/j.gsd.2024.101312
69. Yang, Z. et al. Spatial distribution and sources of seven available heavy metals in the paddy soil of red region in Hunan Province of China. Environ. Monit. Assess. (2018).
70. White, C. M. & Barbercheck, M. E. Managing Soil Health: Concepts and Practices. https://extension.psu.edu/managing-soil-health-concepts-and-practices (2012).
71. Aqeel, M., Jamil, M. & Yusoff, I. Soil Contamination, Risk Assessment and Remediation. in Environmental Risk Assessment of Soil Contamination vol. i 13 (InTech, 2014).
72. Hou D Bolan NS Tsang DCW Kirkham MB O’Connor D Sustainable soil use and management: An interdisciplinary and systematic approach Sci. Total Environ. 2020 729 138961 10.1016/j.scitotenv.2020.138961 32353725
Hou, D., Bolan, N. S., Tsang, D. C. W., Kirkham, M. B. & O’Connor, D. Sustainable soil use and management: An interdisciplinary and systematic approach. Sci. Total Environ. 729, 138961 (2020).32353725 10.1016/j.scitotenv.2020.138961
73. Hussain, S. Species diversity of basidiomycetes of district Malakand. (PhD Dessertation, Department of Botany, Hazara University. pp 1–249.http://prr.hec.gov.pk/jspui/handle/123456789/7580, (2016).
74. Jan, S., Khan, K., Hameed, I. & Ahmad, N. Ethnobotanical studies of the medicinal plants of Malakand agency, Khyber Pakhtunkhwa. Pakistan J. Plant Sci. 18, (2012).
75. Khan A Khan N Ali K An assessment of the floristic diversity, life-forms and biological spectrum of vegetation in Swat Ranizai, district Malakand, Khyber Pakhtunkhwa, Pakistan Technol. Dev. 2017 36 61 78
Khan, A., Khan, N. & Ali, K. An assessment of the floristic diversity, life-forms and biological spectrum of vegetation in Swat Ranizai, district Malakand, Khyber Pakhtunkhwa, Pakistan. Technol. Dev. 36, 61–78 (2017).
76. Iqbal M Khalid N Sajjad M Pervaiz U Ullah S Farmers field schools and rice productivity: An empirical analysis of District Malakand Sarhad J. Agric. 2012 28 143 149
Iqbal, M., Khalid, N., Sajjad, M., Pervaiz, U. & Ullah, S. Farmers field schools and rice productivity: An empirical analysis of District Malakand. Sarhad J. Agric. 28, 143–149 (2012).
77. Malkani, M. S., Mahmood, Z., Alyani, M. I. & Siraj, M. Mineral Resources of Khyber Pakhtunkhwa and FATA, Pakistan. Geol. Surv. Pakistan, Inf. Release 996, 1–61 (2017).
78. Wroble J Frederick T Frame A Vallero D Comparison of soil sampling and analytical methods for asbestos at the Sumas mountain asbestos site, working towards a toolbox for better assessment PLoS One 2017 12 1 26 10.1371/journal.pone.0180210
Wroble, J., Frederick, T., Frame, A. & Vallero, D. Comparison of soil sampling and analytical methods for asbestos at the Sumas mountain asbestos site, working towards a toolbox for better assessment. PLoS One 12, 1–26 (2017).10.1371/journal.pone.0180210
79. Rafique, N. & Tariq, S. R. Distribution and source apportionment studies of heavy metals in soil of cotton/wheat fields. Environ. Monit. Assess. 188, (2016).
80. Khan MU Malik RN Muhammad S Human health risk from Heavy metal via food crops consumption with wastewater irrigation practices in Pakistan Chemosphere 2013 93 2230 2238 10.1016/j.chemosphere.2013.07.067 24075531
Khan, M. U., Malik, R. N. & Muhammad, S. Human health risk from Heavy metal via food crops consumption with wastewater irrigation practices in Pakistan. Chemosphere 93, 2230–2238 (2013).24075531 10.1016/j.chemosphere.2013.07.067
81. Ali, F., Ullah, H. & Khan, I. Heavy Metals Accumulation in Vegetables Irrigated with Industrial Influents and Possible Impact of such Vegetables on Human Health. Sarhad J. Agric. (2017).
82. Shi C Ding H Zan Q Li R Spatial variation and ecological risk assessment of heavy metals in mangrove sediments across China Mar. Pollut. Bull. 2019 143 115 124 10.1016/j.marpolbul.2019.04.043 31789145
Shi, C., Ding, H., Zan, Q. & Li, R. Spatial variation and ecological risk assessment of heavy metals in mangrove sediments across China. Mar. Pollut. Bull. 143, 115–124 (2019).31789145 10.1016/j.marpolbul.2019.04.043
83. Usman K Al-Ghouti MA Abu-Dieyeh MH The assessment of cadmium, chromium, copper, and nickel tolerance and bioaccumulation by shrub plant Tetraena qataranse Sci. Rep. 2019 9 1 11 10.1038/s41598-019-42029-9 30626917
Usman, K., Al-Ghouti, M. A. & Abu-Dieyeh, M. H. The assessment of cadmium, chromium, copper, and nickel tolerance and bioaccumulation by shrub plant Tetraena qataranse. Sci. Rep. 9, 1–11 (2019).30626917 10.1038/s41598-019-42029-9
84. Doabi SA Karami M Afyuni M Yeganeh M Pollution and health risk assessment of heavy metals in agricultural soil, atmospheric dust and major food crops in Kermanshah province, Iran Ecotoxicol. Environ. Saf. 2018 163 153 164 10.1016/j.ecoenv.2018.07.057 30053585
Doabi, S. A., Karami, M., Afyuni, M. & Yeganeh, M. Pollution and health risk assessment of heavy metals in agricultural soil, atmospheric dust and major food crops in Kermanshah province, Iran. Ecotoxicol. Environ. Saf. 163, 153–164 (2018).30053585 10.1016/j.ecoenv.2018.07.057
85. Liang, Y. et al. Heavy metal contamination and health risk assessment in the vicinity of a tailing pond in Guangdong, China. Int. J. Environ. Res. Public Health (2017).
86. Paetz, A. & Crmann, G. Problems and results in the development of international standards for sampling and pretreatment of soils. In Environmental Sampling for Trace Analysis 321–334 (Wiley-VCH Verlag GmbH, 1994). 10.1002/9783527615872.ch16.
87. Adebiyi, F. M. & Ore, O. T. Assessing highway-related metal pollution using surface soil and tree bark as indicators. Environ. Forensics (2021) 10.1080/15275922.2021.1976314.
88. Muller G Index of geoaccumulation in sediments of the Rhine River GeoJournal 1969 2 108 118
Muller, G. Index of geoaccumulation in sediments of the Rhine River. GeoJournal 2, 108–118 (1969).
89. Muller G The heavy metal pollution of the sediments of Neckars and its tributary: A stocktaking Chem Zeit 1981 105 157 164
Muller, G. The heavy metal pollution of the sediments of Neckars and its tributary: A stocktaking. Chem Zeit 105, 157–164 (1981).
90. Hakanson, L. An ecological risk index for aquatic pollution control.a sedimentological approach. Water Res. 14, 975–1001 (1980).
91. Tomlinson DL Wilson JG Harris CR Jeffrey DW Problems in the assessment of heavy-metal levels in estuaries and the formation of a pollution index Helgoländer Meeresuntersuchungen 1980 33 566 575 10.1007/BF02414780
Tomlinson, D. L., Wilson, J. G., Harris, C. R. & Jeffrey, D. W. Problems in the assessment of heavy-metal levels in estuaries and the formation of a pollution index. Helgoländer Meeresuntersuchungen 33, 566–575 (1980).10.1007/BF02414780
92. Lu X Wang L Lei K Huang J Zhai Y Contamination assessment of copper, lead, zinc, manganese and nickel in street dust of Baoji, NW China J. Hazard. Mater. 2009 161 1058 1062 10.1016/j.jhazmat.2008.04.052 18502044
Lu, X., Wang, L., Lei, K., Huang, J. & Zhai, Y. Contamination assessment of copper, lead, zinc, manganese and nickel in street dust of Baoji, NW China. J. Hazard. Mater. 161, 1058–1062 (2009).18502044 10.1016/j.jhazmat.2008.04.052
93. Turekian KK Wedepohl KH Distribution of the elements in some Major Units of the Earth’s Crust GSA Bull. 1961 72 175 192 10.1130/0016-7606(1961)72[175:DOTEIS]2.0.CO;2
Turekian, K. K. & Wedepohl, K. H. Distribution of the elements in some Major Units of the Earth’s Crust. GSA Bull. 72, 175–192 (1961).10.1130/0016-7606(1961)72[175:DOTEIS]2.0.CO;2
94. Lu X Pan H Wang Y Pollution evaluation and source analysis of heavy metal in roadway dust from a resource-typed industrial city in Northwest China Atmos. Pollut. Res. 2017 8 587 595 10.1016/j.apr.2016.12.019
Lu, X., Pan, H. & Wang, Y. Pollution evaluation and source analysis of heavy metal in roadway dust from a resource-typed industrial city in Northwest China. Atmos. Pollut. Res. 8, 587–595 (2017).10.1016/j.apr.2016.12.019
95. Karimi A Assessment of human health risks and pollution index for heavy metals in farmlands irrigated by effluents of stabilization ponds Environ. Sci. Pollut. Res. 2020 27 10317 10327 10.1007/s11356-020-07642-6
Karimi, A. et al. Assessment of human health risks and pollution index for heavy metals in farmlands irrigated by effluents of stabilization ponds. Environ. Sci. Pollut. Res. 27, 10317–10327 (2020).10.1007/s11356-020-07642-6
96. El Azhari A Rhoujjati A El Hachimi ML Ambrosi J Pollution and ecological risk assessment of heavy metals in the soil-plant system and the sediment-water column around a former Pb/Zn-mining area in NE Morocco Ecotoxicol. Environ. Saf. 2017 144 464 474 10.1016/j.ecoenv.2017.06.051 28667858
El Azhari, A., Rhoujjati, A., El Hachimi, M. L. & Ambrosi, J. Pollution and ecological risk assessment of heavy metals in the soil-plant system and the sediment-water column around a former Pb/Zn-mining area in NE Morocco. Ecotoxicol. Environ. Saf. 144, 464–474 (2017).28667858 10.1016/j.ecoenv.2017.06.051
97. Weissmannová HD Pavlovský J Indices of soil contamination by heavy metals—methodology of calculation for pollution assessment (minireview) Environ. Monit. Assess. 2017 189 616 10.1007/s10661-017-6340-5 29116419
Weissmannová, H. D. & Pavlovský, J. Indices of soil contamination by heavy metals—methodology of calculation for pollution assessment (minireview). Environ. Monit. Assess. 189, 616 (2017).29116419 10.1007/s10661-017-6340-5
98. Emenike, P. G. C. et al. An integrated assessment of land-use change impact, seasonal variation of pollution indices and human health risk of selected toxic elements in sediments of River Atuwara, Nigeria. Environ. Pollut. 265, (2020).
99. Lu X Risk assessment of toxic metals in street dust from a medium-sized industrial city of China Ecotoxicol. Environ. Saf. 2014 106 154 163 10.1016/j.ecoenv.2014.04.022 24840878
Lu, X. et al. Risk assessment of toxic metals in street dust from a medium-sized industrial city of China. Ecotoxicol. Environ. Saf. 106, 154–163 (2014).24840878 10.1016/j.ecoenv.2014.04.022
100. Lu X Shi D Yin N Pan H Smith P Risk assessment of heavy metals in finer than 63-μm dust particles from various functional areas in Xi’an, China Air Qual. Atmos. Heal. 2017 10 907 915 10.1007/s11869-017-0480-1
Lu, X., Shi, D., Yin, N., Pan, H. & Smith, P. Risk assessment of heavy metals in finer than 63-μm dust particles from various functional areas in Xi’an, China. Air Qual. Atmos. Heal. 10, 907–915 (2017).10.1007/s11869-017-0480-1
101. Ke X Ecological risk assessment and source identification for heavy metals in surface sediment from the Liaohe River protected area, China Chemosphere 2017 175 473 481 10.1016/j.chemosphere.2017.02.029 28242463
Ke, X. et al. Ecological risk assessment and source identification for heavy metals in surface sediment from the Liaohe River protected area, China. Chemosphere 175, 473–481 (2017).28242463 10.1016/j.chemosphere.2017.02.029
102. Adimalla N Heavy metals contamination in urban surface soils of Medak province, India, and its risk assessment and spatial distribution Environ. Geochem. Health 2020 42 59 75 10.1007/s10653-019-00270-1 30843166
Adimalla, N. Heavy metals contamination in urban surface soils of Medak province, India, and its risk assessment and spatial distribution. Environ. Geochem. Health 42, 59–75 (2020).30843166 10.1007/s10653-019-00270-1
103. Lian M Profiles and potential health risks of heavy metals in soil and crops from the watershed of Xi River in Northeast China Ecotoxicol. Environ. Saf. 2019 169 442 448 10.1016/j.ecoenv.2018.11.046 30471581
Lian, M. et al. Profiles and potential health risks of heavy metals in soil and crops from the watershed of Xi River in Northeast China. Ecotoxicol. Environ. Saf. 169, 442–448 (2019).30471581 10.1016/j.ecoenv.2018.11.046
104. USEPA. Risk Assessment Guidance for Superfund Volume I : Human Health Evaluation Manual ( PartE, Supplemental Guidance for Dermal Risk Assessment ) Final. (2004).
105. Ahmad W Toxic and heavy metals contamination assessment in soil and water to evaluate human health risk Sci. Rep. 2021 11 1 12 10.1038/s41598-021-94616-4 33414495
Ahmad, W. et al. Toxic and heavy metals contamination assessment in soil and water to evaluate human health risk. Sci. Rep. 11, 1–12 (2021).33414495 10.1038/s41598-021-94616-4
106. Dessie BK Gari SR Mihret A Desta AF Mehari B Determination and health risk assessment of trace elements in the tap water of two sub-cities of Addis Ababa Ethiopia. Heliyon 2021 7 e06988 10.1016/j.heliyon.2021.e06988 34136673
Dessie, B. K., Gari, S. R., Mihret, A., Desta, A. F. & Mehari, B. Determination and health risk assessment of trace elements in the tap water of two sub-cities of Addis Ababa. Ethiopia. Heliyon 7, e06988 (2021).34136673 10.1016/j.heliyon.2021.e06988
107. Sultana MS Rana S Yamazaki S Aono T Yoshida S Health risk assessment for carcinogenic and non-carcinogenic heavy metal exposures from vegetables and fruits of Bangladesh Cogent Environ. Sci. 2017 3 1 17 10.1080/23311843.2017.1291107
Sultana, M. S., Rana, S., Yamazaki, S., Aono, T. & Yoshida, S. Health risk assessment for carcinogenic and non-carcinogenic heavy metal exposures from vegetables and fruits of Bangladesh. Cogent Environ. Sci. 3, 1–17 (2017).10.1080/23311843.2017.1291107
108. USEPA. Risk Assessment Guidance for Superfund. Human Health Evaluation Manual (Part A). vol. I https://rais.ornl.gov/documents/HHEMA.pdf (1989).
109. WHO. Agents Classified by the International Agency for Research on Cancer (IARC) Monographs, Volumes 1–23. https://monographs.iarc.who.int/wp-content/uploads/2018/09/ClassificationsAlphaOrder.pdf (2016).
110. Mesa-Frias M Chalabi Z Vanni T Foss AM Uncertainty in environmental health impact assessment: Quantitative methods and perspectives Int. J. Environ. Health Res. 2013 23 16 30 10.1080/09603123.2012.678002 22515647
Mesa-Frias, M., Chalabi, Z., Vanni, T. & Foss, A. M. Uncertainty in environmental health impact assessment: Quantitative methods and perspectives. Int. J. Environ. Health Res. 23, 16–30 (2013).22515647 10.1080/09603123.2012.678002
111. Mari M Nadal M Schuhmacher M Domingo JL Exposure to heavy metals and PCDD/Fs by the population living in the vicinity of a hazardous waste landfill in Catalonia, Spain: Health risk assessment Environ. Int. 2009 35 1034 1039 10.1016/j.envint.2009.05.004 19515422
Mari, M., Nadal, M., Schuhmacher, M. & Domingo, J. L. Exposure to heavy metals and PCDD/Fs by the population living in the vicinity of a hazardous waste landfill in Catalonia, Spain: Health risk assessment. Environ. Int. 35, 1034–1039 (2009).19515422 10.1016/j.envint.2009.05.004
112. Li X Status of copper accumulation in agricultural soils across China (1985–2016) Chemosphere 2020 244 125516 10.1016/j.chemosphere.2019.125516 31830645
Li, X. et al. Status of copper accumulation in agricultural soils across China (1985–2016). Chemosphere 244, 125516 (2020).31830645 10.1016/j.chemosphere.2019.125516
113. Wang Z Yin Z Caers J Zuo R A Monte Carlo-based framework for risk-return analysis in mineral prospectivity mapping Geosci. Front. 2020 11 2297 2308 10.1016/j.gsf.2020.02.010
Wang, Z., Yin, Z., Caers, J. & Zuo, R. A Monte Carlo-based framework for risk-return analysis in mineral prospectivity mapping. Geosci. Front. 11, 2297–2308 (2020).10.1016/j.gsf.2020.02.010
114. Sanaei F Health risk assessment of potentially toxic elements intake via food crops consumption: Monte Carlo simulation-based probabilistic and heavy metal pollution index Environ. Sci. Pollut. Res. 2021 28 1479 1490 10.1007/s11356-020-10450-7
Sanaei, F. et al. Health risk assessment of potentially toxic elements intake via food crops consumption: Monte Carlo simulation-based probabilistic and heavy metal pollution index. Environ. Sci. Pollut. Res. 28, 1479–1490 (2021).10.1007/s11356-020-10450-7
115. Guo G Zhang D Wang Y Probabilistic human health risk assessment of heavy metal intake via vegetable consumption around Pb/Zn Smelters in Southwest China Int. J. Environ. Res. Public Health 2019 16 3267 10.3390/ijerph16183267 31491979
Guo, G., Zhang, D. & Wang, Y. Probabilistic human health risk assessment of heavy metal intake via vegetable consumption around Pb/Zn Smelters in Southwest China. Int. J. Environ. Res. Public Health 16, 3267 (2019).31491979 10.3390/ijerph16183267
116. Jiang C Distribution, source and health risk assessment based on the Monte Carlo method of heavy metals in shallow groundwater in an area affected by mining activities, China Ecotoxicol. Environ. Saf. 2021 224 112679 10.1016/j.ecoenv.2021.112679 34419640
Jiang, C. et al. Distribution, source and health risk assessment based on the Monte Carlo method of heavy metals in shallow groundwater in an area affected by mining activities, China. Ecotoxicol. Environ. Saf. 224, 112679 (2021).34419640 10.1016/j.ecoenv.2021.112679
117. Egozcue JJ Pawlowsky-Glahn V Compositional data: The sample space and its structure TEST 2019 28 599 638 10.1007/s11749-019-00670-6
Egozcue, J. J. & Pawlowsky-Glahn, V. Compositional data: The sample space and its structure. TEST 28, 599–638 (2019).10.1007/s11749-019-00670-6
118. Thio-Henestrosa S Comas S CoDaPack v2 User’s Guide 2016 University of Girona
Thio-Henestrosa, S. & Comas, S. CoDaPack v2 User’s Guide (University of Girona, 2016).
119. van den Boogaart KG Tolosana-Delgado R Analyzing compositional data with R Analyz. Compos. Data R 2013 10.1007/978-3-642-36809-7
van den Boogaart, K. G. & Tolosana-Delgado, R. Analyzing compositional data with R. Analyz. Compos. Data R10.1007/978-3-642-36809-7 (2013).10.1007/978-3-642-36809-7
120. Sall J Stephens M Loring S Lehman A JMP start statistics: A guide to statistics and data analysis using JMP 2017 SAS Institute Inc
Sall, J., Stephens, M., Loring, S. & Lehman, A. JMP start statistics: A guide to statistics and data analysis using JMP (SAS Institute Inc, 2017).
121. Zornoza R Faz A Carmona DM Martínez-Martínez S Acosta JA Plant cover and soil biochemical properties in a mine tailing pond five years after application of marble wastes and organic amendments Pedosphere 2012 22 22 32 10.1016/S1002-0160(11)60188-4
Zornoza, R., Faz, A., Carmona, D. M., Martínez-Martínez, S. & Acosta, J. A. Plant cover and soil biochemical properties in a mine tailing pond five years after application of marble wastes and organic amendments. Pedosphere 22, 22–32 (2012).10.1016/S1002-0160(11)60188-4
122. Mohanavelu A Naganna SR Al-Ansari N Irrigation induced salinity and sodicity hazards on soil and groundwater: An overview of its causes, impacts and mitigation strategies Agriculture 2021 11 983 10.3390/agriculture11100983
Mohanavelu, A., Naganna, S. R. & Al-Ansari, N. Irrigation induced salinity and sodicity hazards on soil and groundwater: An overview of its causes, impacts and mitigation strategies. Agriculture 11, 983 (2021).10.3390/agriculture11100983
123. Zewd I Siban M The effects of alkalinity on physical and chemical properties of soil J. Plant Biol. Agric. Sci. 2021 3 1 5
Zewd, I. & Siban, M. The effects of alkalinity on physical and chemical properties of soil. J. Plant Biol. Agric. Sci. 3, 1–5 (2021).
124. Saha, L. & Bauddh, K. Sustainable agricultural approaches for enhanced crop productivity, better soil health, and improved ecosystem services BT—ecological and practical applications for sustainable agriculture. In Bauddh, K., Kumar, S., Singh, R. P. & Korstad, J. (eds) 1–23 (Springer Singapore, 2020). 10.1007/978-981-15-3372-3_1.
125. Mishra, A. K. et al. Promising management strategies to improve crop sustainability and to amend soil salinity. Front. Environ. Sci. (2023).
126. Shakir E Zahraw Z Al-Obaidy AHMJ Environmental and health risks associated with reuse of wastewater for irrigation Egypt. J. Pet. 2017 26 95 102 10.1016/j.ejpe.2016.01.003
Shakir, E., Zahraw, Z. & Al-Obaidy, A. H. M. J. Environmental and health risks associated with reuse of wastewater for irrigation. Egypt. J. Pet. 26, 95–102 (2017).10.1016/j.ejpe.2016.01.003
127. Ozcelik M Environmental pollution and its effect on water sources from marble quarries in western Turkey Environ. Earth Sci. 2016 75 796 10.1007/s12665-016-5627-0
Ozcelik, M. Environmental pollution and its effect on water sources from marble quarries in western Turkey. Environ. Earth Sci. 75, 796 (2016).10.1007/s12665-016-5627-0
128. Noreen U Water pollution and occupational health hazards caused by the marble industries in district Mardan, Pakistan Environ. Technol. Innov. 2019 16 100470 10.1016/j.eti.2019.100470
Noreen, U. et al. Water pollution and occupational health hazards caused by the marble industries in district Mardan, Pakistan. Environ. Technol. Innov. 16, 100470 (2019).10.1016/j.eti.2019.100470
129. Singare P Meshram N Kausadikar H Calcareus soil and their management: A review Just Agric. 2022 3 1 6
Singare, P., Meshram, N. & Kausadikar, H. Calcareus soil and their management: A review. Just Agric. 3, 1–6 (2022).
130. Carrillo V Fuentes B Gómez G Vidal G Characterization and recovery of phosphorus from wastewater by combined technologies Rev. Environ. Sci. Bio/Technol. 2020 19 389 418 10.1007/s11157-020-09533-1
Carrillo, V., Fuentes, B., Gómez, G. & Vidal, G. Characterization and recovery of phosphorus from wastewater by combined technologies. Rev. Environ. Sci. Bio/Technol. 19, 389–418 (2020).10.1007/s11157-020-09533-1
131. Feng W Chemical composition, sources, and ecological effect of organic phosphorus in water ecosystems: A review Carbon Res. 2023 2 12 10.1007/s44246-023-00038-4
Feng, W. et al. Chemical composition, sources, and ecological effect of organic phosphorus in water ecosystems: A review. Carbon Res. 2, 12 (2023).10.1007/s44246-023-00038-4
132. Krishna AK Govil PK Heavy metal distribution and contamination in soils of Thane-Belapur industrial development area, Mumbai Western India Environ. Geol. 2005 47 1054 1061 10.1007/s00254-005-1238-x
Krishna, A. K. & Govil, P. K. Heavy metal distribution and contamination in soils of Thane-Belapur industrial development area, Mumbai Western India. Environ. Geol. 47, 1054–1061 (2005).10.1007/s00254-005-1238-x
133. Bhuiyan MAH Parvez L Islam MA Dampare SB Suzuki S Heavy metal pollution of coal mine-affected agricultural soils in the northern part of Bangladesh J. Hazard. Mater. 2010 173 384 392 10.1016/j.jhazmat.2009.08.085 19744789
Bhuiyan, M. A. H., Parvez, L., Islam, M. A., Dampare, S. B. & Suzuki, S. Heavy metal pollution of coal mine-affected agricultural soils in the northern part of Bangladesh. J. Hazard. Mater. 173, 384–392 (2010).19744789 10.1016/j.jhazmat.2009.08.085
134. Charkiewicz AE Omeljaniuk WJ Nowak K Garley M Nikliński J Cadmium toxicity and health effects—a brief summary Molecules 2023 28 6620 10.3390/molecules28186620 37764397
Charkiewicz, A. E., Omeljaniuk, W. J., Nowak, K., Garley, M. & Nikliński, J. Cadmium toxicity and health effects—a brief summary. Molecules 28, 6620 (2023).37764397 10.3390/molecules28186620
135. Genchi G Sinicropi MS Lauria G Carocci A Catalano A The effects of cadmium toxicity Int. J. Environ. Res. Public Health 2020 17 3782 10.3390/ijerph17113782 32466586
Genchi, G., Sinicropi, M. S., Lauria, G., Carocci, A. & Catalano, A. The effects of cadmium toxicity. Int. J. Environ. Res. Public Health 17, 3782 (2020).32466586 10.3390/ijerph17113782
136. Shi D Lu X Accumulation degree and source apportionment of trace metals in smaller than 63 μm road dust from the areas with different land uses: A case study of Xi’an China Sci. Total Environ. 2018 636 1211 1218 10.1016/j.scitotenv.2018.04.385 29913583
Shi, D. & Lu, X. Accumulation degree and source apportionment of trace metals in smaller than 63 μm road dust from the areas with different land uses: A case study of Xi’an China. Sci. Total Environ. 636, 1211–1218 (2018).29913583 10.1016/j.scitotenv.2018.04.385
137. Zuo R Identification of weak geochemical anomalies using robust neighborhood statistics coupled with GIS in covered areas J. Geochem. Explor. 2014 136 93 101 10.1016/j.gexplo.2013.10.011
Zuo, R. Identification of weak geochemical anomalies using robust neighborhood statistics coupled with GIS in covered areas. J. Geochem. Explor. 136, 93–101 (2014).10.1016/j.gexplo.2013.10.011
138. Zhuang, S. & Lu, X. Environmental risk evaluation and source identification of heavy metal (loid )s in agricultural soil of Shangdan valley, Northwest China. (2020).
139. Khan, K. et al. Heavy metal pollution in the soil of a riverine basin: Distributio, source, and potential hazards. 1–15 (2022) 10.1007/s10661-022-10287-2.
140. Chen X Lu X Yang G Sources identification of heavy metals in urban topsoil from inside the Xi’an second ringroad, NW China using multivariate statistical methods CATENA 2012 98 73 78 10.1016/j.catena.2012.06.007
Chen, X., Lu, X. & Yang, G. Sources identification of heavy metals in urban topsoil from inside the Xi’an second ringroad, NW China using multivariate statistical methods. CATENA 98, 73–78 (2012).10.1016/j.catena.2012.06.007
141. Lu X Multivariate statistical analysis of heavy metals in street dust of Baoji, NW China J. Hazard. Mater. 2010 173 744 749 10.1016/j.jhazmat.2009.09.001 19811870
Lu, X. et al. Multivariate statistical analysis of heavy metals in street dust of Baoji, NW China. J. Hazard. Mater. 173, 744–749 (2010).19811870 10.1016/j.jhazmat.2009.09.001
142. Rashid A heavy metal contamination in agricultural soil: Environmental pollutants affecting crop health Agronomy 2023 13 1521 10.3390/agronomy13061521
Rashid, A. et al. heavy metal contamination in agricultural soil: Environmental pollutants affecting crop health. Agronomy 13, 1521 (2023).10.3390/agronomy13061521
143. Weissmannová, H. D., Mihočová, S., Chovanec, P. & Pavlovský, J. Potential ecological risk and human health risk assessment of heavy metal pollution in industrial affected soils by coal mining and metallurgy in Ostrava, Czech Republic. Int. J. Environ. Res. Public Health (2019).
144. Wang Z Hazardous heavy metals accumulation and health risk assessment of different vegetable species in contaminated soils from a typical Mining City, Central China Int. J. Environ. Res. Public Health 2021 18 1 18
Wang, Z. et al. Hazardous heavy metals accumulation and health risk assessment of different vegetable species in contaminated soils from a typical Mining City, Central China. Int. J. Environ. Res. Public Health 18, 1–18 (2021).
145. Jehan S Evaluation of the Swat River, Northern Pakistan, water quality using multivariate statistical techniques and water quality index (WQI) model Environ. Sci. Pollut. Res. 2020 27 38545 38558 10.1007/s11356-020-09688-y
Jehan, S. et al. Evaluation of the Swat River, Northern Pakistan, water quality using multivariate statistical techniques and water quality index (WQI) model. Environ. Sci. Pollut. Res. 27, 38545–38558 (2020).10.1007/s11356-020-09688-y
146. Ali H Khan E Bioaccumulation of Cr, Ni, Cd and Pb in the economically important freshwater fish Schizothorax plagiostomus from three rivers of Malakand division, Pakistan: Risk assessment for human health Bull. Environ. Contam. Toxicol. 2019 102 77 83 10.1007/s00128-018-2500-8 30456654
Ali, H. & Khan, E. Bioaccumulation of Cr, Ni, Cd and Pb in the economically important freshwater fish Schizothorax plagiostomus from three rivers of Malakand division, Pakistan: Risk assessment for human health. Bull. Environ. Contam. Toxicol. 102, 77–83 (2019).30456654 10.1007/s00128-018-2500-8
147. Yüksel B Ustaoğlu F Tokatli C Islam MS Ecotoxicological risk assessment for sediments of Çavuşlu stream in Giresun, Turkey: Association between garbage disposal facility and metallic accumulation Environ. Sci. Pollut. Res. 2022 29 17223 17240 10.1007/s11356-021-17023-2
Yüksel, B., Ustaoğlu, F., Tokatli, C. & Islam, M. S. Ecotoxicological risk assessment for sediments of Çavuşlu stream in Giresun, Turkey: Association between garbage disposal facility and metallic accumulation. Environ. Sci. Pollut. Res. 29, 17223–17240 (2022).10.1007/s11356-021-17023-2
148. Chen X Zhang H Wong CUI Li F Xie S Assessment of heavy metal contamination and ecological risk in soil within the Zheng–Bian–Luo Urban agglomeration Processes 2024 12 996 10.3390/pr12050996
Chen, X., Zhang, H., Wong, C. U. I., Li, F. & Xie, S. Assessment of heavy metal contamination and ecological risk in soil within the Zheng–Bian–Luo Urban agglomeration. Processes 12, 996 (2024).10.3390/pr12050996
149. Din IU Muhammad S Rehman I Heavy metal(loid)s contaminations in soils of Pakistan: A review for the evaluation of human and ecological risks assessment and spatial distribution Environ. Geochem. Health 2023 45 1991 2012 10.1007/s10653-022-01312-x 35759076
Din, I. U., Muhammad, S. & Rehman, I. Heavy metal(loid)s contaminations in soils of Pakistan: A review for the evaluation of human and ecological risks assessment and spatial distribution. Environ. Geochem. Health 45, 1991–2012 (2023).35759076 10.1007/s10653-022-01312-x
150. Diami SM Kusin FM Madzin Z Potential ecological and human health risks of heavy metals in surface soils associated with iron ore mining in Pahang Malaysia. Environ. Sci. Pollut. Res. 2016 23 21086 21097 10.1007/s11356-016-7314-9
Diami, S. M., Kusin, F. M. & Madzin, Z. Potential ecological and human health risks of heavy metals in surface soils associated with iron ore mining in Pahang. Malaysia. Environ. Sci. Pollut. Res. 23, 21086–21097 (2016).10.1007/s11356-016-7314-9
151. Chen H Teng Y Lu S Wang Y Wang J Contamination features and health risk of soil heavy metals in China Sci. Total Environ. 2015 512–513 143 153 10.1016/j.scitotenv.2015.01.025 25617996
Chen, H., Teng, Y., Lu, S., Wang, Y. & Wang, J. Contamination features and health risk of soil heavy metals in China. Sci. Total Environ. 512–513, 143–153 (2015).25617996 10.1016/j.scitotenv.2015.01.025
152. Ajah, K. C., Ademiluyi, J. & Nnaji, C. C. Spatiality, seasonality and ecological risks of heavy metals in the vicinity of a degenerate municipal central dumpsite in Enugu, Nigeria. J. Environ. Heal. Sci. Eng. 13, 0–14 (2015).
153. Wu W Assessment of heavy metal pollution and human health risks in urban soils around an electronics manufacturing facility Sci. Total Environ. 2018 630 53 61 10.1016/j.scitotenv.2018.02.183 29475113
Wu, W. et al. Assessment of heavy metal pollution and human health risks in urban soils around an electronics manufacturing facility. Sci. Total Environ. 630, 53–61 (2018).29475113 10.1016/j.scitotenv.2018.02.183
154. Lin M Ma S Liu J Jiang X Dai D Remediation of arsenic and cadmium co-contaminated soil: A Review Sustainability 2024 16 687 10.3390/su16020687
Lin, M., Ma, S., Liu, J., Jiang, X. & Dai, D. Remediation of arsenic and cadmium co-contaminated soil: A Review. Sustainability 16, 687 (2024).10.3390/su16020687
155. Lu S Wang Y Teng Y Yu X Heavy metal pollution and ecological risk assessment of the paddy soils near a zinc-lead mining area in Hunan Environ. Monit. Assess. 2015 187 627 10.1007/s10661-015-4835-5 26373302
Lu, S., Wang, Y., Teng, Y. & Yu, X. Heavy metal pollution and ecological risk assessment of the paddy soils near a zinc-lead mining area in Hunan. Environ. Monit. Assess. 187, 627 (2015).26373302 10.1007/s10661-015-4835-5
156. Long Z Effect of different industrial activities on soil heavy metal pollution, ecological risk, and health risk Environ. Monit. Assess. 2021 193 20 10.1007/s10661-020-08807-z 33389235
Long, Z. et al. Effect of different industrial activities on soil heavy metal pollution, ecological risk, and health risk. Environ. Monit. Assess. 193, 20 (2021).33389235 10.1007/s10661-020-08807-z
157. Kumar V A review of ecological risk assessment and associated health risks with heavy metals in sediment from India Int. J. Sediment Res. 2020 35 516 526 10.1016/j.ijsrc.2020.03.012
Kumar, V. et al. A review of ecological risk assessment and associated health risks with heavy metals in sediment from India. Int. J. Sediment Res. 35, 516–526 (2020).10.1016/j.ijsrc.2020.03.012
158. Cheng H Concentrations of toxic metals and ecological risk assessment for sediments of major freshwater lakes in China J. Geochem. Explor. 2015 157 15 26 10.1016/j.gexplo.2015.05.010
Cheng, H. et al. Concentrations of toxic metals and ecological risk assessment for sediments of major freshwater lakes in China. J. Geochem. Explor. 157, 15–26 (2015).10.1016/j.gexplo.2015.05.010
159. Ali H Khan E Ilahi I Environmental chemistry and ecotoxicology of hazardous heavy metals: environmental persistence, toxicity, and bioaccumulation J. Chem. 2019 2019 1 14
Ali, H., Khan, E. & Ilahi, I. Environmental chemistry and ecotoxicology of hazardous heavy metals: environmental persistence, toxicity, and bioaccumulation. J. Chem. 2019, 1–14 (2019).
160. Loomis D Huang W Chen G The International Agency for Research on Cancer (IARC) evaluation of the carcinogenicity of outdoor air pollution: focus on China Chin. J. Cancer 2014 33 189 196 10.5732/cjc.014.10028 24694836
Loomis, D., Huang, W. & Chen, G. The International Agency for Research on Cancer (IARC) evaluation of the carcinogenicity of outdoor air pollution: focus on China. Chin. J. Cancer 33, 189–196 (2014).24694836 10.5732/cjc.014.10028
161. Chen H Lu X Li LY Spatial distribution and risk assessment of metals in dust based on samples from nursery and primary schools of Xi’an China. Atmos. Environ. 2014 88 172 182 10.1016/j.atmosenv.2014.01.054
Chen, H., Lu, X. & Li, L. Y. Spatial distribution and risk assessment of metals in dust based on samples from nursery and primary schools of Xi’an. China. Atmos. Environ. 88, 172–182 (2014).10.1016/j.atmosenv.2014.01.054
162. USEPA. Edition of the drinking water standards and health advisories. Environ. Prot. Agency.), Washington (2012).
163. Carpenter DO Bushkin-Bedient S Exposure to Chemicals and Radiation During Childhood and Risk for Cancer Later in Life J. Adolesc. Heal. 2013 52 S21 S29 10.1016/j.jadohealth.2013.01.027
Carpenter, D. O. & Bushkin-Bedient, S. Exposure to Chemicals and Radiation During Childhood and Risk for Cancer Later in Life. J. Adolesc. Heal. 52, S21–S29 (2013).10.1016/j.jadohealth.2013.01.027
164. Yu J Major influencing factors identification and probabilistic health risk assessment of soil potentially toxic elements pollution in coal and metal mines across China: A systematic review Ecotoxicol. Environ. Saf. 2024 274 116231 10.1016/j.ecoenv.2024.116231 38503102
Yu, J. et al. Major influencing factors identification and probabilistic health risk assessment of soil potentially toxic elements pollution in coal and metal mines across China: A systematic review. Ecotoxicol. Environ. Saf. 274, 116231 (2024).38503102 10.1016/j.ecoenv.2024.116231
165. Hao H Li P Lv Y Chen W Ge D Probabilistic health risk assessment for residents exposed to potentially toxic elements near typical mining areas in China Environ. Sci. Pollut. Res. 2022 29 58791 58809 10.1007/s11356-022-20015-5
Hao, H., Li, P., Lv, Y., Chen, W. & Ge, D. Probabilistic health risk assessment for residents exposed to potentially toxic elements near typical mining areas in China. Environ. Sci. Pollut. Res. 29, 58791–58809 (2022).10.1007/s11356-022-20015-5
166. Cao S Health risks from the exposure of children to As, Se, Pb and other heavy metals near the largest coking plant in China Sci. Total Environ. 2014 472 1001 1009 10.1016/j.scitotenv.2013.11.124 24345860
Cao, S. et al. Health risks from the exposure of children to As, Se, Pb and other heavy metals near the largest coking plant in China. Sci. Total Environ. 472, 1001–1009 (2014).24345860 10.1016/j.scitotenv.2013.11.124
167. Genchi G Carocci A Lauria G Sinicropi MS Nickel: Human health and environmental toxicology Int. J. Environ. Res. Public Heal. 2020 17 2 21
Genchi, G., Carocci, A., Lauria, G. & Sinicropi, M. S. Nickel: Human health and environmental toxicology. Int. J. Environ. Res. Public Heal. 17, 2–21 (2020).
168. Rahman MS Assessing risk to human health for heavy metal contamination through street dust in the Southeast Asian Megacity: Dhaka Bangladesh. Sci. Total Environ. 2019 660 1610 1622 10.1016/j.scitotenv.2018.12.425 30743952
Rahman, M. S. et al. Assessing risk to human health for heavy metal contamination through street dust in the Southeast Asian Megacity: Dhaka. Bangladesh. Sci. Total Environ. 660, 1610–1622 (2019).30743952 10.1016/j.scitotenv.2018.12.425
169. Chen K Association of soil arsenic and nickel exposure with cancer mortality rates, a town-scale ecological study in Suzhou China Environ. Sci. Pollut. Res. 2015 22 5395 5404 10.1007/s11356-014-3790-y
Chen, K. et al. Association of soil arsenic and nickel exposure with cancer mortality rates, a town-scale ecological study in Suzhou China. Environ. Sci. Pollut. Res. 22, 5395–5404 (2015).10.1007/s11356-014-3790-y
170. Suwatvitayakorn P Ko MS Kim KW Chanpiwat P Human health risk assessment of cadmium exposure through rice consumption in cadmium-contaminated areas of the Mae Tao sub-district, Tak Thailand Environ. Geochem. Health 2020 42 2331 2344 10.1007/s10653-019-00410-7 31502116
Suwatvitayakorn, P., Ko, M. S., Kim, K. W. & Chanpiwat, P. Human health risk assessment of cadmium exposure through rice consumption in cadmium-contaminated areas of the Mae Tao sub-district, Tak Thailand. Environ. Geochem. Health 42, 2331–2344 (2020).31502116 10.1007/s10653-019-00410-7
171. Kusin FM Azani NNM Hasan SNMS Sulong NA Distribution of heavy metals and metalloid in surface sediments of heavily-mined area for bauxite ore in Pengerang, Malaysia and associated risk assessment Catena 2018 165 454 464 10.1016/j.catena.2018.02.029
Kusin, F. M., Azani, N. N. M., Hasan, S. N. M. S. & Sulong, N. A. Distribution of heavy metals and metalloid in surface sediments of heavily-mined area for bauxite ore in Pengerang, Malaysia and associated risk assessment. Catena 165, 454–464 (2018).10.1016/j.catena.2018.02.029
172. Saha N Industrial metal pollution in water and probabilistic assessment of human health risk J. Environ. Manage. 2017 185 70 78 10.1016/j.jenvman.2016.10.023 28029481
Saha, N. et al. Industrial metal pollution in water and probabilistic assessment of human health risk. J. Environ. Manage. 185, 70–78 (2017).28029481 10.1016/j.jenvman.2016.10.023
173. Cai K Li C Song Z Gao X Wu M Pollution and health risk assessment of carcinogenic elements As, Cd, and Cr in multiple media: A case of a sustainable farming area in China Sustain. 2019 11 1 22
Cai, K., Li, C., Song, Z., Gao, X. & Wu, M. Pollution and health risk assessment of carcinogenic elements As, Cd, and Cr in multiple media: A case of a sustainable farming area in China. Sustain. 11, 1–22 (2019).
174. Muhammad N Assessment of industrial wastewater for potentially toxic elements, human health (dermal) risks, and pollution sources: A case study of Gadoon Amazai industrial estate, Swabi Pakistan. J. Hazard. Mater. 2021 419 126450 10.1016/j.jhazmat.2021.126450 34323708
Muhammad, N. et al. Assessment of industrial wastewater for potentially toxic elements, human health (dermal) risks, and pollution sources: A case study of Gadoon Amazai industrial estate, Swabi. Pakistan. J. Hazard. Mater. 419, 126450 (2021).34323708 10.1016/j.jhazmat.2021.126450
175. Fallahzadeh RA Ghaneian MT Miri M Dashti MM Spatial analysis and health risk assessment of heavy metals concentration in drinking water resources Environ. Sci. Pollut. Res. 2017 24 24790 24802 10.1007/s11356-017-0102-3
Fallahzadeh, R. A., Ghaneian, M. T., Miri, M. & Dashti, M. M. Spatial analysis and health risk assessment of heavy metals concentration in drinking water resources. Environ. Sci. Pollut. Res. 24, 24790–24802 (2017).10.1007/s11356-017-0102-3
176. Buccianti A Lima A Albanese S De Vivo B Measuring the change under compositional data analysis (CoDA): Insight on the dynamics of geochemical systems J. Geochemical Explor. 2018 189 100 108 10.1016/j.gexplo.2017.05.006
Buccianti, A., Lima, A., Albanese, S. & De Vivo, B. Measuring the change under compositional data analysis (CoDA): Insight on the dynamics of geochemical systems. J. Geochemical Explor. 189, 100–108 (2018).10.1016/j.gexplo.2017.05.006
177. Kicińska, A., Pomykała, R. & Izquierdo‐Diaz, M. Changes in soil pH and mobility of heavy metals in contaminated soils. Eur. J. Soil Sci. (2022).
178. Fijałkowski K Kacprzak M Grobelak A Placek A The influence of selected soil parameters on the mobility of heavy metals in soils Inżynieria i Ochr. środowiska 2012 15 81 92
Fijałkowski, K., Kacprzak, M., Grobelak, A. & Placek, A. The influence of selected soil parameters on the mobility of heavy metals in soils. Inżynieria i Ochr. środowiska 15, 81–92 (2012).
179. Zhang Y de Vries W Thomas BW Hao X Shi X Impacts of long-term nitrogen fertilization on acid buffering rates and mechanisms of a slightly calcareous clay soil Geoderma 2017 305 92 99 10.1016/j.geoderma.2017.05.021
Zhang, Y., de Vries, W., Thomas, B. W., Hao, X. & Shi, X. Impacts of long-term nitrogen fertilization on acid buffering rates and mechanisms of a slightly calcareous clay soil. Geoderma 305, 92–99 (2017).10.1016/j.geoderma.2017.05.021
180. Król A Mizerna K Bożym M An assessment of pH-dependent release and mobility of heavy metals from metallurgical slag J. Hazard. Mater. 2020 384 121502 10.1016/j.jhazmat.2019.121502 31732354
Król, A., Mizerna, K. & Bożym, M. An assessment of pH-dependent release and mobility of heavy metals from metallurgical slag. J. Hazard. Mater. 384, 121502 (2020).31732354 10.1016/j.jhazmat.2019.121502
181. Kim HN Park JH Monitoring of soil EC for the prediction of soil nutrient regime under different soil water and organic matter contents Appl. Biol. Chem. 2024 67 1 10.1186/s13765-023-00849-4
Kim, H. N. & Park, J. H. Monitoring of soil EC for the prediction of soil nutrient regime under different soil water and organic matter contents. Appl. Biol. Chem. 67, 1 (2024).10.1186/s13765-023-00849-4
182. Korfali SI Karaki H Speciation of metals in soils and water: Risk assessment Environ. Process. 2018 5 101 125 10.1007/s40710-018-0328-1
Korfali, S. I. & Karaki, H. Speciation of metals in soils and water: Risk assessment. Environ. Process. 5, 101–125 (2018).10.1007/s40710-018-0328-1
183. Marchuk, A. Effect of cations on structural stability of salt-affected soils. (2013).
184. Shrivastav, P. et al. Role of Nutrients in Plant Growth and Development BT - Contaminants in Agriculture: Sources, Impacts and Management. In Naeem, M., Ansari, A. A. & Gill, S. S. (eds.) 43–59 (Springer International Publishing, 2020). 10.1007/978-3-030-41552-5_2.
185. Stavi I Thevs N Priori S Soil salinity and sodicity in drylands: A review of causes, effects, monitoring, and restoration measures Front. Environ. Sci. 2021 9 1 16 10.3389/fenvs.2021.712831
Stavi, I., Thevs, N. & Priori, S. Soil salinity and sodicity in drylands: A review of causes, effects, monitoring, and restoration measures. Front. Environ. Sci. 9, 1–16 (2021).10.3389/fenvs.2021.712831
186. Adjovu GE Stephen H James D Ahmad S Measurement of total dissolved solids and total suspended solids in water systems: A review of the issues, conventional, and remote sensing techniques Remote Sens. 2023 15 3534 10.3390/rs15143534
Adjovu, G. E., Stephen, H., James, D. & Ahmad, S. Measurement of total dissolved solids and total suspended solids in water systems: A review of the issues, conventional, and remote sensing techniques. Remote Sens. 15, 3534 (2023).10.3390/rs15143534
187. Ibrahimi, K., Attia, K. Ben, Amami, R., Américo-Pinheiro, J. H. P. & Sher, F. Assessment of three decades treated wastewater impact on soil quality in semi-arid agroecosystem. J. Saudi Soc. Agric. Sci. 21, 525–535 (2022).
188. Yadav S Irfan M Ahmad A Hayat S Causes of salinity and plant manifestations to salt stress: A review J. Environ. Biol. 2011 32 667 22319886
Yadav, S., Irfan, M., Ahmad, A. & Hayat, S. Causes of salinity and plant manifestations to salt stress: A review. J. Environ. Biol. 32, 667 (2011).22319886
189. Valipour A Hamnabard N Woo K-S Ahn YH Performance of high-rate constructed phytoremediation process with attached growth for domestic wastewater treatment: Effect of high TDS and Cu J. Environ. Manage. 2014 145 1 8 10.1016/j.jenvman.2014.06.009 24981281
Valipour, A., Hamnabard, N., Woo, K.-S. & Ahn, Y. H. Performance of high-rate constructed phytoremediation process with attached growth for domestic wastewater treatment: Effect of high TDS and Cu. J. Environ. Manage. 145, 1–8 (2014).24981281 10.1016/j.jenvman.2014.06.009
190. Kormoker, T. et al. Toxic metals in agricultural soils near the industrial areas of Bangladesh: ecological and human health risk assessment. Toxin Rev. 1–20 (2019).
191. Khorshidi N Parsa M Lentz DR Sobhanverdi J Identification of heavy metal pollution sources and its associated risk assessment in an industrial town using the K-means clustering technique Appl. Geochemistry 2021 135 105113 10.1016/j.apgeochem.2021.105113
Khorshidi, N., Parsa, M., Lentz, D. R. & Sobhanverdi, J. Identification of heavy metal pollution sources and its associated risk assessment in an industrial town using the K-means clustering technique. Appl. Geochemistry 135, 105113 (2021).10.1016/j.apgeochem.2021.105113
192. Wu J Li J Teng Y Chen H Wang Y A partition computing-based positive matrix factorization (PC-PMF) approach for the source apportionment of agricultural soil heavy metal contents and associated health risks J. Hazard. Mater. 2020 388 121766 10.1016/j.jhazmat.2019.121766 31818669
Wu, J., Li, J., Teng, Y., Chen, H. & Wang, Y. A partition computing-based positive matrix factorization (PC-PMF) approach for the source apportionment of agricultural soil heavy metal contents and associated health risks. J. Hazard. Mater. 388, 121766 (2020).31818669 10.1016/j.jhazmat.2019.121766
193. Pan L Ma J Wang X Hou H Heavy metals in soils from a typical county in Shanxi Province, China: Levels, sources and spatial distribution Chemosphere 2016 148 248 254 10.1016/j.chemosphere.2015.12.049 26807946
Pan, L., Ma, J., Wang, X. & Hou, H. Heavy metals in soils from a typical county in Shanxi Province, China: Levels, sources and spatial distribution. Chemosphere 148, 248–254 (2016).26807946 10.1016/j.chemosphere.2015.12.049
194. Petrik A Thiombane M Albanese S Lima A De Vivo B Source patterns of Zn, Pb, Cr and Ni potentially toxic elements (PTEs) through a compositional discrimination analysis: A case study on the Campanian topsoil data Geoderma 2018 331 87 99 10.1016/j.geoderma.2018.06.019
Petrik, A., Thiombane, M., Albanese, S., Lima, A. & De Vivo, B. Source patterns of Zn, Pb, Cr and Ni potentially toxic elements (PTEs) through a compositional discrimination analysis: A case study on the Campanian topsoil data. Geoderma 331, 87–99 (2018).10.1016/j.geoderma.2018.06.019
195. Buccianti A Exploring topsoil geochemistry from the CoDA (compositional data analysis) perspective: The multi-element data archive of the Campania region (Southern Italy) J. Geochemical Explor. 2015 159 302 316 10.1016/j.gexplo.2015.10.006
Buccianti, A. et al. Exploring topsoil geochemistry from the CoDA (compositional data analysis) perspective: The multi-element data archive of the Campania region (Southern Italy). J. Geochemical Explor. 159, 302–316 (2015).10.1016/j.gexplo.2015.10.006
196. Tepanosyan G Sahakyan L Maghakyan N Saghatelyan A Combination of compositional data analysis and machine learning approaches to identify sources and geochemical associations of potentially toxic elements in soil and assess the associated human health risk in a mining city Environ. Pollut. 2020 261 114210 10.1016/j.envpol.2020.114210 32109822
Tepanosyan, G., Sahakyan, L., Maghakyan, N. & Saghatelyan, A. Combination of compositional data analysis and machine learning approaches to identify sources and geochemical associations of potentially toxic elements in soil and assess the associated human health risk in a mining city. Environ. Pollut. 261, 114210 (2020).32109822 10.1016/j.envpol.2020.114210
197. Liao Z Cd, Cu, and Zn accumulations caused by long-term fertilization in greenhouse soils and their potential risk assessment Int. J. Environ. Res. Public Health 2019 16 2805 10.3390/ijerph16152805 31390808
Liao, Z. et al. Cd, Cu, and Zn accumulations caused by long-term fertilization in greenhouse soils and their potential risk assessment. Int. J. Environ. Res. Public Health 16, 2805 (2019).31390808 10.3390/ijerph16152805
198. Kardel F Wuyts K De Wael K Samson R Biomonitoring of atmospheric particulate pollution via chemical composition and magnetic properties of roadside tree leaves Environ. Sci. Pollut. Res. 2018 25 25994 26004 10.1007/s11356-018-2592-z
Kardel, F., Wuyts, K., De Wael, K. & Samson, R. Biomonitoring of atmospheric particulate pollution via chemical composition and magnetic properties of roadside tree leaves. Environ. Sci. Pollut. Res. 25, 25994–26004 (2018).10.1007/s11356-018-2592-z
199. Egozcue JJ Pawlowsky-Glahn V Gloor GB Linear association in compositional data analysis Austrian J. Stat. 2018 47 3 31 10.17713/ajs.v47i1.689
Egozcue, J. J., Pawlowsky-Glahn, V. & Gloor, G. B. Linear association in compositional data analysis. Austrian J. Stat. 47, 3–31 (2018).10.17713/ajs.v47i1.689
200. Dartan G Taşpınar F Toröz İ Assessment of heavy metals in agricultural soils and their source apportionment: A Turkish district survey Environ. Monit. Assess. 2015 187 99 10.1007/s10661-015-4337-5 25663407
Dartan, G., Taşpınar, F. & Toröz, İ. Assessment of heavy metals in agricultural soils and their source apportionment: A Turkish district survey. Environ. Monit. Assess. 187, 99 (2015).25663407 10.1007/s10661-015-4337-5
201. Sarwar T Analysis of Arsenic concentration and its speciation in rice of different markets of Pakistan and its associated health risk Environ. Technol. Innov. 2021 21 101252 10.1016/j.eti.2020.101252
Sarwar, T. et al. Analysis of Arsenic concentration and its speciation in rice of different markets of Pakistan and its associated health risk. Environ. Technol. Innov. 21, 101252 (2021).10.1016/j.eti.2020.101252
