Effects of Cigarette Butt Leachate on the Growth of White Mustard (Sinapis alba L.) and Soil Properties: A Preliminary Study

Abstract

Cigarette butts (CBs) are emerging soil contaminants, releasing chemicals upon contact with moisture. This study examined heavy metal concentrations leached from smoked and unsmoked CBs (Pall Mall, Philip Morris, and Marlboro) into OECD artificial soil and Vertisol soil and their accumulation in white mustard (Sinapis alba L.). Key physiological parameters, including germination rate, plant height, fresh weight, and dry weight, were analyzed, along with the uptake of heavy metals (Al, Fe, Mn, Zn, Ba, Ti, and Cu) and essential elements (Ca, Mg, Na, and K). Results showed that Mn had the highest bioaccumulation index (BAI = 1.10) in OECD soil, while Zn uptake was consistently high across soil types. Soil type significantly influenced plant height (χ² = 41.269, p < 0.01) and elemental composition, with Vertisol soil facilitating greater overall growth and heavy metal uptake than OECD soil. MANOVA revealed no three-way interaction among soil type, CB use, and CB brand on elemental uptake. However, two-way interactions, particularly between soil type and CB use (F (4, 39) = 40.233, p < 0.001, Wilk’Λ = 0.195), showed significant effects on heavy metal uptake. These findings highlight the complex interactions influencing plant contamination, underlining the ecological risks of CB pollution in soils.

1. Introduction

Cigarette butts (CBs) are one of the emerging environmental pollutants [1], with over 6 trillion cigarettes produced each year around the world, resulting in 1.2 million tons of toxic waste dumped into the environment [2]. Most of the harmful substances are trapped in the cigarette filter and are leached once in contact with moisture [3]. These harmful substances include nicotine, heavy metals, and polycyclic aromatic hydrocarbons (PAHs), which can significantly impair seed germination and early plant growth [4,5,6,7,8], including the soil bacterial community [7,9]. The effect of CBs on plant growth was reported by Jakimiuk et al. [6], who examined the effects of CBs on seed germination using Sinapis alba L. (white mustard) and Hordeum vulgare L. (common barley) as model plants. The researchers aimed to understand how different concentrations of CB leachates impacted germination rates and root growth. The results showed that high concentrations of CBs in water solutions had detrimental effects on both seed germination and radical growth. Specifically, the study highlighted that even small amounts of leachate could significantly reduce the germination index and root length, reinforcing the notion that CBs pose a toxic threat to plant life. In a recent study by Mohamed [7], the author investigated the effects of smoked CB extracts on soil chemical properties and local plant growth species, including Moringa oleifera, Dodonaea viscosa, Melilotus officinalis, and Chrysanthemum indicum. The study aimed to assess how different concentrations of CB extracts (0%, 30%, 60%, and 90%) affected soil pH, electrical conductivity (EC), and sodium (Na) concentration. The results indicated that the application of CBs significantly increased soil pH from a baseline value to higher alkalinity levels, with EC rising from 2.00 to 6.4 dS·m⁻¹ at the highest concentration. Increased Na levels were also observed, highlighting the potential for CBs to degrade soil quality through chemical alterations. Additionally, the authors reported significant impediments to germination as the concentration of butt extracts increased. Specifically, higher concentrations led to reduced germination rates and inhibited bacterial communities in the soil. Their effect on the bacterial community was also reported by Koroleva et al. [9], who explored the effects of biodegradable and non-biodegradable CBs on bacterial community structures in soil using automated ribosomal intergenic spacer analysis (ARISA) to assess changes in bacterial diversity before and after adding CB leachate treatments. The findings revealed that biodegradable CBs significantly altered bacterial community composition due to higher concentrations of toxic metals leached into the soil compared to non-biodegradable CBs. These studies emphasize how CB pollution can disrupt microbial ecosystems, which are essential for maintaining soil health. Also, further investigations into the long-term effects of CB pollution have highlighted the persistence of toxic chemicals in soils. For instance, a five-year decomposition study by Bonanomi et al. [10] assessed how CBs decay and their chemical changes over time, revealing that these pollutants can continue to release harmful substances into the environment long after disposal. For example, research indicates that metals like Ba, Fe, Mn, and Sr leach into water over extended periods, suggesting that cigarette litter acts as a persistent point source of contamination. In a landfill context, the introduction of freshly smoked CBs was shown to increase heavy metal concentrations in leachate significantly, with Cd and Pb among the most concerning pollutants [11]. Additionally, a study investigating urban environments found that heavy metal leakage from littered CBs varied significantly based on climatic conditions, with rainy periods leading to markedly higher rates of metal release [12]. The buildup of heavy metals in soils is a significant problem in agricultural production as it has negative impacts on food safety, marketability, crop growth due to phytotoxicity, and the overall environmental health of soil organisms [13,14]. Koroleva et al. [9] reported the presence of various heavy metals in both biodegradable and non-biodegradable CBs and concluded that biodegradable CBs significantly altered bacterial community composition due to higher concentrations of toxic metals leached into the soil compared to non-biodegradable ones. The primary concern of heavy metals over other pollutants is that they are persistent for a half-life exceeding 20 years and cannot be destroyed through the biodegradation process [15].

The current study was conducted in Hungary, in which the largest market segment is cigarettes, estimated to have a market volume of EUR 1754.0 million in 2024 [16]. Also, the filter cigarettes segment held the highest share of the Hungarian cigarette market, in both value and volume terms [17]. Kaul [18] observed the harmful consequences of littering CBs in Budapest, Hungary, and published a paper titled “How to reduce the harmful impact of cigarettes on the environment” with the objectives of assessing the environmental impacts, mitigation strategies, and raising public awareness and education regarding the environmental hazards associated with the improper disposal of CBs. This was a review paper, and an experiment conducted in the present study employed an experimental methodology to examine the ecotoxicological effects of CB leachates on soil properties and plant growth. Leachates from both smoked and unsmoked CBs of three major cigarette brands were prepared and applied to two soil types: OECD artificial soil and Vertisol soil. White mustard (Sinapis alba L.), a standard test crop in ecotoxicological studies, was grown under controlled conditions to investigate the uptake of heavy metals, physiological impacts, and soil alterations. Comprehensive statistical analyses, including Kruskal–Wallis tests, principal component analysis (PCA), and MANOVA, were conducted to investigate the effects of soil type, CB use, and CB brand on plant performance and heavy metal bioaccumulation. This multifaceted approach enabled a robust evaluation of CB contamination’s implications for soil health and plant growth dynamics. The scientific contribution of this study lies in its pioneering examination of CB pollution within an agricultural and ecotoxicological framework, specifically in the context of Hungary. While previous studies have focused predominantly on urban or aquatic environments, this research bridges a critical gap by assessing CB leachate impacts on terrestrial ecosystems. The findings provide novel insights into the persistent nature of CB contaminants in soils and their capacity to impair crop development and alter soil chemical properties.

2. Materials and Methods

2.1. Sources of Materials

The research was carried out in a laboratory belonging to the Hungarian University of Agriculture and Life Sciences, located in Gödöllő Campus, Pest County, Hungary (latitude: 47.5937° or 47°35′37.4″ north; longitude: 19.3652° or 19°21′54.6″ east) in 2022. Three popular cigarette brands, Pall Mall, Phillip Morris, and Marlboro (i.e., no light or flavored cigarettes), were purchased brand new from a convenience store and kept sealed in their original packaging. Philip Morris International was the leading company in the cigarettes category [17]. In another study by Statista Markets Insights [16], Marlboro and Pall Mall were among the top three most popular brands in Hungary by 2022. Also, discarded smoked CBs were collected from covered cigarette receptacles adjacent to buildings on the Gödöllő Campus of the Hungarian University of Agriculture and Life Sciences in Pest County, Hungary. Collecting CBs necessitated manual sorting, and those with at least 1 cm of tobacco residual were placed in disposable containers for later use. It should be noted that CBs were not collected after local precipitation events to reduce analyte loss before sampling. The white mustard (Sinapsis alba L.) seeds were purchased in Hungary.

2.2. Leaching Experiment Procedure

The toxicity of cigarette butts (CBs) was determined by preparing leachates from smoked and unsmoked CBs. The leachates were prepared by placing five CBs of each cigarette type and brand in separate bottles, each containing 1 L of distilled water, following the suggestion from Slaughter et al. [19] and Wright et al. [20], who recommended a concentration of between 4 and 400 CBs·L⁻¹ for studies on biological effects and chemical release of CBs. Distilled water was used as a control treatment. It is worth noting that metal leaching occurs within one day [21,22]. The bottles were placed on a 360° rotating shaker for 7 days at room temperature, and the leachates were then filtered to remove particulate matter before use in toxicity tests.

2.3. Test Soil

A portion of uncontaminated Vertisol soil was collected at a depth of 0–15 cm from the topsoil layer at Atkár village in Heves county, Gyöngyös district, located in the northern part of Hungary (47°42′24.5″ N 19°54′35.6″ E), in 2022. Soil samples were collected using a spade, placed in containers, and transported to the laboratory, where plant material and stones were removed before the soil was ground with a mechanical shaker. Soil samples were passed through a 2 mm sieve before use. The physicochemical properties of the soil are listed in

Table 1. Physicochemical properties of Vertisol soil (elements in mg·kg⁻¹ per dry weight).

Elements	Vertisol Soil
pH (H2O)	6.88
pH (KCl)	6.30
EC (µS·cm−1)	95.63
N (%)	0.18
C (%)	1.83
C: N	10.34
OM (%)	4.90
CEC (cmol+·kg−1)	40.1
Sand (%)	3.00
Silt (%)	45.05
Clay (%)	51.95
Textural class	Silty clay
Plasticity index (ml)	43.4
P2O5	23.75
Ca	4509.08
Mg	2218.24
K	1747.49
Na	84.54
Al	9492
Co	7.83
Cr	11.22
Cu	23.17
Fe	15,342
Mn	570.07
Ni	18.87
Pb	10.71
Zn	32.04
Ba	77.44
Ti	134.66
V	17.73

. Briefly, the soil had a grain size distribution of 3% sand (0.05–2.0 mm), 45.05% silt (0.002–0.05 mm), and 51.95% clay (<0.002 mm). The soil was characterized by a pH value of 6.88, an electric conductivity (EC) of 95.63 µS·cm⁻¹, organic matter of 4.90%, CEC 40.1 cmol₊·kg⁻¹, and P 23.75 mg·kg⁻¹. Since soil components such as mineral clay and organic matter content can influence the bioavailability and therefore the toxicity of toxicants to soil organisms, an additional reference soil was used, namely OECD soil. The OECD artificial soil is a widely used substrate in soil toxicity tests. It is indicated as a medium for ecotoxicological assessments and serves as a “reference soil” in the evaluation of complicated solid samples, such as wastes or contaminated soils [23]. The OECD soil was chosen for this investigation because of its low metal background, lack of inhomogeneity, aggregates, and other variables that influence metal fate in soil (e.g., Mn and Fe hydroxides) [24]. It is worth noting that the OECD recommends artificial soils as they are convenient for use in the laboratory with no problem in estimating the toxicity of pollutants in general [25].

The OECD soil used in the present study consisted of 10% sphagnum peat, 20% kaolin clay, and 70% air-dried quartz sand [26], and had a pH of 6.48.

2.4. Terrestrial Plant Test

A pot experiment was conducted under laboratory conditions at the Hungarian University of Agriculture and Life Sciences. The study was designed as a trifactorial experiment including (1) soil type, i.e., OECD soil and Vertisol soil; (2) cigarette brand, i.e., Pall Mall (1), Philip Morris (2), and Marlboro (3); (3) CB use, i.e., used (SCB) and unused CB (USCB). Hence, a total of 14 treatments, including the control treatments, each with four replicates.

• OECD soil alone
• Vertisol soil alone
• OECD soil + SCB₁
• OECD soil + SCB₂
• OECD soil + SCB₃
• OECD soil + USCB₁
• OECD soil + USCB₂
• OECD soil + USCB₃
• Vertisol soil + SCB₁
• Vertisol soil + SCB₂
• Vertisol soil + SCB₃
• Vertisol soil + USCB₁
• Vertisol soil + USCB₂
• Vertisol soil + USCB₃

Toxicity test methods followed OECD guidelines 208 “Terrestrial plants, growth test” [27]. Plastic pots (height of 8.5 cm, inner diameter of 11 cm) were filled with 500 g of air-dry soil. The CB leachate was poured into the soil at a concentration of 100 mL per 500 g dry weight, corresponding to 60% water holding capacity, which was determined using the plasticity index as described by Arany (K_A): MSZ 08-0205:1978 [28]. The leachate was thoroughly mixed with the soil in the glass beaker after being transferred to plastic pots, and the mixture was allowed to stabilize for two days before planting. Fifteen undamaged seeds of white mustard (Sinapsis alba L.) were planted and grown until the early flowering stage. The selection of white mustard was based on its exceptional capacity to accumulate metal ions and its ability to thrive in severely polluted soil [29]. Furthermore, white mustard is implemented in standardized soil ecotoxicity assessments in Hungary (MSZ 21 976–17:1993 Hungarian Standard). To provide a photoperiod of 12/12 day/night, cultivation lamps (MARS HYDRO MH-150MA-41B, LG LED Solutions Ltd., Shenzhen, Guangdong Province, China) were used.

2.5. Plant Physiological Parameters and Analysis of Heavy Metals in Plant Tissue

The plants from the present study were harvested 31 days after the seeds germinated. The germination percentage was recorded after week 1 and week 4 of planting, while the plant height was recorded at week 1, week 2, week 3, and week 4 after planting, at which point all replicates were re-randomized for the position they occupied in the laboratory. The plant height was recorded and measured from the base of the plant to the tip of the leaf and was expressed in centimeters. At the end of the experiment, seedlings were weighed for the determination of fresh weight. After air drying for 2 weeks, the dry weight was determined. Due to the small size of the stem, we considered the leaf’s dry weight to be representative of the shoot’s dry weight, encompassing both the stem and the leaf. For the effect estimations, the mean weight of seedlings in each replicate was used. After harvesting, the plants and the soil samples were analyzed for Ca, Mg, Na, K, Al, Cu, Fe, Mn, Zn, Ba, and Ti according to the MSZ-21470-50:2006 [30] standard after digestion with a mixture of nitric acid (HNO₃) and hydrogen peroxide (H₂O₂) (i.e., 5 mL of concentrated reagent-grade HNO₃ (65%) and 2 mL of H₂O₂ (35%)). The extracts were filtered and analyzed using a Horiba Jobin Yvon ACTIVA-M inductively coupled plasma optical emission spectrometer (ICP-OES) after acid digestion (HNO₃) (MSZ-21470-50:2006) [30]. Instrument calibration was conducted before analyzing each batch of samples.

2.6. Heavy Metal Indexes

Plant uptake of trace metals from mixtures was expressed as bioaccumulation index (BAI), heavy metal uptake index (UI), and tolerance index (TI). The bioaccumulation index was calculated as the ratio of heavy metal concentrations in white mustard tissues and heavy metal concentrations in soil [31]. Based on the total contents of elements in the soil and white mustard, the bioaccumulation index (BAI) was calculated according to Equation (1) below:

$$\mathrm{B}\mathrm{A}\mathrm{I}={\displaystyle \frac{\mathrm{G}\mathrm{C}\mathrm{x}}{\mathrm{S}\mathrm{C}\mathrm{x}}}$$

(1)

where GC is the total concentration in white mustard (in mg·kg⁻¹ of dry weight), and SC total concentration in the soil (in mg·kg⁻¹ of dry weight). The heavy metal uptake index (UI) was calculated by Equation (2).

$$\mathrm{U}\mathrm{I} ({\displaystyle \frac{\mathrm{m}\mathrm{g}}{\mathrm{p}\mathrm{o}\mathrm{t}}})={\displaystyle \frac{\mathrm{S}\mathrm{h}\mathrm{o}\mathrm{o}\mathrm{t}\mathrm{s} \mathrm{m}\mathrm{e}\mathrm{t}\mathrm{a}\mathrm{l} \mathrm{c}\mathrm{o}\mathrm{n}\mathrm{c}\mathrm{e}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n} (\frac{\mathrm{m}\mathrm{g}}{\mathrm{k}\mathrm{g}})}{\mathrm{S}\mathrm{h}\mathrm{o}\mathrm{o}\mathrm{t} \mathrm{d}\mathrm{r}\mathrm{y} \mathrm{w}\mathrm{e}\mathrm{i}\mathrm{g}\mathrm{h}\mathrm{t} (\frac{\mathrm{k}\mathrm{g}}{\mathrm{p}\mathrm{o}\mathrm{t}})}}$$

(2)

where UI (mg·pot⁻¹) is equal to the shoot metal concentration (in mg·kg⁻¹) divided by the shoot dry weight (in kg·pot⁻¹). The tolerance index (TI) was calculated as the mean dry weight (dw) of a plant grown in heavy metal-contaminated soil divided by the mean dry weight of the control plants [32] as seen in Equation (3).

$$\mathrm{T}\mathrm{I}={\displaystyle \frac{\mathrm{B}\mathrm{i}\mathrm{o}\mathrm{m}\mathrm{a}\mathrm{s}\mathrm{s} \mathrm{o}\mathrm{f} \mathrm{t}\mathrm{h}\mathrm{e} \mathrm{t}\mathrm{r}\mathrm{e}\mathrm{a}\mathrm{t}\mathrm{e}\mathrm{d} \mathrm{p}\mathrm{l}\mathrm{a}\mathrm{n}\mathrm{t}\left(\mathrm{g}·\mathrm{d}\mathrm{w}\right)}{\mathrm{B}\mathrm{i}\mathrm{o}\mathrm{m}\mathrm{a}\mathrm{s}\mathrm{s} \mathrm{o}\mathrm{f} \mathrm{t}\mathrm{h}\mathrm{e} \mathrm{c}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l} \mathrm{p}\mathrm{l}\mathrm{a}\mathrm{n}\mathrm{t}\left(\mathrm{g}·\mathrm{d}\mathrm{w}\right)}}$$

(3)

where TI was calculated on the dry weight basis of white mustard at 4 weeks after planting.

2.7. Soil Analysis

The guidelines of the Hungarian Standards were used for soil analysis and sample preparation. The soil pH was determined in water (H₂O) and KCl solution by the potentiometric method (glass electrode). The pH and electrical conductivity (EC) were measured in soil–water suspensions (1:2.5, w/v) after standing for 12 h, using a digital pH meter (Radelkis OP-211/2) and an electrical conductivity meter (Jenway 4510), respectively (MSZ-08-0206-2:1978) [33]. Soil organic matter (SOM) was determined by the Walkler–Black wet oxidation method [34]. Briefly, 1.0 g of air-dried soil was placed into a 500 mL Erlenmeyer flask. To the soil, 10 mL of 0.167 M potassium dichromate (K₂Cr₂O₇) was added and gently swirled to disperse the sample. Next, 20 mL of concentrated sulfuric acid (H₂SO₄) was carefully poured into the mixture, and the flask was stirred for a minute in a fume hood. The flask was left to stand on an insulated surface in a fume hood for 30 min to minimize heat loss. Afterwards, 200 mL of distilled water was added to the flask. For the titration, 10 mL of 85% phosphoric acid was added, followed by three to four drops of the barium diphenylamine sulfonate indicator. The solution was then titrated with 0.5 M ferrous sulfate (FeSO₄) to determine the remaining unreacted dichromate. Thus, we obtain the following formula:

$$\mathrm{\%} \mathrm{c}\mathrm{a}\mathrm{r}\mathrm{b}\mathrm{o}\mathrm{n}={\displaystyle \frac{\left(\mathrm{V}\mathrm{b}\mathrm{l}\mathrm{a}\mathrm{n}\mathrm{k}-\mathrm{V}\mathrm{s}\mathrm{a}\mathrm{m}\mathrm{p}\mathrm{l}\mathrm{e}\right)\times {\mathrm{M}}_{\mathrm{F}\mathrm{e}2+}\times 0.003\times 100\times \mathrm{f}\times \mathrm{m}\mathrm{c}\mathrm{f}}{\mathrm{W}}}$$

(4)

where V_blank is equal to the volume of titrant in the blank (in mL), V_sample is the volume of titrant in the sample (in mL), M_Fe2+ is the concentration of standardized FeSO₄ solution (molarity), f is the correction factor, 1.3, W is the weight of the soil (in grams), and mcf is the moisture correction factor. The total organic carbon (%) results were subsequently converted to soil organic matter (%) using a conversion factor of 1.72.

The CaCO₃ content was given in percentage figures and was determined via Scheibler-type calorimetry per the MSZ-08-0206-2 (1978) standard [33]. Contents of Al, Cu, Fe, Mn, Zn, Ba, Ti, Ca, Mg, K, and Na were determined by the Horiba Jobin Yvon ACTIVA-M inductively coupled plasma optical emission spectrometer (ICP-OES) after acid digestion (HNO₃) (MSZ-21470-50:2006) [30]. The optimized operational parameters were incident power 1200 W; plasma argon gas flow 16.04 L/min; sheath argon gas flow 0.288 L/min; nebulizer pressure 2.89 bar; nebulizer sample uptake 0.83 mL/min; auxiliary gas flow 0.589 L/min; argon moistening; cyclon type spray chamber and Meinhard nebulizer; internal standard: Yttrium (1 mg/L). Calibration was performed with the MERCK CertiPUR 1.11355.0100 ICP multi-element standard solutions diluted with extractant solvents. The total N and C were determined in the C, N, and S elemental analyzer (Vario MAX cube, Elemental Analysenysteme GmbH, Langenselbold, Germany). Briefly, the samples were placed in a ceramic crucible (200 mg ± 50 mg) designed for this purpose and burned in a closed system in a high-purity helium–oxygen atmosphere at 1140 °C. The instrument then determined the percentage of each element per mass measured. The instrument was calibrated with sulfadiazine before and after the measurement.

2.8. Statistical Analysis

SPSS version 29.0.1.0 (171) statistical software and R software (R 4.1.3) were used to analyze and process the data. The significant effect of soil type, CB use, and CB brand on white mustard growth and elemental uptake was detected using the non-parametric Kruskal–Walli’s (H) test. To assess the interactive effects of soil type, CB use, and CB brand on the physiological parameters and elemental composition of white mustard plants, a multivariate analysis of variance (MANOVA) was initially planned. However, due to the violation of certain assumptions required for MANOVA, principal component analysis (PCA) was first conducted to reduce the dimensionality of the dataset, retaining components with eigenvalues > 1, based on Kaiser’s recommendation [35]. This step allowed for a more robust MANOVA to be subsequently performed. Principal component analysis (PCA) is a dimensionality-reduction technique that effectively condenses a large set of variables into a smaller set while retaining most of the information from the original dataset [36]. The resulting latent variables, known as principal components, are linear combinations of the original variables, which are compressed into these new components without significant loss of information [36]. The normality of the PCA components was verified using the Shapiro–Wilk test and Levene’s test for homoscedasticity. Boxplots were used to check outliers. In the case of finding an outlier, that value was removed from the dataset using Microsoft Excel. Thereafter, a MANOVA was performed to assess the impact of the independent variables (i.e., soil type, CB use, and CB brand) and their interactions on the PCA components. Assumptions of MANOVA were checked, including univariate and multivariate normality using the Shapiro–Wilk test, linearity (scatterplot matrix), homogeneity of variance using Levene’s test, and Mahalanobis distance to detect multivariate outliers. The MANOVA analysis was also chosen to minimize the risk of Type 1 errors when making multiple comparisons. Post hoc multiple comparisons were conducted using Tukey’s test (p ≤ 0.05), due to an equal number of cases for each level, i.e., for CB brand and CB use, excluding soil type, which had only 2 levels. Despite the inability to compute Box’s test of equality of covariance matrices due to group sizes, the analysis proceeded under the assumption that the group covariance matrices were approximately equal. Pearson correlation analysis was performed between plant physiological parameters and elemental uptake.

3. Results

3.1. Physiological Observations

From the beginning to the last stage of the investigation, the germination rate decreased in all treatment combinations. The treatment with Vertisol soil alone resulted in a considerable decrease, followed by the treatment with OECD soil treated with Pall Mall CB leachate (SCB₁ + OECD) (

Table 2. Effect of CB leachate on germination, plant height, fresh weight, and plant dry weight (mean ± SD n = 4).

OECD SOIL
	Germination (%)		Plant Height (cm)		Fresh Weight (g)		Dry Weight (g)
CB use	Used	Unused	Used	Unused	Used	Unused	Used	Unused
Soil alone	55.00 ± 17.53		5.69 ± 1.37		1.01 ± 0.37		0.27 ± 0.09
CB brand
Pall Mall (1)	60.00 ± 13.33	70.00 ± 15.87	9.88 ± 2.32	9.78 ± 1.87	2.13 ± 0.23	1.92 ± 0.18	0.40 ± 0.06	0.41 ± 0.07
Philip Morris (2)	78.33 ± 18.36	73.33 ± 14.40	9.48 ± 3.11	7.56 ± 0.83	1.92 ± 0.71	1.63 ± 0.58	0.38 ± 0.14	0.37 ± 0.09
Marlboro (3)	65.00 ± 11.39	65.00 ± 17.53	8.40 ± 1.24	9.32 ± 4.62	1.63 ± 0.49	1.65 ± 0.45	0.38 ± 0.10	0.36 ± 0.06
VERTISOL SOIL
Soil alone	68.33 ± 17.53		23.43 ± 3.97		7.13 ± 1.23		1.50 ± 0.37
CB brand
Pall Mall (1)	90.00 ± 3.85	76.67 ± 8.61	22.90 ± 4.11	23.45 ± 5.24	8.04 ± 1.33	8.09 ± 1.15	1.68 ± 0.18	1.65 ± 0.20
Philip Morris (2)	81.67 ± 16.67	81.67 ± 6.38	21.63 ± 1.67	23.31 ± 3.08	7.96 ± 0.90	7.94 ± 0.41	1.58 ± 0.29	1.58 ± 0.22
Marlboro (3)	81.67 ± 14.78	85.00 ± 11.39	21.80 ± 3.11	24.01 ± 4.88	8.01 ± 0.92	8.50 ± 1.17	1.57 ± 0.17	1.78 ± 0.20

). Following these treatments, a decline of greater than 15% was observed in the treatment with used Marlboro cigarette leachate applied to a Vertisol (SCB₃) and in the treatment with OECD soil applied with unused Marlboro CB leachate (USCB₃), respectively. Regarding the OECD treatment alone, a 15% decrease was observed, with the smallest decrease occurring in the OECD soil treatment administered with used CB from the Philip Morris brand of cigarettes.

A Kruskal–Walli’s (H) test showed that the soil type affected mustard physiological parameters (p < 0.01), with results showing significant differences in white mustard germination rate (df = 1, χ² = 11.047, p < 0.01), plant height (df = 1, χ² = 41.269, p < 0.01), fresh weight (df = 1, χ² = 41.269, p < 0.01), and dry weight (df = 1, χ² = 41.310, p < 0.01) (

Table 3. Kruskal–Walli’s test of physiological parameters of white mustard grown in CB-contaminated soils.

Factors	Germination	Plant Height	Fresh Weight	Dry Weight
Soil type	11.047 a	41.269 a	41.269 a	41.310 a
CB use	4.637	0.386	1.854	1.103
CB brand	5.482	0.652	2.003	1.310

Note: The results are expressed as Chi-square value (χ²). ^a p < 0.001.

). There were no significant differences related to the effect of CB use and CB brand on white mustard growth. For instance, the Kruskal–Walli’s test showed no effect of CB use on white mustard with no significant differences for germination rate (df = 2, χ^{2 =} 4.637, p < 0.01), plant height (df = 2, χ² = 0.386, p = 0.098), fresh weight (df = 2, χ² = 1.854, p = 0.825), and dry weight (df = 2, χ² = 1.103, p = 0.576), respectively.

3.2. Elemental Composition of White Mustard Plant

The Kruskal–Walli’s test was used to test the differences in plant growth parameters, heavy metal, and Ca, Mg, Na, and K concentrations of the white mustard plant between soil type, CB use, and CB brand. The Kruskal–Walli’s (H) test results showed significant differences (p < 0.01) in the accumulation of Mn (χ² = 41.053, p < 0.001), Zn (χ² = 37.762, p < 0.001), Ti (χ² = 13.594, p < 0.001), by white mustard between OECD soil and Vertisol soil. Also, CB use showed a significant effect on white mustard uptake of Cu (χ² =20.805, p < 0.001), Fe (χ² = 15.143, p < 0.001), Ba (χ² = 24.134, p < 0.001), and Ti (χ² = 9.496, p = 0.009), while there was no significant difference (p < 0.05) observed for the accumulation of heavy metals between the three CB brands. The highest Mn content was observed in white mustard growth in OECD soil contaminated with unused CB (USCB₁), with an Mn content of 69.95 mg·kg⁻¹, followed by the treatment with used CB (SCB₃) with a content of 50.52 mg·kg⁻¹ (Figure 1). Moreover, the highest Mn content in Vertisol soil treatments was observed in the treatments with unused CB (USCB₁), with a Mn content of 21.178 mg·kg⁻¹, followed by the treatment with unused CB (USCB₃), with a Mn content of 16.278 mg·kg⁻¹. The highest Zn content of 62.623 mg·kg⁻¹ was observed in an OECD soil treatment with used CB (SCB₁), followed by the OECD soil applied with unused CB (USCB₁), with a Zn content of 45.922 mg·kg⁻¹. Moreover, the highest Fe content was observed in the Vertisol treatment with unused CB (USCB₁), with a Fe content of 468.172 mg·kg⁻¹, followed by an OECD soil treatment with unused CB, with an Fe content of 417.185 mg·kg⁻¹.

The Kruskal–Walli’s test results for Ca, Mg, K, and Na showed a significant difference (p < 0.01) in the uptake of Ca (χ² = 20.455, p < 0.001), Mg (χ² = 16.118, p < 0.001), and K (χ² = 39.185, p < 0.001) (

Table 4. Kruskal–Walli’s (H) test of the elemental content of white mustard grown in CB-contaminated soils.

Factors	Al	Cu	Fe	Mn	Zn	Ba	Ti	Ca	Mg	K	Na
Soil type	3.613	4.331 c	6.452 c	41.053 a	37.762 a	1.170	13.594 a	20.455 a	16.118 a	39.185 a	3.997 c
CB use	10.372 b	20.805 a	15.143 a	3.132	0.496	24.134 a	9.496b	0.858	1.803	1.260	0.522
CB brand	6.736	10.056c	5.317	2.106	4.671	5.776	3.299	1.987	1.120	1.232	0.582

Note: The results are expressed as Chi-square value (χ²). ^a p < 0.001, ^b p < 0.01, ^c p < 0.05.

). Moreover, the results of Ca, Mg, K, and Na showed no significant differences (p < 0.05) between the CB use and the CB brand used in the present study. The highest concentrations of Ca and Na were detected in OECD soil treatments, as compared to Vertisol soil treatments (Figure 2). Notably, the highest Mg content in white mustard was observed in Vertisol soil treatments, in contrast to OECD soil treatments, following the order soil alone ˃ USCB₂ ˃ SCB₁ ˃ USCB₃ ˃ USCB₁ ˃ SCB₂ ˃ SCB₃.

3.3. Correlation Between Plant Biological and Chemical Properties

The heat map presented in Figure 3 provides insights into the relationships among various variables measured in the study, which included plant physiological parameters (i.e., germination, plant height, fresh weight, and dry weight) and elemental concentrations (i.e., Al, Fe, Mn, Zn, Ba, Ti, Cu, Ca, Mg, K, and Na) in white mustard plants. The heatmap uses a colour gradient, with red indicating a positive correlation and blue indicating a negative correlation, while the colour’s intensity represents the correlation’s strength. The results showed that a very strong positive correlation exists between plant physiological parameters, particularly between fresh weight and dry weight (r = 0.983, p < 0.01), as indicated by the deep red colour. Furthermore, a very strong positive correlation was identified between plant height and fresh weight (r = 0.937, p < 0.01). In contrast, a weaker positive correlation was observed between plant germination and plant height (r = 0.335, p < 0.05), with germination showing a moderate positive correlation with fresh weight (r = 0.478, p < 0.01), and dry weight (r = 0.512, p < 0.01).

The correlation analysis between plant growth parameters and the nutrients Ca, Mg, Na, and K revealed several significant relationships. Notably, Mg showed a moderate positive correlation with plant height (r = 0.461, p < 0.01), fresh weight (r = 0.439, p < 0.01), and dry weight (r = 0.432, p < 0.01), and a weak positive correlation with germination (r = 0.268, p < 0.05). Additionally, K exhibited a weak positive correlation with plant height (r = 0.275, p < 0.05). In contrast, Ca displayed a moderate negative correlation with plant height (r = −0.546, p < 0.01), fresh weight (r = −0.596, p < 0.01), and a strong negative correlation with dry weight (r = −0.617, p < 0.01), while Na showed a very strong negative correlation with plant height (r = −0.837, p < 0.01), fresh weight (r = −0.832, p < 0.01), and dry weight (r = −0.803, p < 0.01), respectively.

Conversely, several elements, such as Mn and Ti, demonstrated negative correlations with plant physiological parameters, particularly plant height, fresh weight, and dry weight. For example, a strong negative correlation was found between Mn and plant height (r = −0.735, p < 0.01), along with a weak negative correlation with plant germination (r = −0.328, p < 0.05). Additionally, Fe exhibited a weak negative correlation with plant height (r = −0.369, p < 0.01), fresh weight and dry weight (r = −0.336, p < 0.05). Moreover, a weak negative correlation was observed between Ba and all the plant growth parameters measured in this study (Figure 3).

3.3.1. Bioaccumulation Index

The results of Figure 4 present the bioaccumulation index (BAI) of various heavy metals (Al, Cu, Fe, Mn, Zn, Ba, Ti) in white mustard grown under different treatments involving soil type and CB leachates (Figure 4). The BAI values indicate the extent to which these metals have accumulated in the plant tissues relative to their concentrations in the soil. BAI was calculated by dividing the total content of heavy metals in white mustard by the total content in the soil. A higher BAI means a greater accumulation of metals in white mustard. In OECD soil samples, a BAI of greater than 1 was observed in all the treatment combinations for Cu, Zn, and Ba, with a BAI of less than 0.5 in all the treatment combinations for Al, and Fe. Notably, the BAI for Zn shows relatively high values across all treatments, with the highest recorded in OECD soil contaminated with used CB (SCB₁) treatment (6.38), with the lowest BAI in the control treatment without CB application. In all the OECD soil treatments, the BAI followed the order Zn (3.31–6.38) ˃ Cu (1.64–2.26) ˃ Ba (1.10–1.69) ˃ Mn (0.56–1.10) ˃ Ti (0.34–0.67) ˃ Al (0.05–0.15) ˃ Fe (0.04–0.09). Conversely, the BAI for Fe and Al remains relatively low across all treatments, with the highest values observed in the USCB₃ treatment for Fe (0.09) and Al (0.14). Additionally, the BAI values for Mn and Ba exhibit variability across treatments, with the highest Mn accumulation observed in the USCB₁ treatment (1.10) and the highest Ba accumulation in the OECD soil alone with a BAI value of 1.69. In Vertisol soil treatments, the BAI was lower than 0.5 in most cases, except for Zn, which showed a Zn content of between 0.50 and 0.70, respectively. The Kruskal–Walli’s test results (Table 4) reveal that soil type significantly influenced the bioaccumulation of Ba (χ² = 9.931, p < 0.01) and Ti (χ² = 8.728, p < 0.01). However, for other metals like Al, Cu, Fe, Mn, and Zn, no significant differences in bioaccumulation were observed based on soil type. The use of CBs as a contaminant in the soil did not lead to significant differences in the bioaccumulation index for most of the metals analyzed, except for Cu (χ² = 3.336) and Ba (χ² = 5.597). Similarly, the CB brand did not significantly affect the bioaccumulation of any of the tested metals, with only minor variations observed across brands.

3.3.2. Heavy Metal Uptake Index

Figure 5 illustrates the variability in heavy metal uptake by white mustard plants across different treatments. In Vertisol soil, Ti and Ba showed the highest uptake, particularly in treatments involving SCB₃ and USCB₃, with Ti peaking at approximately 0.8 mg·pot⁻¹ and Ba reaching similar levels. In contrast, the uptake of other metals such as Al, Cu, Fe, Mn, and Zn remained relatively low, indicating selective accumulation of specific metals. In OECD soil, the overall UI values were lower, with Ti and Ba again showing higher uptake compared to other metals, though at reduced levels compared to Vertisol soil. This suggests that the soil type significantly influences the extent of heavy metal accumulation, with Vertisol soil facilitating greater metal uptake. The lower uptake in OECD soil may be due to differences in soil properties, such as pH and organic matter content, which can affect metal solubility and bioavailability.

Kruskal–Walli’s test results for the UI presented in

Table 5. Kruskal–Walli’s (H) test of BAI and UI of white mustard grown in CB-contaminated soils.

	BAI
Factors	Al	Cu	Fe	Mn	Zn	Ba	Ti
Soil type	0.155	0.845	0.069	3.625	1.724	9.931 b	8.728 b
CB use	2.049	3.336	2.078	0.856	0.478	5.597	5.160
CB brand	1.856	2.740	1.248	0.787	2.543	2.317	4.023
	UI
Soil type	4.142 c	0.276	5.280 c	9.121 b	6.897 b	0.155	0.069
CB use	7.213 c	1.915	16.505 a	10.518 b	1.289	2.654	22.614 a
CB brand	6.701	0.630	10.321 c	6.984	5.277	0.285	2.146

Note: The results are expressed as Chi-square value (χ²). ^a p < 0.001, ^b p < 0.01, and ^c p < 0.05.

indicate significant variability in the accumulation of certain heavy metals in white mustard plants due to soil type, the use of CBs, and the CB brand. Soil type significantly influenced the uptake of Fe (χ² = 5.280, p < 0.05), Mn (χ² = 9.121, p < 0.01), Zn (χ² = 6.897, p < 0.01). The presence of CBs in the soil also had a significant impact on the uptake of Fe (χ² = 16.505, p < 0.001), Mn (χ² = 10.518, p < 0.01), and titanium (Ti) (χ² = 22.614, p < 0.001). Additionally, the CB brand was found to significantly influence the uptake of Fe (χ² = 10.321, p < 0.05). However, other heavy metals such as Cu and Ba did not exhibit significant differences across soil type, CB use, or CB brand, implying a more consistent uptake regardless of these factors.

3.3.3. Tolerance Index

The investigated plant, white mustard, can tolerate soils containing CB leachates from three cigarette brands since the mean TI in all the treatment combinations and soil types ranged between 0.93 ±0.22 to 1.60 ±0.38 (Figure 6). The values of less than 1 (i.e., 0.90), were observed in a white mustard plant grown in soils contaminated with used and unused CB leachate (USCB₂). It is worth noting that the soil type, cigarette use, and CB brand showed significant differences in the TI content of the tested plant. The control treatments of both soils had the highest TI content compared to treatment where soils were incorporated with used and unused CB leachate, with a TI content of 1.60 for OECD soil without CB leachate, and a TI content of 1.19 in the Vertisol alone.

3.4. Factor and Principal Component Analysis

Kaiser–Meyer–Olkin (KMO) and Bartlett’s sphericity tests were carried out to guarantee that the data were suitable for the factor analysis [37]. KMO levels between 0.8 and 1 are regarded as adequate, whereas those between 0.5 and 0.8 are moderately acceptable, and those below 0.5 are considered inadequate. KMO’s sample adequacy value of 0.781 and Bartlett’s sphericity test statistically significant level (χ² = 877.564, p < 0.001) indicate that the PCA may be implemented appropriately. The rotated component matrix presents the factor loadings of each variable on the four components extracted through principal component analysis (PCA) with varimax rotation.

Table 6. Varimax-rotated component matrix.

	Rotated Component Loadings
	1	2	3	4	Communality Estimates
Dry weight	−0.931				0.949
Fresh weight	−0.919				0.948
Mn	0.888				0.796
Zn	0.865				0.753
Plant height	−0.862				0.878
Ca	0.793		0.472		0.883
Na	0.737			0.304	0.735
Al		0.878			0.785
Fe		0.793			0.769
Ti	0.447	0.678		0.507	0.947
K			0.856		0.735
Mg	−0.317		0.834		0.809
Ba		0.428		0.801	0.844
Cu			0.470	0.682	0.769
Germination	−0.414			−0.581	0.626
Eigenvalues	6.701	2.581	1.787	1.159
% of Variance	44.675	17.203	11.914	7.725
Cumulative %	44.675	61.878	73.792	81.516

Note: Extraction method: principal component analysis; Rotation method: varimax with Kaiser normalization; Kaiser–Meyer–Olkin measure of sampling adequacy: 0.781; Bartlett’s test of sphericity: <0.001.

illustrates the FA/PCA results, including eigenvalues, cumulative variance, and communality estimates. These loadings illustrate the variables that contribute significantly to each component, aiding in the interpretation of the underlying structure of the data. For instance, all four components have characteristic roots greater than 1 and together account for 81.516% of the overall variation.

The scree plot in Figure 7A displays information on all factors’ eigenvalues. Of the total of 15 components, four showed an eigenvalue of greater than 1, and these were taken into consideration. A significant drop in eigenvalue magnitude after the first two components explains a substantial amount of variance in the data. The first two components gave eigenvalue values of 6.701 and 2.581, and the third and fourth gave eigenvalue values of 1.787 and 1.159, respectively. The 3D plot in Figure 7B visualizes the relationships among the first three principal components extracted from the analysis, accounting for 73.792% of the overall variance (Figure 7). The factor loadings were classified as “strong”, “moderate”, and “weak” based on the absolute loading values of 0.75 and above, 0.75–0.50, and 0.50–0.30, respectively [38]. Each point in the plot represents a variable, with the positions indicating the loadings on the components. The first component (Component 1) has high positive loadings for Ca, Na, Zn, and Mn, and high negative loadings for fresh weight, dry weight, and plant height, which explains 44.675% of the total variance. The second component (Component 2) shows high positive loadings for Al, Fe, and Ti, and negative loadings for K, accounting for 17.203% of the variance, while the third component (Component 3) has high positive loadings for K and Mg, accounting for 11.914% of the variance. It is worth noting that Component 1 is primarily defined by strong negative loadings of dry weight (−0.931), fresh weight (−0.919), and plant height (−0.862), with Component 4 showing a negative loading for germination (−0.581).

Multivariate Interaction and Main Effects of Indicator Variability

The MANOVA results presented in

Table 7. MANOVA results.

Effect	Multivariate Test	Value	F	Hypothesis df	Error df	Sig.
CB use	Pillai’s trace	0.844	52.618	4	39	<0.001
	Wilks’ lambda	0.156	52.618	4	39	<0.001
	Hotelling’s trace	5.397	52.618	4	39	<0.001
	Roy’s largest root Pillai’s trace	5.397	52.618	4	39	<0.001
CB brand		0.409	2.574	8	80	0.015
	Wilks’ lambda	0.628	2.551	8	78	0.016
	Hotelling’s trace	0.532	2.527	8	76	0.017
	Roy’s largest root	0.370	3.702	8	40	0.012
Soil type	Pillai’s trace	0.979	445.951	4	39	<0.001
	Wilks’ lambda	0.021	445.951	4	39	<0.001
	Hotelling’s trace	45.739	445.951	4	39	<0.001
	Roy’s largest root	45.739	445.951	4	39	<0.001
CB use × CB brand	Pillai’s trace	0.512	3.444	8	80	0.002
	Wilks’ lambda	0.496	4.099	8	78	<0.001
	Hotelling’s trace	1.002	4.757	8	76	<0.001
	Roy’s largest root	0.985	9.852	4	40	<0.001
CB use × Soil type	Pillai’s trace	0.805	40.233	4	39	<0.001
	Wilks’ lambda	0.195	40.233	4	39	<0.001
	Hotelling’s trace	4.126	40.233	4	39	<0.001
	Roy’s largest root	4.126	40.233	4	39	<0.001
CB brand × Soil type	Pillai’s trace	0.586	4.146	8	80	<0.001
	Wilks’ lambda	0.487	4.222	8	78	<0.001
	Hotelling’s trace	0.903	4.290	8	76	<0.001
	Roy’s largest root	0.683	6.834	4	40	<0.001
CB use × CB brand × Soil type	Pillai’s trace	0.254	1.455	8	80	0.187
	Wilks’ lambda	0.751	1.500	8	78	0.171
	Hotelling’s trace	0.324	1.541	8	76	0.157
	Roy’s largest root	0.301	3.013	4	40	0.29

indicate the multivariate tests for the effects of CB use, CB brand, soil type, and their interactions on the dependent variables. The four multivariate tests—Pillai’s trace, Wilks’ lambda, Hotelling’s trace, and Roy’s largest root—provide insight into the significance of these effects. Wilks’ lambda was used to interpret multivariate tests. The results of Wilks’ lambda showed a statistically significant multivariate effect of CB use (value of 0.156, F-statistics of 52.618, and p < 0.001), CB brand (0.628, F-statistic of 2.551, p of 0.016), and soil type (value of 0.021, F-statistic of 445.951, p < 0.001) on the dependent variables. The results of the two-way interaction, i.e., CB use * CB brand and CB use * soil type, showed a statistically significant interaction effect (value is 0.496, with an F-statistic of 4.099 and p < 0.001; value is 0.195, with an F-statistic of 40.233 and p < 0.001). The results of the three-way interaction showed a non-statistically significant effect on the dependent variables (value is 0.751, with an F-statistic of 1.150 and a p-value of 0.187).

3.5. Post-Harvest Soil Chemical Properties

Compared to the initial chemical properties of the soils (Table 1), the pH of the Vertisol soil and OECD soil alone without CB leachate increased up to 7.05 and 7.23 from an initial pH of 6.88 and 6.48, with a slight increase after the application of CB leachate in all Vertisol and OECD soil treatments (Table S1). For example, in Vertisol soils, the pH (H₂O) values range from 6.63 to 6.70 for treatments involving CB leachates, slightly lower than the pH of 7.05 observed in the control treatment. For OECD soils, the pH (H₂O) ranges from 7.25 to 7.43, with SCB₁ and SCB₂ treatments recording identical pH values of 7.25. It is worth noting that the pH of OECD soil alone is slightly higher at 7.23, showing minimal variation across treatments. The electrical conductivity (EC) increases across both soil types in treatments involving CB leachates. In Vertisol soil, EC ranges from 73.70 μS·cm⁻¹ in USCB₂ to 90.70 μS·cm⁻¹ in SCB₂, representing a percentage increase of approximately 23.07% in SCB₂ compared to USCB₂. Similarly, for OECD soils, EC increases from 112.65 μS·cm⁻¹ in SCB₂ to 123.08 μS·cm⁻¹ in USCB₃, an increase of 9.26%. In both soil types, the CaCO₃ content remains low or undetectable in Vertisol soils, whereas in OECD soils, it ranges from 1.05% to 1.19%, showing only a slight variation across treatments, with slightly higher values among the used CB treatments, which explains the results of the Kruskal–Wallis test for the post-harvest soil elemental properties. For instance, the results of pH, Ca, Mg, Na, and K showed no significant differences between soil type, CB use, and CB brand, with only CaCO₃ (%) showing significant differences between OECD soil and Vertisol soil. Moreover, the concentrations of key elements, such as Ca, Mg, Na, and K, exhibit considerable variation across treatments. Vertisol soil showed the highest Mg and K content, compared to OECD soil treatment combinations, including the soils without the CB leachate. Moreover, the Ca content was higher in the OECD soil compared to the Vertisol soil treatments. However, compared to the initial Ca content of 45.09.08 mg·kg⁻¹ (Table 1), the Ca content in SCB₂ and SCB₃ decreased by 21.73% and 30.62%, respectively, while it increased in other treatments. In Vertisol soils, Ca content ranges from 3128.55 mg·kg⁻¹ in SCB₃ to 4968.44 mg·kg⁻¹ in USCB₃, representing a 58.81% increase in USCB₃ compared to SCB₃, while for OECD soils, the Ca concentration in SCB₂ is 5022.83 mg·kg⁻¹, rising to 5380.85 mg·kg⁻¹ in USCB₃, marking a 7.11% increase. The Na content increased in Vertisol soils compared to the initial Na value of 84.54 mg·kg⁻¹, with an increase percentage of between 12.16% and 68.60%, respectively.

The results of the soil heavy metals content (Table S2) illustrate the effect of CB leachates on the element content of post-harvest soils, specifically comparing the chemical composition between Vertisol and OECD soils treated with various CB brands. Compared to OECD soil, Al, Co, Cr, Cu, Fe, Mn, Ni, Pb, Zn, Ba, and V were high in Vertisol soil and followed the order Al ˃ Fe ˃ Mn ˃ Ba ˃ Zn ˃ Cu ˃ V ˃ Cr ˃ Ni ˃ Pb ˃ Co. For instance, the Vertisol alone recorded 12,670.04 mg·kg⁻¹, while the Al concentration in OECD soil was 2431.34 mg·kg⁻¹. The addition of CB leachates caused an increase in Al content, particularly in the USCB treatments, where Al reaches 15,212.87 mg·kg⁻¹ to 15,605.72 mg·kg⁻¹, a 19.22% to 22.30% increase compared to Vertisol alone. Similarly, Fe content showed a marked rise across the Vertisol treatments, with USCB₁ leading to an increase of 13.30% compared to Vertisol alone (1483.30 mg·kg⁻¹). In OECD soil, elemental content also shifts significantly due to CB leachate treatments, and the treatments followed the order Fe ˃ Al ˃ Ti ˃ Mn ˃ Ba ˃ Zn ˃ Pb ˃ Cu ˃ V ˃ Cr ˃ Ni. The Co content in all OECD soil treatments was undetected (Table S2). Compared to Vertisol, Fe content showed a modest increase compared to Al in the soil, rising from 4227.73 mg·kg⁻¹ in the control to 4464.65 mg·kg⁻¹ in USCB2, marking a 5.60% increase. This is, however, smaller than the increase seen in Vertisol soil (i.e., 16.51%). Contrastingly, Al experienced a reduction from 2431.34 mg·kg⁻¹ in the control to 2110.22 mg·kg⁻¹ in USCB₃, a reduction of 13.21%.

4. Discussion

The detrimental effects of CB leachate are readily apparent for OECD soils that contain 10% sphagnum peat, which is representative of organically carbon-rich and nutritionally available soils. The noted reduction in germination rate, especially in OECD soils treated with SCB₁ leachate, highlights the possible phytotoxic effects of smoked CB pollutants on plant growth (Table 2). Additionally, the Kruskal–Walli’s test results confirm that soil type significantly affects key physiological parameters of white mustard, such as germination rate, plant height, fresh weight, and dry weight (Table 3), indicating that the physical and chemical properties of the soil influence the extent of CB toxicity. These results can be explained by the negative correlations observed between Ca and Na with growth parameters, suggesting that high concentrations of these elements (with high values in OECD soil treatments) may have detrimental effects, possibly due to nutrient imbalances or osmotic stress, which can inhibit plant development [39,40]. The observed reduction may also be attributed to the nicotine content present in the used CBs employed to prepare the leachate. According to Selmar et al. [41], nicotine from CBs is absorbed by plants, thereby establishing a novel pathway for agricultural contamination. For instance, their study reported a significantly higher nicotine uptake in peppermint plants exposed to CBs compared to those subjected to tobacco mulching. The elevated Mg content in Vertisol soils explains the positive correlation between Mg and key growth parameters, including plant height, fresh weight, and dry weight, highlighting the essential role of Mg in chlorophyll biosynthesis and photosynthesis, which are critical for healthy plant growth [42].

The variability in heavy metal accumulation by white mustard plants, as depicted in Figure 1, highlights the substantial impact of soil type on metal uptake. The significant disparities in the accumulation of Mn, Zn, and Ti between the two soil types (Table 4) indicate that soil type is pivotal in influencing the bioavailability and absorption of these elements by plants. For instance, the Ti concentration in white mustard grown in OECD-contaminated soil with used CB leachate was high compared to Vertisol soil treatments. Holubík et al. [43] reported the potential of the white mustard plant as a hyperaccumulator of Ti. The study examined Ti bioaccessibility in white mustard grown in artificial soil versus hydroponic conditions, finding greater bioaccessibility in soil, with the highest concentrations observed in the plant shoots. The bioaccumulation index (BAI) results corroborated these findings, demonstrating considerable diversity in heavy metal uptake by white mustard plants, significantly influenced by soil type and the presence of CB leachates. According to Kabata-Pendias [44], BAI can assess the efficiency of element accumulation. The higher BAI values for Cu, Zn, and Ba in OECD soil treatments, particularly the elevated Zn accumulation, with a BAI of 6.38 in the SCB₁ treatment, suggest that OECD soil enhances the bioavailability of these metals, facilitating greater uptake by the plants. This could be attributed to the physicochemical properties of OECD soil, such as pH and organic matter content, which are known to influence metal mobility and plant absorption [45]. Also, the low germination rate of the white mustard plant reported in the same treatment (SCB₁) might be associated with high Zn bioaccumulation, including the low plant height, fresh weight, and dry weight in all OECD soil treatments compared to the Vertisol soil treatments (Table 2). In contrast, the lower BAI values for Al and Fe across all treatments indicate that these metals are less bioavailable or that white mustard plants possess mechanisms to limit their uptake, possibly to avoid toxicity. For instance, the availability of Pb in the post-harvest soil (Table S2), with non-detected Pb (results not reported in the study) in white mustard plant, aligns with previous findings by Vasile et al. [46], which demonstrated that Pb is predominantly retained in the soil and absorbed by white mustard roots in minimal concentrations, without translocating to other plant parts (e.g., stems or leaves). Similarly, in the present study, Pb from CB leachates was retained in the soil and not detected in white mustard plants, indicating its immobilization in the soil and minimal risk of plant bioaccumulation. The relatively low BAI values in Vertisol soil, except for Zn, further underline the impact of soil characteristics on metal accumulation, with Vertisol soil potentially binding these metals more strongly, thus reducing their availability for plant uptake. These results are also supported by the high concentrations of all the tested metals in post-harvest Vertisol soil compared to the OCED soil (Table S2).

Apart from the effect of soil type, Kruskal–Walli’s test results for heavy metal uptake index (UI) showed an effect of CB use on Al, Fe, Mn, and Ti uptake by the white mustard plant, with the CB brand showing an effect on Fe uptake. The significant effect of CB brand on Fe uptake indicates variability in the metal content of different CBs, which could be linked to differences in the composition of tobacco and filter materials among brands. The absence of significant differences in metal bioaccumulation (BAI and UI) across CB brands (Table 5) implies that the metal content in CBs is relatively consistent, likely due to standardized manufacturing processes. Moreover, the highest tolerance index (TI) values in the control treatments without CB leachate indicate that the absence of contaminants is optimal for growth, confirming the detrimental effects of CB leachates on plant health, although within a tolerable range for white mustard. For instance, the TI results suggest that white mustard demonstrates a notable capacity to tolerate soils contaminated with CB leachates across different brands, as evidenced by TI values ranging from 0.93 ± 0.22 to 1.60 ± 0.38. This finding indicates that the plant can sustain growth under the stress imposed by CB contaminants, with most TI values exceeding 1, which typically signifies tolerance or slight growth stimulation in the presence of contaminants [47].

In addition to the results of Kruskal–Wallis, the MANOVA results highlight the significant interaction effects between CB use and soil type (F (4, 39) = 40.233, p < 0.001, Wilk’Λ = 0.195), as well as between CB use and soil type (F (8, 78) = 4.222, p < 0.001, Wilk’Λ = 0487) (Table 6), suggesting that the combined influence of these factors can either mitigate or amplify the effects of CB contamination on plant growth, depending on the specific CB brand and soil type involved. Moreover, the lack of a significant three-way interaction (CB use × CB brand × soil type) indicates that while each factor and their pairwise combinations are critical, their combined interaction does not further exacerbate the impact on the dependent variables.

5. Conclusions

The findings of this study emphasize the significant phytotoxic effects of CB leachates on white mustard seed germination and growth, which are particularly evident in treatments involving CB leachates in both Vertisol and OECD soils. The observed reductions in germination rates, plant height, fresh weight, and dry weight demonstrate the negative impact of CB contaminants, including heavy metals. The Kruskal–Walli’s test results further reinforce the pivotal role of soil type in mediating these effects, with OECD soil, rich in organic carbon, showing more pronounced reductions in plant physiological parameters, likely due to nutrient imbalances and osmotic stress. These outcomes suggest that CB pollution poses a considerable risk to plant health, depending upon soil characteristics.

Moreover, the study highlights the variability in heavy metal uptake by white mustard, significantly influenced by both soil type and CB leachates. The bioaccumulation index (BAI) results point out the heightened metal absorption, particularly of Zn in OECD soils, which may worsen toxicity and impair plant growth. Contrastingly, the reduced metal bioavailability in Vertisol soils suggests that soil characteristics can mitigate the extent of CB contamination effects. The significant interaction between CB use and soil type, as indicated by MANOVA, suggests that while these factors independently influence plant responses, their combined effects do not aggravate the impact beyond a tolerable range for white mustard. Nevertheless, the absence of significant variations linked to the brand or usage of CBs indicates that, although CBs are harmful to plant growth, the specific brand or usage status may not be a decisive factor in determining their overall impact. These results highlight the complex relationship between soil contaminants and plant responses, highlighting the importance of precise soil management practices to reduce the detrimental effects of CB contamination on plant growth. Future research could investigate the level of PAHs in soils contaminated by CB leachate to understand the extent of their accumulation, their persistence over time, and their impact on soil microbial activity and plant health. Additionally, such studies could explore the potential of bioremediation strategies, such as the use of hyperaccumulator plants, to mitigate the harmful effects of these contaminants on both soil quality and overall ecosystem functioning.