Soil, Tree Species, and Pleurozium schreberi as Tools for Monitoring Heavy Metal Pollution in Urban Parks

Abstract

Urban parks are an integral component of cities; however, they are susceptible to heavy metal contamination from anthropogenic sources. Here, we investigated the moss Pleurozium schreberi and tree leaves as bioindicators for monitoring heavy metal contamination in urban parks. We determined heavy metal concentrations in P. schreberi, leaf tissues of selected tree species, and soil samples collected from various locations within a designated urban parks. The order of heavy metal accumulation was Zn > Pb > Cr > Cu > Ni > Cd > Hg in soil and Zn > Cu > Pb > Cr > Ni > Cd > Hg in P. schreberi. The order was Zn > Cu > Cr > Ni > Pb > Cd > Hg in linden and sycamore leaves, while birch leaves displayed a similar order but with slightly more Ni than Cr. The heavy metal concentration in the tested soils correlated positively with finer textures (clay and silt) and negatively with sand. The highest metal accumulation index (MAI) was noted in birch and P. schreberi, corresponding to the highest total heavy metal accumulation. The bioconcentration factor (BAF) was also higher in P. schreberi, indicating a greater ability to accumulate heavy metals than tree leaves, except silver birch for Zn in one of the parks. Silver birch displayed the highest phytoremediation capacity among the analysed tree species, highlighting its potential as a suitable bioindicator in heavy metal-laden urban parks. Our findings revealed significant variation in heavy metal accumulation, highlighting the potential of these bioindicators to map contamination patterns.

1. Introduction

Heavy metals present significant challenges in urban environments, particularly in parks and green spaces, where human activities intersect with natural ecosystems [1]. These pollutants enter urban parks through various means, such as atmospheric deposition, surface runoff, and improper waste disposal [2]. Once heavy metals infiltrate parks, they disrupt the balance of plant, animal, and microbial life, threatening ecological stability and posing health risks through contaminated water sources and food chains [3]. Addressing this issue requires strategic park design and management. Phytoremediation, which uses plants to remove pollutants from the soil [4], and sustainable practices such as green infrastructure development can gradually reduce metal concentrations in contaminated areas [5]. Toxic elements, including lead, cadmium, mercury, and arsenic, present serious concerns, particularly when detected in densely populated urban areas with high industrial activity and inadequate waste management [6]. Their accumulation on tree leaves negatively impacts environmental health and human well-being. Trees are passive air quality monitors, with their leaves effectively capturing airborne particles [7]. Consequently, the accumulation of toxic elements on tree leaves indicates pollution levels in urban areas [8]. These elements can disrupt physiological processes in trees, impairing growth, photosynthesis, and nutrient uptake [9]. Additionally, they may leach from tree leaves into the surrounding soil, further contaminating the environment and posing risks to soil microbial communities and groundwater quality [10,11]. Monitoring and assessing levels of toxic elements in tree leaves can provide valuable data for environmental management and decision-making processes aimed at protecting both ecological and human health in urban areas [12].

Bryophytes, including mosses and liverworts, are important indicators of environmental integrity in urban parks due to their ability to absorb heavy metals from their surroundings [13]. Monitoring the level of heavy metals in bryophyte tissues could indicate their distribution within park environments and help determine the degree of pollution [14]. The sensitivity of bryophytes to environmental changes makes them effective sentinels for detecting heavy metal pollution, offering practical benefits for urban environmental monitoring [15]. Integrating bryophyte-based monitoring and epiphytic lichens into environmental strategies can improve how contamination in cities is detected and managed, protecting urban ecosystems and public health [16]. One of the most commonly used bryophytes in environmental studies is Pleurozium schreberi [17,18].

Community engagement and public education are essential for raising awareness about heavy metal contamination and empowering residents to help prevent pollution [19]. Tackling contamination in urban areas requires comprehensive strategies that focus on pollution prevention, remediation, and public awareness [20]. Measures such as reducing emissions from industrial sources and vehicles, improving waste management practices, and promoting green infrastructure can help minimise the introduction of metal pollutants into the environment [21]. Humans, especially vulnerable individuals, e.g., children, risk exposure to heavy metals through direct contact or ingesting contaminated tree leaves in urban parks or residential areas [22,23]. Cities can create healthier, more resilient urban landscapes for current and future generations, especially in heavily polluted areas, by adopting integrated approaches prioritising sustainability and community involvement [24,25].

Despite their efforts, monitoring stations often face limitations due to their number and placement, leading to uneven coverage, especially in urban areas and remote green spaces within parks. Here, we aim to examine the interactions among leaves and soil, focusing on the urgent need to identify gaps in addressing heavy metal contamination from various sources. This study contributes to the broader scientific goals of understanding traffic and other pollutants and can be applied to evidence-based strategies for pollution mitigation and environmental management. The novelty of our research was the combined use of moss and tree species in reference to the urban parks in Krakow. The objectives of our study were to (1) demonstrate the role of urban parks in capturing pollutants, (2) quantify heavy metal indices in urban settings and identify their origins, (3) establish the level of contamination in urban parks based on pollution indices, and (4) assess the levels of Pleurozium schreberi and compare them to determine their effectiveness in delineating pollution disparities across various tree species in urban parks.

2. Materials and Methods

2.1. Study Area

The study was performed on 25 monitoring plots located in 5 city parks of Krakow. In each park, 5 monitoring plots, each with an area of 100 m², were established (Figure 1).

In each monitoring plot, 4 individual soil samples were taken at a depth of 0–15 cm, from which, after thorough mixing, a composite sample was created for further laboratory analysis. This method of taking soil samples was employed due to the fact that previous studies [19] indicated that the topsoil accumulates the largest amount of heavy metal. Tree leaves and Pleurozium schreberi tissues were also collected from each monitoring plot. Birch, sycamore, and linden were the tree species selected for this study, and leaves were always sampled from the two most common tree species in a given park and assessed by visual dominance. The tree leaves were collected in July. Fifty leaves were collected from five trees of each species. These tree species are typical of the climatic conditions of Central Europe and are common in Krakow’s parks, although with varying frequency as a result of previous afforestation.

2.2. Laboratory Analysis

Soil samples were dried at 45 °C, crushed in a mortar, and passed through a 2 mm sieve. A fraction of each soil sample was pulverised in a laboratory grinder and used to determine trace elements. The soil texture was determined using an Particle Sizer ANALYSETTE 22 (Fritsch GmbH, Idar-Oberstein, Germany). The soil pH was measured in distilled water and 1 M KCl (20 g of soil mixed with 50 mL distilled water or KCl), and electrical conductivity (EC) was determined using a potentiometric method. TOC and total N and S content were measured using the TruMac CNS elemental analyser (Leco Corporation, Saint Joseph, MI, USA). The ICP-OES technique was applied to determine heavy metal content (Cd, Cr, Cu, Ni, Pb, and Zn) using an iCAP 6500 DUO spectrometer (Thermo Fisher Scientific, Cambridge, UK) following wet digestion in a mixture of concentrated nitric (V) and perchloric acid (VII) in a 2:1 ratio [26]. The Hg content was determined using the ASA technique on a MILESTONE DMA-80 Direct Mercury Analyzer (Milestone, Via Fatebenefratelli 1/5, Sorisole, Italy). The quality of determination was controlled by subsequent analysis of ERM-CC141-certified reference materials.

The collected unwashed plant material was dried at room temperature and then in a laboratory dryer at 30–40 °C for several days. Plant samples were then crushed using a grinder. The same heavy metals as in the soil samples were determined via the ICP-OES technique, using the above-mentioned spectrometer, following sample mineralisation in concentrated perchloric acid (VII).

2.3. Pollution Indices

2.3.1. Soil

Geoaccumulation Index (I_geo)

I_geo is one of the most long-established and accurate indices for assessing soil pollution, allowing comparisons of past and present pollution [27]. It is particularly useful for assessing heavy metal pollution of the surface soil layer, with reference to a specific geochemical background [28].

$$Igeo={log}_2\left[{\displaystyle \frac{Cn}{1.5GB}}\right]$$

(1)

where

Cn is the concentration of individual heavy metals in the sample;

GB is the geochemical background; value 1.5 (constant allowing for an analysis of the variability of heavy metals as a result of natural processes).

The calculations were made using the geochemical background proposed by Kabata-Pendias (K-P) [29], the upper continental crust (UCC) as determined by Rudnick and Gao [30], as well as the local background, i.e., the heavy metal content of the bedrock of the studied Krakow area. The values given by Kabata-Pendias were based on the average heavy metal content in surface soil horizons, and UCC according to Rudnick and Gao on the composition in upper continental crust. Their compilation can be found in Kowalska et al. [27]. Soil quality was classed as follows: ≤0: unpolluted; 0–1: unpolluted to moderately polluted; 1–2: moderately polluted; 2–3: moderately to highly polluted; 3–4: highly polluted; 4–5: highly to extremely highly polluted; ≥5 extremely highly polluted [28].

2.3.2. Plants

Metal Accumulation Index (MAI)

The MAI is an indicator used to assess the degree of heavy metal accumulation in plants. The formula provided by Liu et al. [31] for calculating the MAI is

$$MAI=\left(1/N\right)\times \Sigma \left(Ij\right)$$

(2)

where

N is the total number of metals analysed;

Σ(Ij) is the summation of the sub-indices for all metals.

The sub-index (Ij) for each metal was calculated as

$$Ij=x/\Delta x$$

(3)

where

x is the mean concentration value of the metal;

Δx is the standard deviation of the metal concentrations.

Interpretation of MAI:

Higher MAI values indicate a higher level of metal accumulation in the sample;

Lower MAI values suggest a lower level of metal accumulation.

Bioaccumulation Factor (BAF)

The BAF indicates the degree to which a metal accumulates in an organism relative to its concentration in the environment. A higher BAF value suggests that the organism is more efficient at accumulating the metal. The formula for determining BAF for heavy metals is

$$BAF={\displaystyle \frac{Cm}{Cs}}$$

(4)

where

Cm is the concentration of the metal in the organic material;

Cs is the concentration of the metal in the soil.

2.4. Statistical Analyses and Spatial Data Processing

The correlation between soil characteristics was analysed using Spearman’s non-parametric method. A multidimensional approach was necessary to compare physicochemical parameters in urban parks, using principal component analysis (PCA) to reduce data points by determining components that were linear combinations of the studied variables. A correlation matrix was used for this analysis, focusing on the physicochemical parameters of the soil. The Kaiser–Meyer–Olkin (KMO) coefficient and Bartlett’s test confirmed the significance of the results. All variables were standardised prior to PCA computation to ensure comparability. No data were missing from the dataset; therefore, PCA was conducted using complete-case analysis. Analyses were conducted using PAST version 4.17.

The Wilcoxon rank test based on Bayesian statistics was used to measure characteristic parameters in soil and heavy metals in moss. BF10 indicates evidence for or against hypotheses in this method, and Rhat checks convergence in Markov Chain Monte Carlo (MCMC) simulations. A value close to 1 indicates similarity between the two variables. Specifically, we selected the Bayesian approach to obtain richer probabilistic interpretations of group differences. However, we acknowledge the convergence issue and clarify that we included only models with Rhat values close to 1. The analysis was performed using JASP version 0.18.3.0.

3. Results

3.1. Basic Soil Properties

The basic properties of the studied soils are presented in

Table 1. Physicochemical soil properties in urban parks, mean values.

Park	pH H2O	pH KCl	EC	TOC	TN	S	Sand	Silt	Clay
			(µS/cm)	g/kg	g/kg	g/kg	%	%	%
Bednarskiego	7.5	7.0	183.8	47.8	2.71	0.117	56.2	38.0	5.8
Lotników Polskich	5.4	4.6	90.6	21.3	1.53	0.221	44.2	50.2	5.6
Planty Bieńczyckie	6.4	5.7	107.0	25.9	1.99	0.253	28.2	65.0	6.8
Jordana	6.8	6.3	165.0	38.9	2.80	0.343	43.4	51.6	5.0
Jerzmanowskich	6.0	5.3	106.6	24.5	1.82	0.282	49.6	44.6	5.8
min.	4.5	3.6	65.0	14.0	1.11	0.077	13.0	27.0	4.0
max. SD	7.6 0.8	7.2 1.0	271.0 48.5	65.6 13.0	3.44 0.68	0.511 0.100	69.0 16.9	74.0 15.2	13.0 2.27

where EC—electrical conductivity, TOC—total organic carbon, TN—total nitrogen, S—sulphur.

. The Bednarski Park and Lotników Park soils were characterised by alkaline and strongly acidic reactions, respectively. The soils of the remaining parks were slightly acidic. The soil with the lowest pH was also characterised by the lowest electrical conductivity. The contents of TOC and TN were highest in the soils of Bednarskiego Park and Jordana Park, located in the centre of Krakow, and lowest in the soils of Lotników Park. The total sulphur content was highest in the soils of Jordana Park, located nearest to the historic city centre. All tested soils had a similar mean clay content (close to 6%). Soils of Bieńczyckie Planty Park had the highest silt content and the lowest sand content due to its location on loess bedrock.

The correlation matrix includes soil properties such as heavy metal concentrations, pH, EC, soil texture components, TN, TOC, and S. Sand was negatively correlated with clay and silt. Heavy metals showed negative correlations with sand but positive correlations with clay and silt (Figure 2). Cr and Cu showed a different direction, Cd and Hg were clustered together, and pH (H₂O), pH (KCl), TN, TOC, EC, Pb, and Zn were closely aligned. PC1 (43.49%) likely represented variations in metal contamination and organic matter (TOC, TN, Pb, and Zn). PC2 (19.86%) may be influenced by soil texture factors (sand, clay, and silt). Combined, they represented 63.35% of the total variance in the dataset (Figure 3).

3.2. Heavy Metal Accumulation in Soil, Pleurozium schreberi, and Tree Leaves

The order of heavy metal (HS) accumulation was Zn> Pb > Cr > Cu > Ni > Cd > Hg in the soil, Zn> Cu > Pb > Cr > Ni > Cd > Hg in P. schreberi, and Zn> Cu >Cr > Ni > Pb > Cd > Hg in linden and sycamore leaves. The order in birch leaves was similar to that in linden and sycamore leaves but with slightly more Ni than Cr. The soils in Bednarskiego Park were usually characterised by the highest heavy metal content. On the other hand, the highest content of heavy metals in P. schreberi and leaves was found in Lotników Polskich Park (

Table 2. Mean value of heavy metals accumulation for topsoil, Pleurozium schreberi, and tree leaves in urban parks.

Park	Cd	Cr	Cu	Ni	Pb	Zn	Hg
			mg/kg
Soil
Bednarskiego	1.56	28.6	13.2	15.1	54.8	173.0	0.20
Lotników Polskich	0.53	27.1	8.3	8.5	31.7	92.5	0.05
Planty Bieńczyckie	0.58	27.2	13.1	16.0	47.2	148.5	0.06
Jordana	0.89	25.5	20.4	15.9	57.9	162.4	0.19
Jerzmanowskich	0.72	22.5	22.7	12.7	44.2	119.2	0.12
min.	0.29	20.4	6.2	6.4	19.3	66.9	0.03
max.	1.94	31.6	47.7	23.1	113.0	237.4	0.34
SD	0.44	3.65	8.28	4.28	22.6	50.6	0.09
Pleurozium schreberi
Bednarskiego	1.04	21.1	14.8	10.0	22.4	130.3	0.12
Lotników Polskich	1.05	14.6	30.6	8.3	20.8	159.8	0.08
Planty Bieńczyckie	0.70	27.0	14.3	12.7	18.3	141.7	0.08
Jordana	0.99	30.5	33.3	13.7	29.2	156.7	0.11
Jerzmanowskich	1.02	18.9	27.3	8.6	24.7	122.3	0.14
min.	0.44	11.9	11.6	5.7	13.3	105.7	0.05
max.	1.92	38.1	39.0	19.7	34.1	214.4	0.22
SD	0.28	6.8	9.2	3.2	5.9	29.9	0.03
Linden
Bednarskiego	0.14	3.4	7.9	1.1	2.1	28.0	0.07
Lotników Polskich	0.20	2.0	18.6	2.5	1.4	48.3	0.07
Planty Bieńczyckie	0.17	2.5	5.7	1.4	1.8	34.2	0.06
Jordana	0.15	2.5	17.0	1.5	1.5	30.3	0.09
Jerzmanowskich	0.18	2.2	13.6	3.5	1.5	34.2	0.07
min.	0.11	1.5	5.4	0.6	1.0	23.8	0.05
max.	0.23	3.9	25.2	5.6	2.4	60.1	0.10
SD	0.04	0.6	7.2	1.4	0.5	10.4	0.01
Birch
Lotników Polskich	0.55	1.4	24.3	2.3	1.2	464.7	0.05
Planty Bieńczyckie	0.24	2.0	5.3	4.0	0.4	142.8	0.06
Jordana	0.32	2.3	18.2	1.1	1.3	229.9	0.05
min.	0.17	1.0	5.3	0.8	0.4	142.8	0.03
max.	0.86	2.7	27.5	4.0	1.6	569.7	0.06
SD	0.26	0.6	7.8	1.3	0.5	170.2	0.01
Sycamore
Bednarskiego	0.30	2.4	6.4	0.9	0.8	60.7	0.05
Planty Bieńczyckie	0.34	2.1	4.9	1.0	0.7	30.3	0.06
Jordana	0.22	5.2	3.7	1.9	1.4	31.3	0.05
Jerzmanowskich	0.27	3.3	5.9	1.3	1.1	47.8	0.05
min.	0.10	1.9	3.7	0.7	0.4	25.4	0.04
max. SD	0.51 0.12	5.2 1.1	7.4 1.2	1.9 0.4	1.4 0.4	103.4 25.4	0.07 0.01

).

The assessment of the degree of heavy metal pollution in the studied soils was mostly influenced by the type of geochemical background (GB) used (

Table 3. Geoaccumulation Index (I_geo) values for investigated soil in the urban parks.

	Cd	Cr	Cu	Ni	Pb	Zn	Hg
K-P
Bednarskiego	1.3	−1.6	−2.2	−1.6	0.4	0.7	0.8
Lotników Polskich	−0.3	−1.7	−2.8	−2.4	−0.4	−0.2	−1.1
Planty Bieńczyckie	−0.1	−1.7	−2.2	−1.4	0.0	0.4	−1.1
Jordana	0.5	−1.8	−1.5	−1.5	0.5	0.6	0.7
Jerzmanowskich	0.1	−2.0	−1.5	−1.9	0.0	0.0	0.1
min.	−1.1	−2.1	−3.2	−2.8	−1.1	−0.7	−2.1
max.	1.7	−1.5	−0.3	−0.9	1.5	1.2	1.7
UCC
Bednarskiego	3.5	−2.3	−1.7	−2.3	1.0	0.7	1.3
Lotników Polskich	1.9	−2.4	−2.4	−3.1	0.3	−0.2	−0.7
Planty Bieńczyckie	2.1	−2.4	−1.7	−2.1	0.6	0.5	−0.6
Jordana	2.7	−2.4	−1.1	−2.2	1.2	0.7	1.2
Jerzmanowskich	2.3	−2.6	−1.1	−2.6	0.7	0.1	0.6
min.	1.1	−2.8	−2.8	−3.5	−0.4	−0.6	−1.6
max.	3.8	−2.1	0.2	−1.6	2.1	1.2	2.2
Local GB
Bednarskiego	7.7	2.1	2.3	9.0	3.0	3.2	-
Lotników Polskich	6.1	2.0	1.7	8.2	2.2	2.4	-
Planty Bieńczyckie	6.3	2.0	2.3	9.1	2.6	3.0	-
Jordana	6.9	1.9	3.0	9.1	3.1	3.2	-
Jerzmanowskich	6.5	1.8	3.0	8.7	2.6	2.6	-
min.	5.3	1.6	1.3	7.8	1.5	1.9	-
max.	8.0	2.3	4.2	9.7	4.1	3.7	-

). The I_geo values using the GB suggested by Kabata-Pendias indicate that most soils are unpolluted with the analysed heavy metals. Only the soils of Bednarski Park showed moderate pollution with Cd, Zn, Hg, and Pb. Using UCC as a geochemical background, the soils of all studied parks were moderately to highly polluted with Cd, while moderate pollution with Pb and Zn was also found in the soils of some parks. The anthropogenic impact on the environment of the studied urban parks in Krakow was most evident through the use of the local geochemical background. Soils were found to be extremely highly polluted with Ni and Cd and at least moderately polluted with all remaining heavy metals.

The Rhat value for the tested element pairs in soils and mosses ranged from 1.454 to 3.278. Only the Rhat value of 1.454 determined in soils, useful for interpreting the results, was obtained for the Cr and Hg pair in soil (

Table 4. Bayesian Wilcoxon signed-rank test.

Rhat	W	BF10	Measure 2	Measure 1
				Soil
1.960	0.0002	5355.256	TOC	TN
1.757	0.0002	383,975.578	Zn	Pb
2.539	2850.000	77,327.862	Cd	Ni
1.454	2850.000	107,551.599	Hg	Cr
			Pleurozium schreberi
3.278	0.0003	4.218 × 106	C	N
2.609	0.0005	4.190 × 106	Zn	Pb
1.764	2850.000	6.731 × 109	Cd	Ni
2.893	2850.000	483,981.191	Hg	Cr

Note. Result based on data augmentation algorithm with 5 chains of 1000 iterations.

). The highest MAI values were observed in birch and P. schreberi and the lowest in linden and sycamore. Particularly high MAI values were calculated for Lotników Polskich Park. The results for the other parks studied were similar (

Table 5. Metal accumulation index (MAI) for tree leaves and Pleurozium schreberi in various urban parks.

MAI	Species	Park
45.55	Sycamore	Bednarskiego
19.64	Linden
48.62	Pleurozium schreberi
95.58	Birch	Lotników Polskich
56.75	Linden
50.26	Pleurozium schreberi
21.40	Linden	Planty Bieńczyckie
20.42	Sycamore
43.03	Birch
47.22	Pleurozium schreberi
13.68	Linden	Jordana
10.94	Sycamore
55.58	Birch
64.10	Pleurozium schreberi
17.35	Sycamore	Jerzmanowskich
41.07	Linden
48.50	Pleurozium schreberi

). Pleurozium schreberi showed a higher capacity to carry and accumulate heavy metals than tree leaves, as indicated by higher BAF values. Notably, high BAF values were obtained for Lotników Polskich Park compared to the other parks, which was especially true for Zn, and less for Cu (

Table 6. Bioaccumulation factor (BAF) for tree leaves and Pleurozium schreberi in different parks.

Hg	Zn	Pb	Ni	Cu	Cr	Cd	Species	Park
0.28	0.42	0.02	0.06	0.58	0.09	0.26	Sycamore	Bednarskiego
0.49	0.16	0.04	0.09	0.52	0.11	0.08	Linden
0.72	0.77	0.43	0.69	1.21	0.74	0.71	Pleurozium schreberi
1.07	5.02	0.04	0.26	3.19	0.06	1.20	Birch	Lotników Polskich
1.41	0.47	0.04	0.30	2.12	0.07	0.33	Linden
1.73	1.79	0.66	0.98	3.38	0.55	1.90	Pleurozium schreberi
0.14	0.24	0.07	0.08	0.47	0.09	0.34	Linden	Planty Bieńczyckie
0.57	0.16	0.01	0.08	0.42	0.08	0.62	Sycamore
0.51	0.75	0.01	0.24	0.34	0.07	0.29	Birch
1.68	0.88	0.54	0.88	1.17	0.98	1.16	Pleurozium schreberi
0.61	0.18	0.03	0.10	0.73	0.10	0.19	Linden	Jordana
0.24	0.20	0.03	0.13	0.17	0.17	0.20	Sycamore
0.41	1.62	0.01	0.08	1.06	0.10	0.42	Birch
0.74	0.99	0.53	0.86	1.67	1.21	1.11	Pleurozium schreberi
0.37	0.37	0.02	0.08	0.23	0.14	0.37	Sycamore	Jerzmanowskich
0.52	0.25	0.01	0.18	0.52	0.10	0.40	Linden
1.03	1.22	0.68	0.75	1.41	0.85	1.31	Pleurozium schreberi

).

4. Discussion

4.1. The Role of Urban Parks in Reducing Anthropogenic Pollution

Studies conducted in urban parks worldwide have reported elevated heavy metal concentrations in the surface layer of soils [32]. In Beijing, research showed that the age of urban parks, or the number of years since development, accounted for 80% of the variance in cadmium (Cd) and zinc (Zn) levels in park soils. Interestingly, population density did not influence the distribution of heavy metals in these urban soils. Thus, the age of the park might play a significant role in determining heavy metal accumulation in urban environments [33]. A similar relationship was also observed in our study. The content of heavy metals, especially Zn, in the soils of Park Lotników Polskich (established in the 1960s and, therefore, the youngest) was lower than in other older parks, especially those closer to the city’s historic centre, such as Park im. H. Jordana and Park im. W. Bednarskiego, established in the late 19th century. The role of urban parks is multifaceted, as they provide recreational space, support biodiversity, and enhance air and water quality. Additionally, urban parks contribute to the overall well-being of city residents by promoting environmental conservation and offering opportunities for restoration and sustainable development within urban areas [34]. Identifying the ecological risk is extremely important in establishing policies related to the remediation of urban parks contaminated with heavy metals [35].

4.2. Heavy Metal Accumulation in Urban Parks

In Nanjing’s Xuanwu District, chromium (Cr) presents the highest non-carcinogenic health risk in urban parks, with the ecological risk of potentially toxic elements (PTEs) ranked as Pb > Ni > Cr > Zn. The risk of Cr to human health surpassed that of other metals, highlighting its seriousness in urban environmental health management [36]. A recent study emphasised the importance of the mine area for atmospheric emissions and identified washing techniques as highly efficient for biomonitoring surveys. Trees in highly contaminated soils could reduce pollutants through bioaccumulation in their tissues at low ecological risk, suggesting their effectiveness as a means of mitigating pollution in the studied areas [37]. Similar concentrations of Pb, Cd, Cu, and Zn were observed in the surface layers of urban park soils in Bydgoszcz, Poland, mirroring findings from urban parks in Krakow [38]. Heavy metal concentrations in soils from 64 urban parks in the karst plateau in Guiyang City were notably high, with Cr at 363 mg/kg, Pb at 1045 mg/kg, and Cd at 4.19 mg/kg. The coefficients of variation (CVs) ranged from 42.2% to 201%, indicating significant variability, particularly around urban centres. These elevated levels suggest the heavy influence of anthropogenic sources of contamination [39].

Negative correlations between sand and metal content indicate that finer soil retains more pollutants (Figure 1). Soil Pb and Zn contents were significantly higher in the city parks of Krakow than in the city parks of Tehran [35], with maximum contents of Pb at 113.0 and 44.7 mg/kg, respectively, and Zn at 237.4 and 131.2 mg/kg, respectively. Conversely, soil Cd content was significantly higher in the parks of Tehran (with a recorded maximum of 6.1 mg/kg) than in the parks of Krakow (1.94 mg/kg). Soil Cu and Ni contents were also higher in the city parks of Tehran; however, the recorded differences were not as high, with Cu at 60.7 and 47.4 mg/kg and Ni at 54.2 and 23.1 mg/kg in Tehran and Krakow, respectively. Soil Cr content was very similar in the urban parks of both cities.

The potential toxicity of heavy metals in the soil can be better assessed using pollution indices [40]. The most widely used universal index is I_geo [27]. Similar to our findings, a study conducted in Planty Park in the heart of Krakow showed that the highest level of contamination was obtained when using the local geochemical background [32]. The studies showed that Planty Park soil was extremely highly polluted with Cd and Ni. Moreover, moderate to high pollution was observed for the other heavy metals. In the case of our research, the highest contamination occurred for Cd, which was confirmed by the I_geo value when we used UCC as well as local background. Undoubtedly, considering only the local geochemical background, there is a clear anthropogenic enrichment in the other analysed heavy metals. According to Dodd et al. [41], the soil I_geo values of urban parks in Halifax and Toronto were moderately to highly polluted with Pb. Park soils from various cities in Canada were moderately polluted with Cd and Cu, while no pollution was associated with Cr, Ni, or Zn.

Despite the low values of the I_geo in our research, especially when the geochemical background of K-P and UCC was used, they are also characterised by high values of MAI and BAF indices. The implementation of a local geochemical background corresponds better with the interpretation of the MAI and BAF.

In our study, we observed the highest content of studied heavy metals in Pleurozium schreberi and leaves in Lotników Park, where soils had the lowest content of heavy metals. This is also evidenced by the highest MAI and BAF values in this park. This was probably due to the low pH values of the studied soils, which contributed to the activation of heavy metals and possible absorption by plants. Other studies indicate a similar relationship [9,37].

4.3. Management of Tree Species Composition in Urban Parks

Strong positive correlations among Cd, Pb, Zn, and Cu suggest a common source of pollution. Soil pH (H₂O) and pH (KCl) levels positively correlated with heavy metals because pH influences the solubility and mobility of metals (Figure 2). Sand pointed in the opposite direction to clay and silt, confirming their negative correlation. Sand displayed a weak contribution to PC1, suggesting that it might not strongly define variability in metal concentrations. Powerful positive correlations among Cd, Pb, Zn, and Cu suggested they share a common source, such as industrial runoff or vehicle emissions, possibly due to pollution. The PCA plot indicated that soil contamination (Cd, Pb, Hg, and Zn) was closely linked to organic matter (TOC and TN). Sand content negatively impacted metal retention, reinforcing our findings from the correlation analysis (Figure 3). High concentrations of Zn, Pb, and Cd in foliar material have been reported, with the Zn concentration in the leaves of silver birch being four times greater than in Scots pine needles. Geostatistical analysis revealed significant variability in Zn and, particularly, Pb concentrations in both washed and unwashed silver birch leaves. Additionally, a greater accumulation of Zn and Pb was observed near the ZGH Bolesław mining and metallurgic plant [37]. Trees that grow in urban parks perform their phytoremediation function by, for example, capturing pollutants from the air. Trees characterised by a larger leaf area, more hairs, a rougher surface, or more wax on the leaves retain more pollutants, including heavy metals, that reach them through this pathway [42,43,44,45]. However, a fraction of the pollutants are also taken up from the soil and incorporated into plant tissues, as demonstrated by the silver birch. Our studies have shown that silver birch has high phytoremediation potential, as evidenced by the MAI and especially BAF values (5.02 for Zn and 3.19 for Cu). This suggests that silver birch is not only an accumulator but also a potential “hyperaccumulator” of these metals. Therefore, it can be used for phytoextraction of Zn and Cu from soils loaded with these metals in urban areas (Table 5 and Table 6). Opinions differ on the role of parks in air purification. Research has shown that small urban parks provide minimal air purification services, with even dense foliage offering little improvement in air quality. This observation is especially true for parks with a length scale of less than 100 m, where pollutant removal is limited [46]. In Hong Kong, tree morphology and distribution significantly influence pollutant dispersion in urban parks. Vegetation barriers are effective only when they have dense foliage within a limited width, while tall trees are more suitable for smaller urban parks, where enhanced ventilation is needed. Proper planting strategies can effectively improve air quality in parks near roadways, as tree height plays a critical role in pollutant infiltration [46]. Although trees were traditionally thought to improve air quality universally, recent studies have shown that their aerodynamic effects can sometimes trap pollutants, exacerbating concentrations in urban parks. Dense trees with low crown bases, planted as barriers, were found to improve air quality, while overly dense plantings reduced wind flow, worsening pollution levels. Tall trees had minimal impact on pedestrian-level airflow but were appropriate for smaller parks where ventilation needed encouragement [47]. Selecting tolerant species and improving soil conditions are essential for managing trees in polluted, reclaimed areas. Utilising resilient tree species reduces mortality, while soil remediation helps minimise health risks for park visitors [48].

5. Conclusions

In this study, we investigated heavy metal contamination in urban parks using tree species and Pleurozium schreberi as bioindicators. TN, TOC, EC, Pb, and Zn were closely aligned, suggesting that organic matter and nutrient content, along with electrical properties, significantly influence heavy metal distribution in soil. Cr and Cu showed different correlation patterns. Cd and Hg clustered together, indicating similar behaviours and possibly common sources or pathways in the soil. The best-converging pair in our Bayesian Wilcoxon model for the soil data was Cr and Hg. Due to limited sample availability, we employed models and pollution indices to estimate heavy metal levels in plant tissues and environmental factors, providing insights into the sources and pathways of contamination. This study demonstrated the potential of tree species and moss as valuable tools for assessing and monitoring heavy metal contamination in urban parks, supporting the development of effective strategies for mitigating environmental risks. Lower MAI in linden and sycamore indicated that these species accumulated fewer heavy metals than birch and P. schreberi. MAI values were similar across the studied parks, suggesting a consistent pattern of metal accumulation in these species. Due to its phytoremediation properties, silver birch should be recommended as the main species in urban park soils with high heavy metal content, especially in the case of parks with low pH soils. Our research has confirmed that birch has high phytoremediation potential and can be used as a hyperaccumulator of heavy metals. P. schreiberi has been proven to be a good indicator of heavy metal contamination and should therefore be used more widely in studies of urban environments. Future research on the pollution status of urban parks should include a larger number of samples and focus on the most sensitive and reliable bioindicators such as P. schreiberi.