Spatial and Temporal Patterns of Trace Element Deposition in Urban Thessaloniki: A Syntrichia Moss Biomonitoring Study

Abstract

Urban air pollution, especially from heavy metal (HM) contamination, poses significant risks to human health and environmental sustainability. This study investigates the spatial and temporal distribution of HM contamination in Thessaloniki, Greece, using Syntrichia moss as a bioindicator to inform urban environmental management strategies. Moss samples were collected from 16 locations representing diverse urban activity zones (motorway, industrial, city center, airport) in March, May, and July 2024. The concentrations of 12 HMs (Al, Sb, As, Ba, Cd, Cr, Co, Cu, Pb, Ni, V, and Zn) were analyzed using ICP-MS, and the contamination factors were calculated relative to controlled moss samples. The results revealed significant spatial variation, with elevated levels of As, Cd, Cr, Pb, and Zn, particularly in high-traffic and industrial zones, exceeding the background levels by up to severe and extreme contamination categories. Temporal trends showed decreases in Al, Ba, and Ni from March to July 2024, while Cr and Cu increased, suggesting seasonally varying sources. Multivariate analyses further distinguished the contamination patterns, implicating traffic and industrial activities as key contributors. Syntrichia effectively captures HM contamination variability, demonstrating its value as a cost-effective bioindicator. These findings provide critical data that can guide urban planners in developing targeted pollution mitigation strategies, ensuring compliance with the European Green Deal’s Zero Pollution Action Plan.

1. Introduction

Urban air pollution is a pressing issue that poses a significant threat to public health and environmental sustainability and is exacerbated by the increasing HM contamination from human activities [1,2]. These activities, including industrial processes, fossil fuel combustion, waste disposal, and agriculture, release persistent and toxic elements such as arsenic, lead, mercury, cadmium, and chromium into the environment. These metals, known to accumulate in ecosystems and bioaccumulate in organisms, are responsible for severe health risks such as respiratory diseases, neurological disorders, and cancers [3,4]. Swain [5] concluded that the principal exposure routes for urban populations include inhalation, ingestion, and dermal contact, increasing the likelihood of chronic health conditions. Given these substantial risks, it is critical to develop efficient and accurate methods for assessing HM pollution and to identify strategies for mitigating its impact, particularly in cities where the problem is more pronounced.

In response to these growing concerns, the European Union (EU) introduced the European Green Deal in 2019. This initiative aims to achieve climate neutrality by 2050, and a vital component of this strategy is the Zero Pollution Action Plan. This plan aims to improve air, water, and soil quality, focusing on reducing pollutants such as HMs to protect human health and ecosystems [6,7]. However, achieving these ambitious goals requires the development of more comprehensive pollution monitoring systems capable of providing high-resolution spatial and temporal data. While effective, traditional methods, such as air sampling and the chemical analyses of soil and water, are often cost-prohibitive and time-consuming, particularly in densely populated urban areas with diverse sources of pollution [8,9,10]. These limitations necessitate the exploration of alternative monitoring strategies that are both cost-effective and able to provide accurate, real-time data on pollution levels.

A promising alternative to conventional monitoring techniques is biomonitoring, which involves using living organisms to assess environmental pollution levels. Mosses have proven to be valuable bioindicators of atmospheric HM deposition [11,12,13]. Mosses possess several characteristics that make them ideal for biomonitoring: their widespread distribution, their high surface area-to-volume ratio, their lack of a cuticle, and their ability to absorb pollutants from rainwater and atmospheric deposition efficiently. These features allow them to effectively capture spatial and temporal variations in air quality, providing critical data for understanding pollution patterns over time [14,15,16,17,18].

The International Cooperative Programme on Effects of Air Pollution on Natural Vegetation and Crops (ICPV) has played a pivotal role in promoting the use of mosses for biomonitoring since 2000. By developing standardized protocols and conducting extensive surveys across Europe, the ICPV has generated a wealth of data on HM deposition trends that have been instrumental in identifying pollution hotspots and evaluating the effectiveness of air quality control measures [19,20,21]. These surveys demonstrate that mosses, as bioindicators, offer a cost-effective and reliable method for assessing air quality, particularly in areas where traditional monitoring methods are impractical.

In Greece, various moss species have been studied for HM content, such as Antitrichia curtipendula, Bryophyton sp., Camptothecium lutescens, Dicranum scoparium, Eucalypta streptocarpa, Grimmia laevigata, Homalothecium aureum, Homalothecium sericeum, Hypnum cupressiforme, Isothecium alopecuroides, Leucodon sp., Pterogonium gracile, Pseudoscleropodium purum, Radula sp., Tortella tortuosa, and Tortula ruralis [22,23,24,25,26,27,28]. However, these moss species are typically found in shaded areas, which may limit their ability to provide pollution data in urban settings, such as cities and industrial areas.

Among the various moss species used in biomonitoring studies, Syntrichia spp. emerges as a promising candidate for urban environments. Unlike many moss species that thrive in shaded or forested areas, Syntrichia is well-adapted to harsh urban conditions. It grows on exposed surfaces such as concrete, rocks, and soil, making it widely available in cities where other moss species may struggle to survive. Furthermore, Syntrichia exhibits a high tolerance for urban stressors, including drought and intense sunlight, making it resilient in environments characterized by fluctuating climate conditions. Its capacity to accumulate airborne pollutants further enhances its suitability as a bioindicator, particularly in urban areas with elevated levels of industrial and vehicular emissions [29,30,31,32,33,34]. Despite its potential, Syntrichia remains underutilized in the biomonitoring of HMs, especially in Mediterranean urban areas.

Thessaloniki, Greece’s second-largest city, presents a compelling case for investigating the use of Syntrichia spp. as a bioindicator of HM contamination. The city hosts a diverse range of industrial activities, including metal processing, textiles production, petroleum refining, chemical, cement and building materials, the transportation and shipping industry, energy production and power plants, processed foods, dairy products, and beverages industries. All of these contribute to elevated atmospheric, soil, and water pollutant levels [35,36]. The city’s dense traffic further exacerbates its pollution problem, while its coastal location and complex topography influence pollutant dispersion patterns, potentially leading to localized hotspots of HM contamination [37]. Although several studies have documented HM concentrations in Thessaloniki’s air, soil, and vegetation [23,24,25], there is a clear need for comprehensive biomonitoring data using Syntrichia to fully understand the extent of the pollution.

The present study aims to assess the spatial and temporal distribution patterns of HM contamination in Thessaloniki using Syntrichia, contributing to urban environmental management strategies. We hypothesize that HM levels will vary significantly across different land-use zones, with higher concentrations expected in areas characterized by dense traffic, industrial activities, and potentially long-range transport. The data generated from this study will contribute to a better understanding of urban pollution in Thessaloniki and provide essential insights for developing effective environmental management strategies in Greece and other Mediterranean cities. Furthermore, by demonstrating the utility of Syntrichia spp. as a bioindicator, this research will support the adoption of biomonitoring practises in urban air quality management, in line with the European Green Deal’s Zero Pollution Action Plan.

2. Materials and Methods

2.1. Study Area and Moss Sampling

Syntrichia was selected as a bioindicator due to its urban adaptability, tolerance to stressors, efficient HM bioaccumulation, and ease of sampling, making it suitable for assessing urban HM contamination [30,31,32,33,34,35], as previous studies with forest mosses in urban areas north of Greece proved unclear in capturing background levels [22,28]. This study was conducted in the urban area of Thessaloniki, Greece, across various activity zones. Samples were collected from an area covering approximately 130.08 km², as measured by the Sentinel–2 L2A satellite imagery. A total of 16 sites were selected to represent the primary HM contamination sources in Thessaloniki, with consideration given to logistical feasibility (

Table 1. Moss sampling locations and site characteristics in Thessaloniki.

Code	Location	Sampling Samples	Latitude	Longitude	Altitude (m)	Substrate pH
M01	L01	Motorway Thessaloniki-Moudanion	40.54780556	23.0035	86	8.06
M02	L02	Motorway Thessaloniki-Moudanion	40.54780556	23.0035	86	8.06
M03	L03	Motorway Thessaloniki-Moudanion	40.54780556	23.0035	86	8.06
M04	L04	Vassillisis Olgas Avenue	40.5949945	22.9555267	54	8.02
M05	L05	Vassillisis Olgas Avenue	40.5933651	22.9547728	64	7.70
M06	L06	Vassillisis Olgas Avenue	40.5952257	22.9562866	82	7.59
M07	L07	Sindos Industrial Area	40.6878863	22.8225288	48	8.01
M07a	L08	Sindos Industrial Area	40.690109	22.821627	48	8.12
M08	L09	Sindos Industrial Area (Aghialos)	40.6981867	22.7862937	58	8.01
M09	L10	Nea Ionia (Thessaloniki)	40.6885446	22.8536126	45	7.54
M10	L11	Nea Ionia (Monastiriou Road)	40.6737908	22.879135	41	8.04
M11	L12	Politechniou (Thessaloniki)	40.637556	22.934306	−2	7.57
M12	L13	Sindos Industrial Area	40.6774342	22.8129888	47	8.04
M14	L14	Monastiriou (Thessaloniki)	40.6471499	22.9220723	0	7.65
M16	L15	Thessaloniki’s Airport Surrounding Area (Thermi-Thessaloniki)	40.524278	22.998611	7	8.16
M17	L16	Thessaloniki’s Airport Surrounding Area (Thermi-Thessaloniki)	40.52425	22.980528	56	8.14

, Figure 1).

This number of sites allowed us to capture the variability of HMs across different land-use zones and identify potential HM contamination hotspots:

• Motorway (M01–M03 samples): These locations were selected to capture the impact of vehicular emissions, particularly HMs such as Pb, Cd, and Zn, along a major transportation artery [36]. The traffic density data for this motorway indicates high volumes of both passenger and commercial vehicles, making it a significant source of traffic-related air HM contamination in Thessaloniki;
• Industrial zone (M07–M09, M12 samples): This zone includes industries known to emit HMs such as Cr, Ni, and As [37]. These locations were chosen to evaluate the localized impact of industrial emissions on air quality;
• City center (M04–M06, M11, M14 samples): These locations represent areas with high population density and mixed land use, exposed to a combination of traffic emissions, commercial activities, and potentially domestic heating. They were selected to capture the overall urban background levels of HMs and assess the variability in HMs across the city center;
• Road adjacent to oil and fuel terminal (M10 sample): This location was chosen to investigate the potential impact of HMs associated with fuel storage and transport activities. The terminal is a potential source of localized air HM contamination due to the handling and transfer of fuel;
• Airport surroundings (M16–M17 samples): These locations were chosen to assess the contribution of aircraft emissions, specifically HMs, to air quality in the vicinity of the airport.

The selection of these samples aimed to provide a comprehensive assessment of HM contamination affected by diverse urban anthropogenic activities. Figure 1 and Table 1 provide detailed information on the sampling.

2.2. Sampling Methodology

After the GPS coordinates were recorded at every location, two sub-samples, each consisting of moss cushions, were collected from each location within an area of approximately 50 × 50 m. Each sample measured between 3 and 5 cm in diameter and 0.5 to 1.0 cm in thickness. If the diameter was less than 3 cm, two adjacent moss cushions were collected to ensure sufficient biomass. Immediately after collection, each sample was placed in a plastic bag to prevent contamination and was promptly transported to the laboratory for analysis [19,20,21,22,23,24,25,26,27,28,29,38,39].

A total of 192 samples were collected across the main metropolitan area of Thessaloniki, covering 130.08 km², corresponding to 1.48 samples per km². This sampling density is considered adequate based on comparisons with previously published studies, where the sampling number per area is a rather unclear issue. The methodology of the minimum number of moss samples collected per km² in urban areas can vary depending on the studies and its objectives. Natali et al. [40] collected 110 moss samples from gravestones collected across 21 urban and peri-urban cemeteries in the Paris metropolitan area, covering approximately 17,174 km², to trace HM air pollution. Donovan et al. [41] collected 346 samples from Portland, Oregon, which spans approximately 376 km². In Germany, Nickel and Schröder [42] collected moss samples from 400 sites across Germany. Yurukova et al. [25] collected 66 samples (39 in Bulgaria and 27 in Greece) to study the atmospheric deposition in a cross-border area of Southern Bulgaria and Northeastern Greece covering a 20,000 km² area. More recently, Betsou et al. [22] collected H. cupressiforme from 95 sites in Northern Greece to study the atmospheric deposition of trace elements following the ICPV Monitoring manual. In 2021, a very similar study was conducted on the same 95 samples, and 10 additional samples from the Chalkidiki region [28]. In total, 105 samples were collected to represent a monitored area of 52,035 km² in Northern Greece. In this study, urban areas such as major cities were not monitored, due to the methodologies employed.

The 192 samples were collected on 21 March, 19 May, and 18 July 2024, during dry weather to prevent HM contamination. These dates were separated by 60-day intervals and were selected to align with key seasonal transitions. All the samples were collected within a single day during each session:

• March (21 March 2024): Sampling near the end of winter was conducted to capture the effects of heating emissions, particularly from residential and commercial heating systems;
• May (19 May 2024): Sampling occurred in mid-spring when vehicle emissions were at their highest as temperatures started to rise. This period was crucial for estimating the impact of anthropogenic emissions on urban air quality;
• July (18 July 2024): Mid-summer sampling was carried out to observe the effects of higher temperatures, which intensified photochemical reactions. This period also reflected the impact of increased tourism and traffic emissions as the city experienced a surge in visitors.

Sampling at 60-day intervals in Thessaloniki is an effective method for capturing seasonal HM contamination trends, while accounting for the city’s diverse weather patterns and targeting hotspots, including industrial zones and densely populated areas. This sampling schedule allowed us to monitor air quality fluctuations and assess the biomonitoring capacity of Syntrichia. It ensured comprehensive data collection, facilitating the management of air quality by establishing urban monitoring methodologies. This approach is consistent with those applied in similar environments, as demonstrated by Vuǩović et al. [39].

2.3. Sample Preparation and Chemical Analysis

Upon arrival at the laboratory, the samples were cleaned to remove any adhering soil or debris. They were then placed in paper bags, dried, and stored at room temperature (25 °C) until further processing. Before analysis, the moss samples were transferred to Petri dishes and dried in an air oven at 40 °C for 8 h until reaching a constant weight. After drying, each sample was milled using an IKA A11 Analysis Mill at 28,000 rpm for 3:30 min (GTE Technologies, Staufen, Germany) and stored at −18 °C in paper bags until further analysis. The two sub-samples collected from the same location were carefully mixed and homogenized to create a single final sample for analysis [19,43,44,45,46].

Trace metals were analyzed using an Agilent 7850 ICP-MS (Agilent Technologies, Santa Clara, CA, USA) equipped with an ORS4 collision cell. Sample introduction was performed using an Agilent SPS 4 autosampler. The 7850 ICP-MS was set up with the standard ISIS 3 injection system. The IntelliQuant feature in the ICP-MS MassHunter 5.1 software allowed for a full mass-spectrum scan with only an additional two seconds of measurement time. However, sample quantitation was conducted using internal standard seven-point calibration. Sample preparation for analysis adhered to the digestion procedure specified in ISO 17294 Part I and II and APHA 3125 [47,48].

The analytical quality control procedures followed standard protocols to ensure measurement accuracy and precision. Quality assurance included the analysis of procedural blanks and reference materials (RM). We used LGC 7173 (poultry feed) and Fapas 07406 (Trace Elements in liver) as the primary reference material. Method blanks were processed in the beginning of each batch to monitor potential contamination. The recovery rates for the certified elements in the reference material ranged from 96% to 104%. Two independent calibration runs were performed: Run1 showed recovery rates of 90–93%, while Run2 achieved 95–100% recovery, demonstrating excellent measurement repeatability. The relative standard deviation (RSD) for the replicate measurements was 3–11%. The detection limits were calculated as three times the standard deviation of ten replicate blank measurements. The measurement uncertainty was estimated following ISO/IEC Guide 98-3, considering contributions from sample preparation, calibration, and instrumental precision. The expanded uncertainty (k = 2, 95% confidence level) ranged from 9 to 21% for most elements. Internal standards (45Sc, 115In, and 175Lu) were used to correct for matrix effects and instrument drift, maintaining signal stability throughout the analytical runs.

The samples were decomposed in acid under high pressure using a Milestone Ethos Up microwave oven, followed by solution analysis. Initially, a sample amount of 0.5 g was weighed, and HNO₃ and H₂O₂ were added before digestion, gradually increasing the temperature to 210 °C. The digested sample was then diluted and analyzed using ICP-MS. The concentration of elements was calculated using predefined.

2.4. Data Analysis

The contamination factor (CF) was calculated to assess the sample contamination levels and to evaluate their environmental impact. The CF was determined using the following formula:

$$\mathrm{CF}={\displaystyle \frac{\mathrm{C} sample (\mathrm{m}\mathrm{g}/\mathrm{k}\mathrm{g})}{\mathrm{C} background (\mathrm{m}\mathrm{g}/\mathrm{k}\mathrm{g})}}, \mathrm{where}$$

• C sample is the concentration of the contaminant in the sample;
• C background is the concentration of the contaminant in the background, or it is the corresponding contamination level of samples from a clean site (uncontaminated or very slightly contaminated).

To obtain the baseline/background values for the CF calculations, mosses were cultivated in a 60 m² growth chamber under controlled environmental conditions (25 °C, 45–55% RH, 16/8h light/dark photoperiod, 450 μmol m²/s light intensity, 500 ppm CO₂ concentration, and continuous aeration provided by a 60-watt Beurer LV 50 Fresh Breeze air cooler). The segmented mosses were propagated on a commercial substrate for ericaceous plants, composed of a blend of high-grade frozen black sphagnum peat and wood fiber, with a pH of 4.0–5.0, an electrical conductivity of 15 mS/m, and added fertilizer (NPK 12:11:18 + 2) at 0.5 kg/m³. These conditions minimized the external influences and standardized the baseline HM levels. Baseline contamination was influenced by geographic factors such as regional climate, which affected pollutant dispersion; soil composition, impacting metal bioavailability; local vegetation types, influencing pollutant accumulation; and proximity to urban or industrial zones, which intensified deposition. Understanding these factors enriches baseline data reliability and interpretation. Therefore, the low contaminant concentrations in these samples reflected geogenic, natural sources, providing a reliable reference for interpreting urban HM contamination as anthropogenic in origin. After two months, three of the newly grown mosses samples were selected and their biomasses were collected, dried, and stored at −30 °C for further analysis. To evaluate the contamination level of each element, the CF scale, originally proposed by Hakanson (1980) [49], was applied. CF is a standard metric in environmental studies [50,51,52]. A CF classification scale ranging from 1 to 6 was used to interpret the results, defined as follows [50] (

Table 2. Contamination factor classification scale.

1	CF < 1	No (no significant contamination)
2	1 ≤ CF < 2	Suspected (potential contamination)
3	2 ≤ CF < 3.5	Slight (minor contamination)
4	3.5 ≤ CF < 8	Moderate (noticeable contamination)
5	8 ≤ CF < 27	Severe (high level of contamination)
6	27 ≤ CF	Extreme (very high level of contamination)

):

In addition, an attempt was made to collect Syntrichia from a spot, such as the forest of Fyliro (Lat: 40.6941, long: 23.0167, alt: ≈325 m), 9.5 km from the center of Thessaloniki (Aristotelous Square, lat: 40.6320, long: 22.9421). However, this attempt was unsuccessful in obtaining a clean sample since the analysis indicated contamination by several HMs. Thus, it was omitted from the background samples.

Moreover, the EU legislation (Directive 2008/50/EC) on HM air pollution limits could not be applied in this study as it defines the exposure levels of As, Cd, Ni, and Pb on an annual basis in air and in concentrations per m³ (e.g., ng/m³). This regulatory framework does not suit the temporal variability and the specific contamination locations measured in this study, which require more granular, localized environmental assessments.

2.5. Statistical Analysis

Descriptive statistical analysis, including the calculations of mean, median, standard deviation, standard error, and relative standard deviation, were performed using Minitab Statistical v22 and MS Excel. A two-way ANOVA was employed to assess the effects of the month and the sample on HMs. Prior to ANOVA, data were checked for normality using the Shapiro-Wilk test and for the homogeneity of variances using Levene’s test. Multivariate statistical analyses were conducted using RStudio v4.4.1 (FactoMineR) and JMP v18. A Principal Component Analysis (PCA) was employed to reduce the dimensionality of the dataset and identify the major factors contributing to HM variability. A Hierarchical Cluster Analysis (HCA) was used for the group sampling locations based on similarities in their HM profiles, using Ward’s method and Euclidean distance. Hierarchical Clustering on Principal Components (HCPC) was applied to further explore the relationships between elements and sampling. The choice of HCPC over traditional HCA was motivated by its ability to handle potential noise and collinearity in the original data by working with the principal components.

3. Results and Discussion

3.1. Effects of Time and Location on Heavy Metal Bioaccumulation in Syntrichia

A two-way ANOVA was performed to assess the effects of the sampling month and sampling location on the HM concentrations in Syntrichia (

Table 3. Two-way ANOVA of heavy metal concentrations in Syntrichia: effects of sampling month and location.

Elements	Factors	Degrees of Freedom	Mean Squares	F-Statistics	p-Value	Model Adjusted R2
Aluminum	Months	2	1,498,825	1.88	0.155 ns	85.4%
	Samples	15	59,838,161	75.13	0.000 ***
Antimony	Months	2	0.40605	9.00	0.000 ***	83.9%
	Samples	15	2.99085	66.31	0.000 ***
Arsenic	Months	2	21.29	0.86	0.424 ns	60.3%
	Samples	15	502.15	20.35	0.000 ***
Barium	Months	2	487,771	5.99	0.003 ***	56.1%
	Samples	15	1,352,898	16.60	0.000 ***
Cadmium	Months	2	0.3054	1.76	0.175 ns	92.1%
	Samples	15	25.9878	150.02	0.000 ***
Chromium	Months	2	5756.2	13.88	0.000 ***	78.8%
	Samples	15	19,335.9	46.62	0.000 ***
Cobalt	Months	2	44.429	6.25	0.002 ***	57.9%
	Samples	15	126.357	17.79	0.000 ***
Copper	Months	2	409.7	1.39	0.253 ns	84.3%
	Samples	15	20,509.5	69.43	0.000 ***
Lead	Months	2	34,173	11.78	0.000 ***	66.7%
	Samples	15	72,657	25.05	0.000 ***
Nickel	Months	2	2049.75	21.13	0.000 ***	84.5%
	Samples	15	6545.31	67.47	0.000 ***
Vanadium	Months	2	91.804	14.83	0.000 ***	81.6%
	Samples	15	344.046	55.59	0.000 ***
Zinc	Months	2	705,126	1.75	0.177 ns	75.2%
	Samples	15	15,942,944	39.48	0.000 ***

ns = non-significant differences; *** = p-value less than 0.001 (very strong significance).

). Significant differences (p < 0.001) were observed for the factor “Month” for Sb, Ba, Cr, Co, Pb, Ni, and V, indicating that the temporal variation in their concentrations is driven by seasonal variability. This suggests a potential link between HM contamination sources and atmospheric conditions. However, no significant differences (p > 0.05) were observed for Al, As, Cd, Cu, and Zn for the factor “Month”, indicating relatively consistent accumulation in the mosses throughout the sampling period, possibly due to continuous or periodically invariant sources. Highly significant differences (p < 0.001) were observed for all the elements for the factor “Sample” (sampling location), highlighting the substantial spatial heterogeneity of HM contamination. This spatial variation emphasizes the importance of considering local factors, such as proximity to emission sources and microclimatic variations, when assessing contamination patterns.

The adjusted R² values provided a measure of the model’s goodness-of-fit, indicating the proportion of variance in the HM levels explained by the factors “Month” and “Sample”. High adjusted R² values were observed for most of the elements, ranging from 56.1% for Ba to 92.1% for Cd. This indicates that the model effectively captured the variability in HM accumulation, with the month and samples being significant determinants of the HM contamination levels. Lower adjusted R² values for Ba, Co, As, and Pb (56.1%, 57.9%, 60.3%, and 66.7%, respectively) may reflect the influence of other factors not included in the model, such as localized emission sources or specific environmental conditions affecting deposition. Overall, the ANOVA results confirm the reliability of Syntrichia as a bioindicator of HMs. The significant differences detected across the sampling locations and, for some elements, across the months, highlight the ability of mosses to capture both spatial and temporal variations in HM contamination levels.

3.2. Spatial and Temporal Trends of Each Heavy Metal

The concentrations of the 12 HMs in the analyzed Syntrichia samples revealed distinct spatial and temporal patterns. These patterns, summarized below, reflect the complex interplay of urban emission sources, atmospheric processes, and “spring to summer” seasonal influences (Figure 2,

Table 4. Heavy metal concentrations (mg/kg) in Syntrichia samples from 16 locations in Thessaloniki.

Sample/Location	Al	Sb	As	Ba	Cd	Cr	Co	Cu	Pb	Ni	V	Zn
M01/L01	10,544.0	0.18	7.08	78.8	0.17	37.5	5.52	25.2	13.8	32.4	23.1	71.2
M02/L02	3138.8	0.22	1.21	45.7	0.06	9.6	1.22	9.7	3.4	7.9	6.9	40.3
M03/L03	13,387.0	0.13	7.76	90.7	0.15	50.0	6.55	26.8	15.9	36.9	27.3	67.8
M04/L04	6500.0	0.69	4.84	82.7	0.31	51.1	4.30	52.7	49.7	39.5	21.4	587.6
M05/L05	8222.0	1.85	8.92	762.9	0.55	60.3	7.17	111.6	126.2	41.4	22.4	679.2
M06/L06	11,110.4	0.97	7.01	131.4	5.78	65.4	5.75	89.8	176.9	35.3	25.2	3927.4
M07/L07	8825.3	0.58	10.21	137.3	2.41	48.4	6.47	53.0	169.9	27.8	22.7	936.7
M7a/L08	7571.7	0.92	21.53	136.7	2.15	93.4	7.79	76.8	274.1	37.3	27.5	1151.1
M08/L09	8757.5	0.55	23.91	96.1	0.55	54.2	4.97	74.8	44.9	27.3	26.5	216.8
M09/L10	8681.8	0.49	12.42	585.5	0.72	80.4	6.09	102.3	96.1	38.4	26.2	471.7
M10/L11	8482.3	1.01	16.52	1258.1	0.81	144.8	7.27	146.7	154.4	67.9	26.4	729.0
M11/L12	7529.0	1.36	8.61	229.0	0.95	76.0	10.83	112.0	69.4	44.9	22.8	1781.8
M12/L13	9742.9	0.53	11.00	386.4	2.09	65.1	15.41	61.5	143.6	35.9	25.6	3122.0
M14/L14	7933.6	0.06	4.00	54.8	0.30	164.7	9.40	18.3	20.7	114.3	20.2	91.1
M16/L15	7305.3	0.19	4.50	61.8	0.17	27.2	3.87	25.2	8.8	27.8	18.7	65.2
M17/L16	7128.2	0.17	2.22	99.7	0.13	39.3	4.16	17.6	71.4	24.0	15.2	64.3
Mean	8428.7	0.6	9.5	264.9	1.1	66.7	6.7	62.7	90.0	39.9	22.4	875.2
Median	8352.2	0.5	8.2	115.6	0.5	57.3	6.3	57.2	70.4	36.4	23.0	529.6
SD	2233.1	0.5	6.5	335.8	1.5	40.1	3.2	41.3	77.8	23.4	5.4	1152.6
SE	558.3	0.1	1.6	83.9	0.4	10.0	0.8	10.3	19.5	5.8	1.3	288.2
RSD%	25.7	78.3	66.1	122.8	131.8	58.3	47.1	63.8	83.8	56.6	23.1	127.5
Baseline concentrations of the elements in Samples derived from a controlled environment
BgV1	579.8	0.14	0.49	15.2	0.09	2.08	0.42	6.7	1.82	2.90	1.90	2.57
BgV2	530.6	0.14	0.46	15.84	0.08	2.37	0.39	7.2	1.7	2.71	1.88	2.68
BgV3	555.2	0.14	0.42	14.85	0.08	2.44	0.37	6.95	1.55	2.79	2.07	2.31
Mean	555.20	0.14	0.46	15.30	0.08	2.30	0.39	6.95	1.69	2.80	1.95	2.52
SD	20.09	0.00	0.03	0.41	0.00	0.16	0.02	0.20	0.11	0.08	0.09	0.16
SE	14.20	0.00	0.02	0.29	0.00	0.11	0.01	0.14	0.08	0.06	0.06	0.11
RSD%	4.4	0.00	7.7	3.3	6.9	8.3	6.4	3.6	8.0	3.4	5.4	7.5

BgV: Background sample values; SD = standard; SE = standard error; RSD = relative standard deviation.

). CFs, calculated relative to Syntrichia mosses samples BgV1, BgV2, BgV3 (BgV representing Background sample values), were cultivated in a controlled environment (

Table 5. Contamination factors for heavy metals in Syntrichia mosses samples from 16 locations in Thessaloniki.

Sample/Location	Al	Sb	As	Ba	Cd	Cr	Co	Cu	Pb	Ni	V	Zn		CF Scale
M01/L01	19.0	1.3	15.5	5.2	2.1	16.6	13.4	3.6	8.2	11.6	11.9	27.9			No
M02/L02	5.7	1.6	2.7	3.0	0.7	4.3	3.0	1.4	2.0	2.8	3.5	15.8			Suspected
M03/L03	24.1	1.0	17.0	5.9	1.8	22.2	15.9	3.9	9.4	13.2	14.0	26.6			Slight
M04/L04	11.7	5.0	10.6	5.4	3.8	22.7	10.4	7.6	29.5	14.1	11.0	230.4			Moderate
M05/L05	14.8	13.2	19.6	49.9	6.6	26.7	17.4	16.1	74.8	14.8	11.5	266.4			Severe
M06/L06	20.0	7.0	15.4	8.6	69.7	29.0	13.9	13.0	104.9	12.6	12.9	1540.2			Extreme
M07/L07	15.9	4.2	22.4	9.0	29.0	21.4	15.7	7.7	100.7	9.9	11.7	367.3
M7a/L08	13.6	6.6	47.3	8.9	25.9	41.4	18.9	11.1	162.5	13.3	14.1	451.4
M08/L09	15.8	3.9	52.5	6.3	6.6	24.0	12.1	10.8	26.6	9.7	13.6	85.0
M09/L10	15.6	3.5	27.3	38.3	8.7	35.6	14.8	14.8	57.0	13.7	13.4	185.0
M10/L11	15.3	7.2	36.3	82.3	9.8	64.1	17.6	21.2	91.6	24.2	13.6	285.9
M11/L12	13.6	9.7	18.9	15.0	11.5	33.7	26.3	16.2	41.1	16.0	11.7	698.7
M12/L13	17.5	3.8	24.2	25.3	25.2	28.8	37.4	8.9	85.2	12.8	13.1	1224.3
M14/L14	14.3	0.4	8.8	3.6	3.6	73.0	22.8	2.6	12.3	40.8	10.4	35.7
M16/L15	13.2	1.4	9.9	4.0	2.0	12.1	9.4	3.6	5.2	9.9	9.6	25.6
M17/L16	12.8	1.2	4.9	6.5	1.5	17.4	10.1	2.6	42.4	8.6	7.8	25.2
Mean	15.2	4.4	20.8	17.3	13.0	29.6	16.2	9.1	53.3	14.3	11.5	343.2
Median	15.0	3.9	18.0	7.6	6.6	25.4	15.2	8.3	41.7	13.0	11.8	207.7
SD	4.0	3.6	14.2	22.0	17.7	17.8	7.9	6.0	46.1	8.3	2.7	452.0
SE	1.0	0.9	3.6	5.5	4.4	4.4	2.0	1.5	11.5	2.1	0.7	113.0
RSD%	0.3	0.8	0.7	1.2	1.3	0.6	0.5	0.6	0.8	0.6	0.2	1.3

SD = standard; SE = standard error deviation; RSD = relative standard deviation.

) and were used to quantify the degree of anthropogenic HM contamination.

Aluminum (Al): The Al concentrations exhibited a predominantly downward trend from March to July. The samples near major roadways (L01, L03) and the industrial zone (L07–L13) showed particularly notable decreases towards the summer, suggesting a reduction in the emissions from potential winter sources. However, some sites, including those in the city center (L04–L06), displayed less consistent trends, indicating localized influences on Al accumulation. The Al concentrations we obtained were extremely high ranging from 3138.8 mg/kg to 13,387 mg/kg (mean of 8428.7 and median of 8352.2), while high levels were found and in the BgV samples (mean 555.2 mg/kg). Due to the high background levels of the CF values, this indicated moderate to severe Al contamination (5.7–24.1), highlighted significant anthropogenic and combined geogenic contaminations;

Antimony (Sb): The Sb concentrations generally showed a slight rising trend from March to July, although the increase was not uniform across all the locations. This variability suggests complex interactions between the Sb sources, transport mechanisms, and “spring to summer” seasonal factors. The locations near major roads (L01, L04, L06–L08, L10) exhibited more pronounced increases. The Sb concentrations ranged from 0.06 mg/kg to 1.85 mg/kg, which was generally lower than the other HMs. While the overall Sb ranged from no contamination to moderate (CF: 0.4–13.2), samples L05 and L12, located near industrial areas, displayed severe contamination, indicating the potential hotspots of Sb emissions. These elevated levels contrasted with the lower values found in the less urbanized regions of Northern Greece [22,23,25,28], reinforcing the role of urban activities in Sb accumulation [53,54];

Arsenic (As): The As concentrations exhibited high variability among the studied HMs, both spatially and temporally, with no consistent overall trend observed. Some locations exhibited decreases, while others showed increases from March to July. This suggests a complex interplay of sources influencing As accumulation, including both seasonally varying and constant emissions. The As and the proximity of some sampling locations (L07–L10, L13) to industrial areas could explain the higher As levels observed [55,56]. The As concentrations ranged from 1.21 mg/kg to 23.91 mg/kg. The CF values, ranging from 2.7 to 52.5, indicated widespread contamination, with many locations showing severe to extreme contamination, highlighting a significant environmental concern. These findings underscore the need for further investigation into the sources and pathways of As contamination in this region. The higher levels compared to the less urbanized areas of Northern Greece [22,23,25,28] point towards stronger anthropogenic influence in Thessaloniki;

Barium (Ba): Analogous to Al, Ba showed a general decreasing trend from March to July, most pronounced near major roads and in the industrial zone. This suggests a reduction in the contributions from winter-related sources. However, some locations deviated from this pattern, indicating the site-specific influences on Ba accumulation. The Ba concentrations ranged widely, from 45.7 mg/kg to 1258.1 mg/kg. The CF values revealed slight to extreme contamination (3.0–82.3), with the samples collected near industrial areas (L10, L11, L13) exhibiting extreme contamination. These higher concentrations compared to previous reports in Northern Greece [27,28] suggest that urban activities are driving Ba accumulation in Thessaloniki;

Cadmium (Cd): The Cd concentrations displayed significant temporal stability from March to July, suggesting consistent Cd inputs throughout the study period; likely sources include non-seasonal activities. While the Cd concentrations were generally low, ranging from 0.06 mg/kg to 5.78 mg/kg, the elevated CF values highlighted substantial variation in the contamination levels, ranging from no contamination to extreme (0.73–69.65). This range, considerably broader than that reported in West Macedonia [24], suggests a greater influence of urban sources in Thessaloniki, underscoring the role of localized emissions and urban-specific activities in shaping Cd accumulation patterns;

Chromium (Cr): Cr displayed a pronounced increasing trend from March to July at numerous locations, particularly near roads and industrial areas, suggesting a link between Cr accumulation and increased industrial activity and traffic emissions during warmer months. This pattern aligns with the observations from other European urban biomonitoring studies [7]. The Cr concentrations ranged from 9.6 mg/kg to 164.7 mg/kg. The CF values, predominantly in the severe to extreme range (4.3–73.0), highlighted substantial Cr contamination across most of Thessaloniki, exceeding the levels reported in earlier studies in Northern Greece [22,27,28]. This discrepancy emphasizes the significant urban influence on Cr accumulation. A study in 2011 by Sawidis et al. [57] attributed part of the elevated Cr levels in Thessaloniki’s city center to local geochemistry; however, the proximity of their control site to potential anthropogenic sources might need deeper investigation to confirm the levels of the geogenic contributions, such as the existence of ultramafic rocks;

Cobalt (Co): Co exhibited diverse temporal trends, with decreasing concentrations observed in some samples, while other locations showed substantial increases. This suggests the combined influence of decreasing winter-related emissions and increasing summer-related emissions. The higher values observed in industrial areas (L07–L011, L13) support the hypothesis of industrial contributions. The Co concentrations ranged from 1.22 mg/kg to 15.41 mg/kg. CFs, primarily in the severe range (3.0–37.4), indicate substantial Co contamination. These levels generally exceeded those reported in past research in Greece [22,27,28], further supporting the hypothesis of urban activities contribution;

Copper (Cu): The Cu concentrations generally increased from March to July at numerous locations, particularly those near roads, aligning with the studies linking elevated Cu levels to traffic congestion [5]. Furthermore, industrial emissions are also known sources of Cu [58], and this might contribute to the elevated concentrations observed in the industrial zone (L07–L10, L13). The Cu concentrations ranged from 9.7 mg/kg to 146.7 mg/kg. The CF values revealed the ranges from suspected to severe Cu contamination (1.4–21.2). The observed levels generally exceeded those reported in prior studies in Greece [23,24], further indicating substantial contamination in the urban environment;

Lead (Pb): The Pb concentrations exhibited mixed temporal trends, with increasing levels observed at some locations and decreasing levels at others, indicating the influence of multiple factors. The Pb concentrations ranged from 3.4 mg/kg to 274.1 mg/kg. The CFs were predominantly in the extreme range (2.0–162.5), indicating widespread and severe Pb contamination across Thessaloniki. This aligns with the studies highlighting the persistence of Pb contamination in urban environments, despite the phase-out of leaded gasoline, attributing this to legacy sources, industrial activities, and the resuspension of contaminated dust [59,60,61,62,63,64,65,66,67,68,69];

Nickel (Ni): Ni exhibited a predominantly decreasing trend from March to July, analogous to Al and Ba. This suggests a reduction in winter-related emissions as a contributing factor. The samples collected from locations near major roadways and in the industrial zone displayed the most prominent decreases, further suggesting an association between traffic emissions and industrial activities. The Ni concentrations ranged from 7.9 mg/kg to 114.3 mg/kg. The CF values, primarily in the severe to extreme range (2.8–40.8), highlighted substantial Ni contamination. This, combined with the higher Ni levels in our study compared to earlier studies in Greece [22,23,27,28], suggests stronger anthropogenic contributions;

Vanadium (V): The V concentrations showed mixed temporal trends, increasing at some sampling locations and decreasing at others. This suggests the influence of both increasing summer-related emissions and decreasing winter-related emissions. The highest value being observed near the oil terminal (L11) suggests the contribution of fuel combustion and related industrial processes as significant V sources [70,71,72]. The V concentrations ranged from 6.9 mg/kg to 27.5 mg/kg. The CF values ranged from the moderate to the severe range (3.5–14.1), highlighting substantial V contamination that generally exceeded the levels reported in prior research in Greece [22,27,28];

Zinc (Zn): The Zn concentrations exhibited the most complex temporal trends among the studied HMs, with increasing, decreasing, and fluctuating patterns observed across the different samples. This complexity underscores the multitude of factors influencing Zn accumulation in the urban environment. The extremely high Zn levels recorded in the city center (L04–L06, L12) and in industrial areas (L07–L10, L13) point towards the contribution of traffic-related emissions [73], industrial activities, and other urban sources. The Zn concentrations ranged widely, from 40.3 mg/kg to 3927.4 mg/kg. The CF values, predominantly in the extreme range (15.8–1540.2), indicated widespread and substantial Zn contamination, exceeding the ranges reported in prior studies in Greece [23,24,27,28]. This emphasizes the significant contribution of urban activities to Zn accumulation in the Syntrichia within Thessaloniki.

Our findings highlight the significant impact of urbanization on HM accumulation in Thessaloniki, mirroring trends observed in other European urban areas where traffic and industrial activities dominate as primary contamination sources. However, the unique characteristics of Thessaloniki’s industrial profile, traffic patterns, and meteorological conditions contribute to city-specific HM accumulation patterns.

3.3. Monthly Variation Patterns

The results revealed a complex interplay of factors influencing HM deposition, with distinct “spring to summer” seasonal patterns emerging for several elements (Figure 2).

Decreasing trend from spring to summer: A general decrease in the concentrations of Al, Ba, and Ni from March to July was observed. This “spring to summer” seasonal pattern may be linked to specific HM contamination sources or atmospheric conditions. Increased rainfall leading to the washout of atmospheric contaminants, or changes in fuel consumption patterns could contribute to this trend. Similar reductions in HM concentrations due to “spring to summer” washout effects have been observed in Thessaloniki, where rain effectively removes atmospheric particles during the cooler months, resulting in cleaner air during this period [74];

Increasing trend from spring to summer: In contrast, Cr and, to a lesser extent, Sb and Cu, exhibited a general increase from March to July. This pattern could be attributed to factors such as increased traffic volume during the summer months, leading to higher emissions from vehicle exhaust and tire wear, or to increased industrial activities during this period. Similar patterns have been documented in other Mediterranean regions, where warmer and drier conditions in the summer exacerbate the accumulation of HMs, as reduced precipitation and increased atmospheric stability hinder the dispersion of pollutants [75,76];

Mixed or location-specific trends: The remaining HMs, such as As, Cd, Co, Pb, V, and Zn, displayed more complex or location-specific trends. Numerous factors, such as traffic patterns, industrial activities, proximity to specific emission sources, and local atmospheric conditions, likely influenced these elements from diverse sources. Studies from Mediterranean cities, such as Bilbao and Rome, underscored the impact of localized factors on HM levels, particularly for elements like Pb and Zn that vary with emission source proximity and topographical features [77];

Temporal HM trends in Syntrichia influenced by biological and meteorological factors: The decreasing Al, Ba, and Ni levels could be due to increased spring rainfall causing a washout effect, removing these HMs from the atmosphere before moss uptake [74]. This aligns with the studies showing reduced HM levels in various matrices during wetter periods. Conversely, increasing Cr, Sb, and Cu might be linked to increased traffic and industrial activities in warmer months, consistent with trends in other Mediterranean areas [75,78,79]. Dry summer conditions can exacerbate these trends by promoting the resuspension of metal-laden dust [77,80]. The mixed trends for As, Cd, Co, Pb, V, and Zn suggest complex interactions. The biological uptake by Syntrichia can be influenced by temperature and humidity, impacting moss physiology and metal absorption [81]. Wind patterns and sea breezes can also influence HM transport and deposition [76]. Further research incorporating meteorological data and analyzing Syntrichia’s uptake mechanisms is needed. Comparisons to other biomonitors like Nerium oleander and Pinus sylvestris, with different seasonal accumulation patterns, could further elucidate Syntrichia’s responses to environmental pressures [79,82].

3.4. Assessment of Heavy Metal Contamination in Thessaloniki Using Contamination Factors

The HM contamination in Thessaloniki was assessed using CFs, calculated relative to Syntrichia cultivated in a controlled environment. The CFs exhibited substantial spatial variability, ranging from minimal to extreme, highlighting the influence of localized anthropogenic inputs (Table 5). This heterogeneity underscores the need to consider local emission sources in pollution management strategies. While the controlled Syntrichia provided a consistent baseline, this approach may not fully represent the natural background levels potentially influenced by geological and soil variations. The unsuccessful attempt to collect background samples from Fyliro forest, approximately 10 km from Thessaloniki’s center and 325 m above sea level, emphasizes the pervasive nature of HM contamination. Future research should prioritize establishing true background levels using multiple, carefully selected locations with certified low contamination, representing natural/geogenic emissions. This will refine the CF interpretations and provide a more accurate contamination assessment. Despite this limitation, the present study’s spatially resolved CF analysis offers a unique contribution to understanding HM contamination in Thessaloniki, absent in previous broader regional or methodologically different studies.

3.5. Potential HM Contamination Sources in Thessaloniki and Their Contribution to Heavy Metal Accumulation in Syntrichia

The above-mentioned results reveal the elevated levels of several HMs, indicating significant anthropogenic activities in the urban region. While the precise contribution of each source requires further investigation using source apportionment techniques, analyzing the spatial and temporal trends of the individual elements, combined with the knowledge of local emission sources, allows us to identify potential key contributors and their likely roles in the observed contamination patterns.

Traffic-related emissions: Vehicular traffic emerges as a dominant contamination source, impacting the accumulation of multiple HMs in Syntrichia. Several elements, including Sb, Cu, Cd, Pb, and Zn, exhibit strong positive correlations and elevated concentrations in areas with high traffic density, such as near major roadways (L1–L6, L11, L12 and L14) [52,53,56,57,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,82,83,84,85,86]. Brakes and tire wears are significant contributors to Sb, Cd, Cu, Pb, and Zn, releasing these metals into the environment as particulate matter, which can easily be deposited onto moss surfaces [58,59,60,61,62,63,64,65,66,67,68,69]. Exhaust emissions, particularly from diesel vehicles, are another source of Cu, Pb, and Zn [58]. The elevated concentrations of these elements in the samples collected near major roadways and in the city center (L4–L6, L12, L14) corroborate the significant influence of traffic emissions. The persistent presence of Pb, despite the phase-out of leaded gasoline, suggests the long-term impact of historical traffic emissions and the ongoing resuspension of Pb-contaminated dust [59,60,61,62,63,64,65,66,67,68,69];

Industrial activities: Industrial emissions play a substantial role in HM contamination, contributing to elevated levels of Al, As, Ba, Cr, Co, Cu, Ni, V, and Zn [86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104]. The spatial distribution of these elements points to localized industrial sources. For example, high As concentrations near industrial zones (L7–L10, L13) are likely associated with metal smelting and other industrial processes that release As–bearing compounds [54,96]. The elevated levels of Cr and Ni, particularly at location L15, suggest the influence of a specific source, such as metal processing or combustion activities [41,42,43,44,45]. The high Ba levels in several locations (L5, L6, L10, L11, L13) imply industrial emissions from processes involving Ba minerals [56,97,98]. The industrial zone (L7–L10, L13) and the area adjacent to the oil and fuel terminal (L11) exhibited high concentrations of various HMs, including Cu, V, and Zn, implying industrial processes, fuel combustion, and related activities as significant contributors [69,70,71,72,99,100]. The presence of V, often associated with oil combustion and industrial processes, further supports the influence of industrial activities, particularly near the oil and fuel terminal (L11) [69,70,71,72];

Other anthropogenic sources: While traffic and industrial activities appear to be the dominant contributors, other anthropogenic sources also may play a role. Agricultural practices, such as the use of fertilizers and pesticides, can contribute to elevated levels of Ba, Cd, and Cu [98,105,106]. The use of coal for heating and other purposes can contribute to Al, As, and V contamination [70,95,96]. Waste incineration is another potential source of As and Co [54,96]. Construction activities and the use of Al-containing products can contribute to Al levels [91,92,93,94,95,96] and are associated with lignite mining and coal-fired power plants, particularly in West Macedonia regions [107,108,109];

Natural background and long-range transport: While anthropogenic sources are the primary drivers of HM contamination, natural background levels and long-range transport can also contribute. The unsuccessful attempt to collect low contaminated Syntrichia from the Fyliro forest, despite its distance from the city center, suggests the pervasive nature of HM contamination and the potential for the long-range transport of contaminants from regional or even distant sources. Geological factors and soil composition can also influence background HM concentrations, although their contribution is generally lower than that of the anthropogenic sources in urban environments, as seen with geogenic Cr contributions in Macedonia (e.g., ultramafic rocks), such as groundwater (0.5 to 131.5 μg/L) [110] and in soils (115–959 mg/kg), with Cr(VI) in groundwater reaching up to 120 μg/L [111]. These contributions under dry and windy conditions can be a source of Cr in the atmosphere. Further research is needed to quantify the contribution of natural background and long-range transport to the observed HM levels in Thessaloniki;

Interactions and synergistic effects: It is important to acknowledge that these sources often interact and exert synergistic effects. For instance, industrial emissions can be transported by wind and traffic, leading to wider contamination patterns. Rainfall can wash out atmospheric contaminants, depositing them onto moss surfaces. Furthermore, the bioavailability of HMs can be influenced by various environmental factors, including pH, organic matter content, and the presence of other contaminants. Understanding these interactions is crucial for developing comprehensive pollution control strategies.

3.6. Multivariate Analysis of Heavy Metal Contamination Patterns

A Multivariate Analysis was performed using Principal Component Analysis (PCA), Hierarchical Clustering Analysis (HCA), and Hierarchical Clustering on Principal Components (HCPC).

The PCA biplot (Figure 3) and the HCA dendrogram (Figure 4) provide insights into the contamination landscape by revealing the distinct groupings of the elements and the sampling locations, and their interrelationships. The length and direction of the vectors in the biplot represent the contributions of the HMs to the principal components (PCs). Cu, V, Pb, Sb, Co, As, Zn, Ba, Cr, and Cd exhibited strong positive loadings on PC1, suggesting a potential association with a dominant contamination source, such as traffic emissions or industrial activities. Ni and Cr loaded strongly on PC2, suggesting a distinct source, potentially related to metal processing or combustion processes. The interpretation of the combined clustering analysis is presented below.

• Cluster 1: Encompassing locations L2, L4, L15, and L16, this is characterized by relatively low HM concentrations, potentially representing less polluted areas or background levels. Specifically, this cluster exhibits low concentrations of V, Al, and Co, consistent with minimal anthropogenic influence;
• Cluster 2: Encompassing locations L1 and L3, this is characterized by high Al levels, suggesting a combined influence of traffic and industrial activities;
• Cluster 3: primarily associated with location L14, this is distinguished by exceptionally high Ni and Cr levels, strongly indicating a unique and localized source;
• Cluster 4: encompassing locations L7, L8, L9, and L10, this is characterized by high As levels, suggesting the influence of nearby distinct sources, such as agricultural runoff or industrial discharge;
• Cluster 5: encompassing locations L5, L11, and L12, this is characterized by elevated levels of Sb, Cu, and Ba, suggesting potential influences from vehicular emissions and related industrial activities;
• Cluster 6: encompassing locations L6 and L13, this is distinguished by exceptionally high Zn and Cd levels, likely associated with intense traffic, industrial emissions, or waste disposal activities.

PC1 accounts for the largest proportion of variance (41.07%) and exhibits strong positive correlations with Cu (0.839), V (0.793), Pb (0.765), Sb (0.667), Co (0.645), As (0.635), Zn (0.631), Ba (0.603), Cr (0.594), and Cd (0.572). These elements, typically associated with traffic emissions and industrial activities, are major contributors to the observed variability in the dataset. PC2, accounting for 17.61% of the variance, is primarily influenced by Ni (0.780) and Cr (0.689), elements commonly associated with metal processing and combustion processes. This distinct association suggests a potential source distinct from traffic and general industrial activities, possibly related to specific industries or localized emissions. The contribution values of each element to each PC further elucidates their relative importance in defining the PCs. For example, Cu (14.29%), V (12.76%), Pb (11.88%), and Sb (9.03%) are the largest contributors to PC1, reinforcing their association with traffic and industrial activities as a dominant source of HM contamination. Similarly, Ni (28.78%) and Cr (22.49%) are the largest contributors to PC2, confirming their significant role in defining a separate contamination source, likely associated with metal processing or combustion processes.

The combined grouping of elements and locations provides further insights. The strong positive loadings of Cu, V, Pb, Sb, Co, As, Zn, Ba, Cr, and Cd on PC1, coupled with the elevated levels of these elements in Clusters 4, 5, and 6, strongly implicate traffic and industrial activities as major contributors to HM contamination in Thessaloniki. The strong association of Ni and Cr with PC2 and their high concentrations in Cluster 3 suggests a localized source. The elevated As levels in Cluster 4 point to a potential agricultural or industrial source specific to those locations.

The HCPC heatmap (Figure 5) illustrates the spatial heterogeneity of HM contamination. It reveals complex contamination patterns, highlighting distinct clusters of elements, locations, and their combined associations, which can inform the development of targeted pollution control strategies. A key observation is the strong positive correlation among several elements in PC1, including Cu, V, Pb, Sb, Co, As, Zn, Ba, Cr, and Cd. This clustering suggests that these elements likely originate from similar sources. The co-occurrence of these elements suggests that traffic is a likely dominant contamination source at many of the sampled locations.

In contrast, Ni and Cr form a distinct cluster, exhibiting a strong positive correlation with each other but limited association with the other metals. This suggests a more specific industrial source, such as metal refining or alloy production. This unique correlation highlights the influence of localized industrial activities, which may explain the isolated high concentrations of these metals observed in certain locations, such as L14. The distinct contribution of these elements to PC2 further differentiates the areas impacted by these metals from those primarily influenced by traffic-related contamination, which is more closely associated with PC1. Locations where PC2 loadings are high are likely to experience the impact of concentrated industrial activities. For instance, high PC2 values suggest proximity to industrial zones or specific emission sources, rather than the diffuse contamination patterns associated with general urban or vehicular emissions. Negative correlations were also observed, notably involving Zn and Cd in certain areas. This indicates that the behavior of these metals may differ in specific locations due to factors such as environmental conditions (e.g., natural remediation or attenuation) or variations in HM contamination sources.

3.7. Comparison with Previous Studies of Heavy Metal Contamination in Thessaloniki and in Other Mediterranean Urban Areas

Numerous studies have investigated HM contamination in Thessaloniki and across the Mediterranean region, providing a historical context for comparison with the 2024 findings. Several key aspects emerge from these investigations:

Traffic as a major pollutant source: The present study identified traffic as a major contributor to elevated levels of Sb, Cd, Cu, Pb, and Zn in Thessaloniki. This aligns with previous studies in Thessaloniki that highlighted traffic as a dominant source of urban air pollution, potentially contributing up to 80% of PM10, NOx, and HM pollution. The significant contribution of diesel vehicles to SO₂ and particulate matter emissions underscores the importance of traffic-related sources [58,112,113]. Other Mediterranean cities also experience similar traffic-related HM pollution. Studies in Coimbra, Portugal showed increased heavy metal levels in road dust during the summer, attributed to heightened tourism-related traffic [78], while research in Kocaeli, Turkey, found elevated HM concentrations in PM near traffic-dense zones [114]. The present study’s findings reinforce the importance of addressing traffic emissions as a key driver of HM pollution in Thessaloniki and across the Mediterranean.

Industrial activities and emissions: The 2024 study found that industrial activities likely contributed to elevated levels of Al, As, Ba, Cr, Co, Ni, V, and Zn in Thessaloniki, with localized hotspots near industrial zones. This is consistent with previous research in Thessaloniki that identified industrial emissions as a key source of PM10 and specific HMs like Pb, Zn, and Sb [36,115]. The spatial distribution of these elements in our study further supports the influence of localized industrial sources. Similar to Thessaloniki, industrial emissions are a major HM pollution source in other Mediterranean cities. Research in southern Italy has also attributed high levels of Pb and Cr to both local industrial emissions and vehicle exhaust [116,117]. Studies on atmospheric deposition using bioindicators in European cities, including Thessaloniki, have highlighted increased HM accumulation during warmer months in areas with heavy traffic and industrial emissions [57], further emphasizing the role of industrial activities in the regional context.

Maritime traffic and port activities: While not explicitly assessed in the 2024 Syntrichia study, the influence of maritime activities on HM contamination is relevant for Thessaloniki as a port city. In other Mediterranean port cities like Lampedusa and Marseille, studies have shown that ship emissions, particularly from the combustion of heavy fuel oil, significantly contribute to elevated Ni and V levels in the atmosphere, particularly during peak tourist seasons when ship traffic is highest [118]. A study in 2022 in Istanbul revealed a strong correlation between the density of ship traffic and elevated PM10 levels, including metals like Ni and V, commonly associated with ship emissions [119]. The potential contribution of maritime traffic to HM levels in Thessaloniki requires further investigation in future studies, especially given its coastal location and active port.

Spring to summer seasonal variations: The 2024 Syntrichia study observed “spring to summer” trends for several HMs, including decreases in Al, Ba, and Ni, and an increase in Cr, from spring to summer. These patterns align with previous findings in Thessaloniki, where higher pollution levels, including elevated concentrations of wood smoke tracers and HMs, have been observed during colder months, potentially due to increased biomass burning for heating and less favorable meteorological conditions for pollutant dispersion [38,115]. Aslanidis Et Al. [120] studied the urban soil HMs in Volos, Greece. They found increased HM contamination in the urban soils due to vehicular and industrial sources, with concentrations spiking in warmer months. Reduced solar radiation during winter due to increased heating emissions and decreased sea breezes could also influence atmospheric conditions and affect HM deposition [121]. Similarly, studies in the eastern Mediterranean have noted higher pollutant concentrations in summer due to reduced ventilation and stable atmospheric conditions, suggesting that “spring to summer” meteorological factors play a significant role in HM pollution dynamics across the region [122]. While our Syntrichia study observed some increasing HM trends from spring to summer, these might reflect specific source Variations (e.g., Increased traffic or industrial activities) rather than solely meteorological influences. Prior research using tree bark and leaves as bioindicators in European cities, including Thessaloniki, showed increased accumulation during warmer months, aligning with our observations of seasonally elevated levels [57].

The elevated levels in 2024 of As, Cd, Cr, Pb, and Zn are consistent with earlier reports [36,57,113], reinforcing the persistent nature of HM contamination in the city. The identification of traffic and industrial zones as contamination hotspots corroborates previous studies highlighting the contribution of these sources to urban air HM contamination. The “spring to summer” trends observed in the 2024 data, such as the decreasing pattern for Al, Ba, and Ni, and the increasing trend for Cr, are also consistent with previous findings regarding seasonal fluctuations. The absence of a strong temporal trend for some elements, like As, suggests a more continuous or less seasonally variable source. Furthermore, our findings agree with the previous studies that raised concerns about the air quality of Thessaloniki [123]. Thus, regarding the continuous poor air quality issue, the European Commission, in December 2020, decided to take legal action against Greece by referring the country to the European Court of Justice for the substandard PM10 air quality of Thessaloniki [124]. However, our findings for the period between spring to summer 2024 suggest that the issue of air quality is still persistent.

3.8. Comparative Effectiveness of Syntrichia Moss Versus Other Bioindicators in Urban HM Detection

Syntrichia moss, commonly used in biomonitoring, offers distinct advantages and limitations as an indicator for HM contamination in urban areas. It efficiently accumulates metals due to its high surface area and lack of root systems, which makes it directly dependent on atmospheric deposition for nutrient uptake, allowing it to precisely reflect airborne contamination levels [125]. Additionally, Syntrichia’s adaptability to varying urban environments, coupled with its low maintenance, makes it a convenient option for continuous monitoring [126].

Comparatively, other mosses such as Pleurozium schreberi have demonstrated consistent HM accumulation across urban areas and can indicate deposition trends more accurately than Syntrichia, particularly when employed with techniques like the moss–bag method [126]. Lichens and tree bark are also robust bioindicators, with tree bark showing year-round HM capture. In contrast, Syntrichia may offer quicker accumulation results and thus serves better in high-traffic urban areas where frequent monitoring is essential [127]. However, while Syntrichia is sensitive to the heavy traffic emissions common in Thessaloniki, it might be less effective than tree-based bioindicators in long-term HM retention [74].

Uniquely, Syntrichia moss’s adaptability to harsh, high-contamination environments and its ease of deployment offers a low-cost, efficient solution for quick HM assessments in Thessaloniki. However, its effectiveness in capturing fluctuating seasonal or localized industrial emissions may require pairing with other indicators to provide a more comprehensive picture of urban HM levels [125].

4. Implications for Public Health

Elevated concentrations of As, a known carcinogen, were found in several samples, particularly near industrial zones and major roadways. This also raises concerns regarding the increased risk of skin lesions, cardiovascular and respiratory effects, and cancer [128]. Cd also threatens kidney function and bone health, particularly for vulnerable populations [129,130,131]. Cr, primarily in its toxic hexavalent form [Cr(VI)], increases the risk of respiratory problems, skin irritation, and lung cancer [132]. Pb, despite the phase-out of leaded gasoline, remains a persistent contaminant that poses significant neurotoxic risks, especially to children [59,60,61,62,63,64,65,66,67,68,69]. Elevated Zn levels raise concerns regarding potential respiratory and immunological effects [133].

These findings underscore the need for integrated HM control strategies in highly urbanized and industrial areas. The stricter enforcement of industrial emission regulations, particularly for industries releasing As, Cd, Cr, Pb, and Zn, is crucial. Promoting cleaner production technologies and transitioning toward sustainable transportation options would reduce traffic-related emissions. Improved waste management practices, including the proper disposal of electronic waste and batteries, are essential to minimize HM release.

Public health interventions should focus on raising awareness regarding the health risks of HM exposure and promoting preventative measures. The regular biomonitoring of HM levels in vulnerable populations could help identify individuals at higher risk and enable timely interventions. Educational campaigns should inform residents about the ways to minimize exposure, such as reducing outdoor activities during peak contamination periods and consuming a balanced diet rich in antioxidants. Collaboration among environmental agencies, public health officials, and local communities is essential to develop and implement effective strategies to mitigate HM contamination and protect public health. This integrated approach, incorporating both pollution control and public health measures, is crucial for creating a healthier and more sustainable urban environment.

5. Conclusions

This study confirms Syntrichia moss as a valuable bioindicator for assessing urban HM contamination, revealing distinct spatial and temporal patterns in Thessaloniki. Elevated HM levels, particularly As, Cd, Cr, Pb, and Zn, were concentrated in high-traffic and industrial zones, often exceeding previously reported levels and reaching severe to extreme contamination categories. This underscores the significant impact of localized anthropogenic emissions on urban air quality, a finding with implications for other Mediterranean cities. The observed seasonal variations, with decreases in Al, Ba, and Ni and increases in Cr and Cu during the spring-to-summer period, suggest complex interactions between emission sources, atmospheric processes, and moss physiology.

Multivariate analyses identified traffic and industrial activities as key contributors to HM accumulation, providing critical information for targeted pollution mitigation strategies. While the controlled environment for baseline Syntrichia samples offers a valuable reference, future research should prioritize establishing the true background levels from multiple uncontaminated sites to refine contamination factor interpretations. Further investigation into source apportionment using receptor models, combined with long-term monitoring, assessing HM bioavailability, and exploring additional bioindicator species would enhance our understanding of urban HM pollution dynamics and their impact on public health.

This research highlights the importance of integrating biomonitoring into urban environmental management frameworks, providing data-driven insights for developing sustainable solutions and contributing to the goals of the European Green Deal’s Zero Pollution Action Plan. The unique insights from this spatially and temporally resolved analysis of HM contamination in Thessaloniki using Syntrichia offer a crucial foundation for developing more effective strategies to protect urban ecosystems and public health in the face of increasing anthropogenic pressures.