[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Next Article in Journal
Characterization of a Major Quantitative Trait Locus for the Whiteness of Rice Grain Using Chromosome Segment Substitution Lines
Previous Article in Journal
Antifungal Activity of Ethanolic Extracts from Aeroponically Grown Cape Gooseberry (Physalis peruviana L.) with LED Lights and In Vitro Habituated Roots
Previous Article in Special Issue
Major and Trace Airborne Elements and Ecological Risk Assessment: Georgia Moss Survey 2019–2023
You seem to have javascript disabled. Please note that many of the page functionalities won't work as expected without javascript enabled.
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Plants
Volume 13
Issue 24
10.3390/plants13243587
plants-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Strawberry Plant as a Biomonitor of Trace Metal Air Pollution—A Citizen Science Approach in an Urban-Industrial Area near Lisbon, Portugal

by
Carla A. Gamelas
1,2,*,
Nuno Canha
1,3,*,
Ana R. Justino
1,
Alexandra Nunes
1,
Sandra Nunes
4,5,
Isabel Dionísio
1,
Zsofia Kertesz
6 and
Susana Marta Almeida
1
1
Centro de Ciências e Tecnologias Nucleares, Instituto Superior Técnico, Universidade de Lisboa, Estrada Nacional 10, Km 139.7, 2695-066 Bobadela, Portugal
2
Instituto Politécnico de Setúbal, Escola Superior de Tecnologia de Setúbal, Campus do IPS, Estefanilha, 2914-508 Setúbal, Portugal
3
HyLab—Green Hydrogen Collaborative Laboratory, Estrada Nacional 120-1, Central Termoelétrica, 7520-089 Sines, Portugal
4
Instituto Politécnico de Setúbal, Escola Superior de Ciências Empresariais, Campus do IPS, Estefanilha, 2914-508 Setúbal, Portugal
5
Center for Mathematics and Applications, NOVAMATH, Universidade Nova de Lisboa, 2829-516 Caparica, Portugal
6
HUN-REN Institute for Nuclear Research (ATOMKI), 4026 Debrecen, Hungary
*
Authors to whom correspondence should be addressed.
Plants 2024, 13(24), 3587; https://doi.org/10.3390/plants13243587
Submission received: 14 November 2024 / Revised: 5 December 2024 / Accepted: 15 December 2024 / Published: 23 December 2024
(This article belongs to the Special Issue Biodiversity of Plants and Their Use in Biomonitoring and Environmental Protection)
Browse Figures
Graphical abstract
"> Figure 1
<p>Location of the study area: (<b>left</b>) at a national level; (<b>middle</b>) spatial distribution of the strawberry plants that could be retrieved after the exposure period (green dots) and the reference background site (red dot); (<b>right</b>) location of industries A, B, C, and D.</p> "> Figure 2
<p>Modified coefficient of variation (standard deviation divided by the median) of element concentrations in strawberry leaves.</p> "> Figure 3
<p>Accumulation Factor (AF) of the elements in exposed strawberry leaves. In the box plot, the square represents the mean, upper and lower times sign (×) represent the maximum and minimum values, and the whiskers extend to 1.5* the interquartile range. Below green line—no accumulation; green to orange line—minimal accumulation; orange to red line—moderate accumulation; above red line—significant accumulation.</p> "> Figure 4
<p>Enrichment factor (EF) of the elements in exposed strawberry leaves. Above the red line, anthropogenic sources are present.</p> "> Figure 5
<p>Spatial distribution of element concentrations in the exposed strawberry leaves in the study area (in mg·kg<sup>−1</sup>) related to (<b>above</b>) crustal natural origin, (<b>middle</b>) industry/steelworks, and (<b>below</b>) traffic.</p> "> Figure 6
<p>Mean elemental concentrations found in the exposed strawberry leaves versus the distance to steelworks B.</p> ">
Versions Notes

Abstract

:
A biomonitoring study of air pollution was developed in an urban-industrial area (Seixal, Portugal) using leaves of strawberry plants (Fragaria × ananassa Duchesne ex Rozier) as biomonitors to identify the main sources and hotspots of air pollution in the study area. The distribution of exposed strawberry plants in the area was based on a citizen science approach, where residents were invited to have the plants exposed outside their homes. Samples were collected from a total of 49 different locations, and their chemical composition was analyzed for 22 chemical elements using X-ray Fluorescence spectrometry. Source apportionment tools, such as enrichment factors and principal component analysis (PCA), were used to identify three different sources, one geogenic and two anthropogenic (steel industry and traffic), besides plant major nutrients. The spatial distribution of elemental concentrations allowed the identification of the main pollution hotspots in the study area. The reliability of using strawberry leaves as biomonitors of air pollution was evaluated by comparing them with the performance of transplanted lichens by regression analysis, and a significant relation was found for Fe, Pb, Ti, and Zn, although with a different accumulation degree for the two biomonitors. Furthermore, by applying PCA to the lichen results, the same pollution sources were identified.

Graphical Abstract">

Graphical Abstract

1. Introduction

Trace metals in atmospheric particulate matter (PM) are of particular interest given their effect on human health [1]. However, only a small number of air monitoring stations measure metal concentrations, and with a poor spatial coverage. In this context, biomonitoring offers advantages, since biomonitors can be spatially distributed and have a low cost [2].
The most commonly used biomonitors are lichens [3], but plant leaves have also been widely used, because they are efficient collectors of atmospheric particles [4] and due to their widespread presence in terrestrial ecosystems [5,6,7,8,9,10,11,12].
Spatial distribution of the accumulation of airborne metals has been mapped through the chemical content of plant leaves across a specific region. Several studies have shown that plant leaves exposed near metallurgical industries can have high concentrations of heavy metals, compared to a background site [12,13,14,15].
Although the vast majority of the studies use plants as passive biomonitors, active biomonitoring methods offer some advantages, as they guarantee a similar exposure period and allow spatially ordered sampling [16]. In particular, active biomonitoring with potted plants is very suitable, as the influence of confounding variables, such as soil, can be diminished.
Moreover, active biomonitoring is particularly suitable to be used in citizen science, which involves citizens and professionals in the same project, “aiming to create a collective action that favors behavioral changes toward the environment” [17,18] and supports complex sustainability transitions [19].
Strawberry leaves have already been used as active PM receptors [4,20], in the framework of two citizen science projects, one implemented in East Flanders (Belgium) [21], and the other in four European cities [22], where strawberry plants were exposed in urban environments, namely on roadsides. Strawberry leaves contain trichomes and have a rugged micromorphology that is important for PM capture [4]. The Saturated Isothermal Remanent Magnetization (SIRM) signal was used on strawberry leaves to monitor ambient [4,20] and indoor [23] PM.
In recent years, there have been complaints from the population of Seixal (Portugal), an urban-industrial area with the influence of steelworks, regarding episodes of dust deposition on properties. Due to these pollution events, the concerns of the citizens have increased regarding the local air quality and its impact on their health [24]. The local council promoted a set of actions to understand the sources contributing to local PM levels, namely the chemical characterization of the settled dust [25], a citizen science air biomonitoring study using lichens [26] and strawberry plants as biomonitors, and the chemical characterization and source apportionment of PM2.5 [27]. Common conclusions were drawn in these three previous publications, namely the influence of traffic and the local steel industries.
This work presents the results of the aforementioned citizen science biomonitoring study regarding the use of the strawberry plant (Fragaria × ananassa Duchesne ex Rozier) as a biomonitor, since the results relating to the simultaneously exposed lichens were previously published [26]. The present work aimed to do the following: (1) assess if the strawberry plant biomonitor could confirm the main sources of air pollution previously identified in the study area, namely traffic and steelworks [25,26], and to identify the hotspots spatial distribution; (2) assess the reliability of using the strawberry plant as a biomonitor, and to identify the scope of its use in terms of the monitored elements. To achieve these goals, the chemical characterization regarding the content of metals, metalloids, and non-metals of the leaves from the exposed plants at the outdoor of the citizens’ dwellings was conducted by X-ray Fluorescence spectrometry. Source apportionment techniques were then used to identify the main pollution sources. To achieve goal (2), the biomonitoring responses of strawberry leaves and transplanted lichens simultaneously exposed in the study area were compared. Comparisons between lichens and vascular plants as biomonitors of air pollution are scarce in the literature [28], and can point to different accumulation capabilities depending on the metal and the species. This is thus a novel insight brought by this study, considering that the number of studies which used strawberry leaves as biomonitors is very low, and the knowledge on the capabilities and limitations of this biomonitor is lacking in the literature.
The citizen science approach with the strawberry plant biomonitor allowed us to engage the population in the study and reach the societal goal of raising awareness on air quality, while providing reliable information on trace metal contamination.

2. Materials and Methods

2.1. Study Area

This study was developed in Aldeia de Paio Pires, municipality of Seixal (Portugal), located in the Lisbon Metropolitan Area (Figure 1). Seixal has a high population density (1744 inhabitants per km2 [29]). The study area is crossed by high-density traffic highways and comprises many industries, namely (Figure 1) a steelworks that manufactures galvanized sheet metal with Cr passivation (A), an Electric Arc Furnace steelworks (B), a lime factory (C), and a metal waste treatment factory (D) [26].

2.2. Selected Biomonitors

The leaves of strawberry plants (Fragaria × ananassa Duchesne ex Rozier, a hybrid that resulted from F. chiloensis and F. virginiana) were selected as biomonitors of air pollution. Therefore, a set of potted strawberry plants (with same age and uniform potting medium) was acquired from a local supplier.
Additionally, samples of the lichen Flavoparmelia caperata (L.) Hale were collected from olive trees at Montargil, a known clean area from inland Portugal. A transplanted lichen was exposed simultaneously next to each potted strawberry plant. Details about the applied methodology and results of the transplanted lichens have been described elsewhere [26].

2.3. Exposure Strategy of Biomonitors—Citizen Science Approach

On 1 February 2020, a public event was promoted by the local council in Aldeia de Paio Pires to involve the local population in the biomonitoring study and to provide all the necessary information: (i) how to expose the biomonitors in the outdoor of the dwellings (either on its ground or first floor, at 1.5 m height); (ii) to water the strawberry plants avoiding contact with the leaves; (iii) not to add any fertilizers to the soil or transplant the strawberry plants; (iv) at the end of the exposure period, five sets of three full-grown and undamaged strawberry leaves should be collected by the participants (by touching only the petiole) and sent to the laboratory in a paper envelope.
A total of 78 strawberry plants (Fragaria × ananassa Duchesne ex Rozier) and 78 lichen (Flavoparmelia caperata (L.) Hale) samples were distributed among the volunteers. These were positioned in a georeferenced grid of 4.55 km × 6.82 km, with 77 cells of 650 m × 620 m, between the coordinates 38°38′53″ N, 9°06′21″ W, and 38°35′9.83″ N, 9°3′12″ W (upper left and lower right corner of the grid), as shown in Figure 1. At each site, one plant and one lichen sample were exposed. Controls were placed outside the grid, in an assumed background site, Verdizela (red dot in Figure 1), characterized by forest and a weekend residential area, with sporadic traffic, at 9 km distance from the industrial area. Considering that citizens that lived nearby the industrial area have already shown concern regarding air quality problems [24], it was expected that those volunteers would show higher engagement, which was confirmed. This allowed us to have a higher sampling density around the steelworks (Figure 1).
The biomonitors were exposed from 1 February to 17 June 2020 (138 days), a period that included the state of emergency decreed in Portugal on 18 March until 4 May due to the COVID-19 pandemic, which included a national lockdown. After the exposure period, leaves from 49 strawberry plants were obtained (due to the withering of some plants), which is above the minimum of 30 suggested for contamination monitoring with leaves [30] and represents a rate of 62.8% in the retrieval of exposed plants.

2.4. Chemical Analysis

At the laboratory, the leaves were quickly rinsed (5 s) with demineralized water, freeze-dried, and ground into powder in a ball mill with PTFE capsules, under liquid nitrogen. Pellets with an average thickness of 12 mm were prepared for element characterization by X-ray Fluorescence spectrometry (XRF), to assess the mass fractions of 22 elements (Al, As, Ba, Br, Ca, Cl, Cr, Cu, Fe, K, Mg, Mn, P, Pb, Rb, S, Se, Si, Sr, Ti, Zn, and Zr). XRF analysis was conducted in ATOMKI, Debrecen, Hungary.
A Bruker M4 Tornado type micro-XRF device (high-brilliance X-ray tube with polycapillary X-ray optics; tube parameters: Rh target, 50 kV, 30 W; spot size: ~25 μm for Mo Kα (17.5 keV); 8 excitation filters) was applied to conduct the XRF analysis. For the excitation, the Rh tube was set to 50 kV voltage with a 400 mA current, and no filter was used. A Be windowed XFlash SDD X-ray detector with a 30 mm2 surface area recorded the X-ray spectra of the samples. The measurements were performed in vacuum (20 mbar). To obtain the best estimates of the average composition of the pellets, spectra and elemental maps were collected from the largest possible surface area of the sample (~6–8 mm × 6–8 mm). Elemental maps helped to filter out samples with large inhomogeneities. Such pellets were excluded from the analysis. In the case of 10 samples, multi-point analysis was also carried out at 60 randomly selected points per sample. Average concentrations obtained from the point measurements were compared to the results of the area scans. In all cases, concentrations obtained in the two ways were the same within 2 sigma. At least 1 of the 2 or 3 replicate pellets was measured, for each sample. Eight randomly selected samples were re-measured 2 more times on different days, and in the case of 5 pellets, both sides were measured. For 6 samples, all 2–3 replicates were analyzed and compared. In all cases, the obtained concentration data were within uncertainty.
The fundamental parameter (FP) method [31] was utilized for the standardless quantification using the built-in software M-Quant of M4 Tornado version 1.6.614.0 [32]. Cellulose (C6H10O5) was set as the matrix material.
Two plant samples from proficiency tests PTNATIAEA/19 and PTNATIAEA/20 conducted by the International Atomic Energy Agency (http://www.pt-nsil.com/) served as reference materials for the QA/QC of the XRF measurements. Results of XRF analysis of the reference samples and instrumental detection limits (DL) are presented in the Supplementary Materials (Table S1).

2.5. Meteorological and Air Quality Data

Meteorological data of the exposure period were obtained from the Barreiro-Lavradio meteorological station (38°40′28″ N, 9°2′51″ W) of Instituto Português do Mar e da Atmosfera (IPMA). Figure S1 (Supplementary Materials) provides an overview of the meteorological conditions during the exposure period. The mean daily temperature was 16.7 ± 3.2 °C (ranging from 9.7 to 25.1 °C), the mean daily relative humidity was 80.1 ± 7.9% (63.0 to 98.0%), the mean wind speed was 2.9 ± 1.0 m·s−1 (1.0 to 6.8 m·s−1), and the mean daily precipitation was 6.1 ± 7.2 mm (0.1 to 21.4 mm), in 41 days with precipitation. Since strawberry leaves are sensitive to wash-off [4], the effect of rain was limited by collecting the leaf samples a week after the last rain event (which was residual, 0.7 mm), as recommended in other studies [33]. The maximum wind speed was 3.9 m·s−1 in the last week of exposure and resuspension of PM caused by wind of 5 m·s−1 is considered small compared with total deposition on leaves [34]. During the exposure period, the prevailing wind directions were north (N), northwest (NW), and west (W) [26].
PM2.5 and PM10 concentrations from the Paio Pires monitoring station (38°37′16″ N, 9°4′55″ W) of the National Air Quality Monitoring Network were obtained through the information system QualAr [35].

2.6. Data Analysis

2.6.1. Statistical Analysis

The Shapiro–Wilk test was performed to assess the normality of the variable distributions. A t-test and a non-parametric Wilcoxon Signed Rank test were performed (for normally and non-normally distributed data, respectively), to compare the elemental concentrations in the strawberry leaves exposed in the study area and at the background site, and also to compare the enrichment factors in the strawberry leaves and in the lichens simultaneously exposed. The Kruskal–Wallis tests were used to compare more than two independent groups. Spearman’s rank correlation was applied to assess the associations between element concentrations. The significance level was based on an alpha of 0.05.
The principal component analysis (PCA) was applied to explore the common contribution of different elements in the leaves. As it is not possible to consider all the variables in the analysis given the number of available cases (exposure sites), variables that had low values (r < 0.5) in the diagonal of the anti-image correlation matrix were eliminated from the analysis to obtain a significant result for Bartlett’s sphericity test (p-value < 0.001). The criterion of Kaiser–Meyer–Olkin (KMO) was 0.642, which confirmed the suitability of the data for PCA. The components with eigenvalues higher than 1 were considered for varimax rotation with Kaiser normalization to obtain the final matrix [36]. Factor loadings higher than 0.5 were considered to relate an element to a source [37]. Statistical analysis was performed using IBM SPSS Statistics 27 software.
For the comparison between biomonitors, the elemental concentrations in Fragaria × ananassa Duchesne ex Rozier leaves and in lichen Flavoparmelia caperata (L.) Hale samples were compared by means of major axis regression analysis, since an independent variable cannot be distinguished. In this situation, Model II regression is recommended for use instead of the usual Model I [38,39,40]. To conclude if there are differences in bioaccumulation between the species, a permutation test is performed to assess if the slope of the regression line that relates the concentrations in Flavoparmelia caperata (considered a good bioaccumulator) and Fragaria × ananassa is significantly different from 1 [40]. “R” package “lmodel2” was used to calculate the regression [41].

2.6.2. Accumulation and Enrichment Factors

The Accumulation Factor (AF) was determined, for each sample, as the ratio of element concentrations (normalized to Al) in the strawberry leaves from the sampling site versus the leaves from the background site [42,43,44]:
A F X = C X C A l l e a v e s   s a m p l i n g   s i t e C X C A l l e a v e s   b a c k g r o u n d   s i t e
where CX/CAl is the ratio between the element and Al concentrations in strawberry leaves. Al was used as the reference element [42,44], since it is considered a non-anthropogenic soil element. This was confirmed by the PM2.5 source apportionment by Positive Matrix Factorization (PMF) performed in the study area [27], which identified a “soil” emission source predominantly characterized by Al. The following contamination categories can be defined based on AF values [43]: AF ≤ 1 denotes no accumulation relative to background; 1 < AF < 2 minimal accumulation; AF= 2–5 moderate accumulation; AF = 5–20 significant accumulation; AF = 20–40 very high accumulation; AF > 40 extremely high accumulation.
The enrichment factor (EF) assists in identifying the crustal versus anthropogenic origin of elements and was determined for each sample [8,38,45]:
E F X = C X C A l s a m p l e C X C A l c r u s t  
where (CX/CAl)sample is the concentration ratio of an element to Al in the exposed strawberry leaves (or lichens), and (CX/CAl)crust is the corresponding ratio in the crust. The upper continental crustal average [46] was used. An EF lower than 10 is generically considered to indicate crustal origin, while an EF ≥ 10 indicates an element mostly emitted from anthropogenic sources [13,37]. According to [47], elements can be assumed to be enriched when the mean EF is larger than 3, with at least 30% of the samples with EF > 3.

2.6.3. Distribution Maps of the Elemental Concentrations

Geostatistical modeling maps of elemental concentrations in strawberry leaves were prepared in QGIS version 3.28.6-Firenze (http://qgis.org), using the Inverse Distance Weighting (IDW) interpolation, as applied in other biomonitoring studies [11,26,48,49,50,51]. IDW calculates a cell value, averaging the sampled data around that cell [52]. In the equation for IDW (Equation (3)),
v 0 = ∑ i = 1 S v i 1 d i k ∑ i = 1 S 1 d i k
where v0 and vi represent the estimated and the sampled value at point I, respectively, di is the distance to point I, S is the number of sampled points used in the calculation (equal to 47), and k is assumed equal to 2. The range of concentration values for each element was subdivided in intervals corresponding to 0–20, 20–40, 40–60, 60–80, and 80–100 percentiles. The values below detection for Cr and Pb were set to half of the detection limit for representation [53].

3. Results and Discussion

3.1. PM Levels

Figure S2 presents the variability of PM2.5 and PM10 levels during the exposure period, when a mean daily PM10 concentration of 19.3 ± 9.0 μg.m−3 (from 3.8 to 51.7 μg.m−3) was registered and the WHO daily guideline value for PM10 of 45 μg.m−3, not to be exceeded more than 3–4 days per year [54], has been exceeded three times. During the exposure period, a mean daily PM2.5 concentration of 9.5 ± 5.3 μg.m−3 (from 1.7 to 25.7 μg.m−3) was registered and the WHO daily guideline value for PM2.5 of 15 μg.m−3, not to be exceeded more than 3–4 days per year [54], has been exceeded 19 times.
The lower PM levels observed in 2020, and specifically in the strawberry plants exposure period, are attributable to the national lockdown decreed on 18 March until 4 May and other measures applied in the following months to slow down the transmission of SARS-CoV-2 [55], which have significantly impacted the anthropogenic activities in the study area, and consequently the PM levels, PM2.5 chemical composition, and source contributions [27]. Nevertheless, the industrial activity in the area did not stop during this period, creating an opportunity to assess the impact of industry on the local air quality.
During the exposure period, PM10 and PM2.5 concentrations were negatively correlated with precipitation (r = −0.485 and −0.487, respectively), as expected due to atmospheric washout, and also with mean daily wind speed (r = −0.333 and −0.489, respectively), since higher wind speeds promote the pollutants’ dispersion [4].

3.2. Elemental Characterization of Strawberry Leaves

The percentage of samples that could be recovered after exposure was 62.8%, a value similar to that obtained in the city of Antwerp (69.2%), although in the other cities of the StrawbAIRies project, much higher recoveries were obtained (globally, 88.7%) [22].
The descriptive analysis of elemental concentrations in the strawberry leaves is presented in Table 1 and Figure S3, where the elements are categorized in macro- and microelements, most of which are plant nutrients, and trace elements, which on the contrary, do not display a physiological function (namely Rb, Sr, Ba, Pb, Cr, As, and Br) [56].
Major elements measured in the strawberry leaves were the macronutrients and structural elements (K, Ca, Mg, P, S, Si), with mean concentrations ranging from 1.1 ± 0.4 to 24.5 ± 7.7 g.kg−1, while the micronutrients (Fe, Mn, Zn, and Cu) had concentrations ranging from 5.6 ± 1.6 to 2420 ± 1770 mg·kg−1 and trace elements, from 6.0 ± 2.5 to 108 ± 29 mg·kg−1. A study conducted in Antwerp (Belgium) found elemental concentrations in the surface of strawberry leaves assessed by ED-XRF ranging from 8.4 ± 3.0 ng.cm−2 (Cr) to 44,656 ± 9160 ng.cm−2 (Ca), with the following order: Cr < Pb < Rb < Ti < Sr < S < Fe < Si < P < Cl < Ca [4], which coincides generically with the results of the present study.
Markert [57] proposed the use of a “reference plant” with an average chemical composition. Several elements in the exposed strawberry leaves, namely all the non-essential trace elements (except for Rb) and Fe, presented mean concentration values above the “reference plant” levels [57]. The values are within the typical range found in terrestrial plants, except for K, Mg, P, S, Sr, Ba, Se, and Cr, for which above-normal concentrations were observed (Table 1). The toxic levels for plants were even reached for Sr and Cr. Although there is no consensus, 0.5–10 mg·kg−1 is indicated as a normal level of Pb in plants [62] and this value has been exceeded in some samples.
The elemental concentrations in the exposed leaves in the exposure sites were significantly higher (t-test and Wilcoxon test, p < 0.05) than in the background site, except for S, Br, and Se, for which the opposite was observed. For Cl, Mn, and Pb, a non-significant difference was found (Table 1). Although the reference site is located in a large natural or seminatural area and it was assumed as a background site, it is a weekend residential area, which may be affected by biomass burning emissions for household heating. The enrichment in S [69] and Br [70] may reflect these emissions. Furthermore, as Tomašević et al. [5] noted, the green areas near urban zones may not be true background sites, due to the transport of air pollutants.
Not only high concentration, but also large element variability, indicate the influence of an anthropogenic source [71,72]. The modified coefficient of variation of element concentrations (standard deviation divided by the median) was determined [15], confirming the existence of high differences in concentration and dispersion between elements (Figure 2). Most macro- and micronutrients (Ca, Mg, S, K, P, Cu, and Zn) presented a low coefficient of variation, suggesting that these elements are regulated by biological processes, whereas Fe and Mn, despite being micronutrients, presented higher values. As generically observed for non-essential trace elements [56,71], Pb, Ti, As, and especially Cr presented higher spatial variability.

3.3. Assessment of Pollution Sources

3.3.1. Bioaccumulation

As previously mentioned, elements with an Accumulation Factor (AF) higher than 2 are considered to evidence enrichment and environmental pollution [43]. Figure 3 presents the AF for the different chemical elements in the exposed strawberry leaves. The obtained AF values reflect plant biochemistry, since all plant macronutrients (K, Mg, Ca, P, and S) and some micronutrients (Mn, Cu, and Cl) presented AF < 1.5. Furthermore, results indicate a minimal accumulation of Ba (1.5 ± 0.5), Zn (1.6 ± 1.0), and Ti (1.9 ± 1.9), and moderate accumulation of Fe (2.5 ± 1.5), Zr (4.0 ± 2.3), As (4.3 ± 4.7), and Cr (4.9 ± 3.5). No accumulation of Br (0.7 ± 0.2), Se (0.8 ± 0.3), Pb (0.9 ± 0.5), and Sr (1.0 ± 0.4) was found, probably due to environmental contamination on the background site. Although unexpected, other works found Pb contamination even in remote areas [13].
A non-normalized AF with a value of 3 for Fe was also observed to arise from PM contamination from steel industry in other works [73]. An AF of 3.55 for Cr was observed in the Quercus ilex L. leaves and was considered to result from the uptake from the air [74]. Castanheiro et al. [4] also identified a significant accumulation of Cr in strawberry leaves exposed in a suburban site, and this was attributed to traffic PM.
To discriminate between anthropogenic and natural sources of the elements measured in the strawberry leaves, enrichment factors (EFs) relative to average crustal composition were determined, considering Al as the normalizing element (Figure 4). The elements Ti (1.8 ± 1.8) and Si (2.0 ± 0.9) have a predominant crustal origin (EF < 10), while Fe (11.1 ± 6.5), Cr (19.7 ± 14.1), and the remaining elements showed the contribution of anthropogenic emissions.
In this work, it is not possible to quantify the washout effect by precipitation (which was residual in the last week of exposure) and by quickly rinsing the leaves in the samples preparation, but a part of the PM may have been removed from the leaves’ surfaces by those processes [4]. As such, the enrichment results determined in this work may be underestimated for some elements.

3.3.2. Spearman Correlations

Spearman correlations were determined to assess the bivariate relations between elemental concentrations in the exposed strawberry leaves, and significant correlations are presented in Table S2. Fe was strongly correlated with Cr (r = 0.831), and Cr was also moderately correlated with Mn (r = 0.605). This suggests that these elements have a common emission source, responsible for the observed EF ≥ 10 for these elements, which probably is the steel industry. This corroborates the findings of the biomonitoring study with lichens in the study area [26], where the strong correlations found between Fe-Cr (r = 0.81), Cr-Mn (r = 0.92), and Fe-Mn (r = 0.77) were assigned to the steelworks. In the strawberry leaves, the Cr-Mn (r = 0.605) and Fe-Mn (r = 0.336) correlations, although significant, were not as strong as in lichens, indicating the presence of mixed sources, namely the root uptake of Mn as a minor nutrient of plants.
In the strawberry leaves, Fe was also strongly correlated with Ti (r = 0.742) and significantly correlated with Al (r = 0.493), all elements of geogenic dust [72,75]. This mixed natural and industrial influence was previously found for Fe in other studies near steelworks [76] and also in the biomonitoring study with lichens in the study area [26], where Fe-Ti (r = 0.84) and Fe-Al (r = 0.87) correlations were found. Differently from our study, the enrichment in Cr and Fe in strawberry leaves has been related to traffic in a Belgian urban area [4].
While Zn and Pb are frequently associated with the steel industry [77,78], in the present study these elements seem to be associated with a distinct emission source, namely traffic, since their correlation with Fe, Cr, and Mn is not significant. Castanheiro et al. [4] also observed the accumulation of Pb in strawberry leaves, which was attributed to traffic. The pair Zn-Pb has been reported to present antagonistic interaction in plant physiology, with competitive inhibition by one another [8,68]. It is thus plausible that the weak Zn-Pb correlation (r = 0.460) observed in strawberry leaves is influenced by this physiological interaction.
Additionally, another anthropogenic source is suggested by the moderate correlation between As and Zr (r = 0.686). Since P and S are major nutrients for plant growth, the significant although weak correlations between As/Zr with P and S may be due to root absorption. Moderate correlations were found between K-Al (r = 0.682), K-Cl (r = 0.648), Cl-Si (r = 0.616), and Cl-Al (r = 0.586), suggesting the geogenic nature of K and Cl. Although Sr is not essential for plants, it is absorbed from the soil along with Ca [66], and this might explain the Sr-Ca correlation (r = 0.590).

3.3.3. Principal Component Analysis

Principal component analysis (PCA) was performed to estimate the profiles of possible emission sources, as previously performed using the concentration of trace elements in leaves [8,37,42,43,66]. Table 2 presents the PCA results for the element concentrations in the exposed strawberry leaves.
The four most discriminant components—PC1 to PC4 (Table 2)—accounted for 71.7% of the total variance in all exposed strawberry leaves. PC1 explained 29.2% of the variability and had high positive loadings for K, Al, Si, Cl, and Mg, which are crustal and leaf-occurring macro-elements. PC2 explained 18.8% of the variability and included Cr, Fe, and Mn, which are tracers of the iron and steel industry [70], and a non-negligible contribution of Ti. PC3 explained 14.3% of the variability and had higher loadings of P, S, and Mg, which are plant major nutrients, absorbed by root uptake. PC4 explains 9.4% of the variability and includes Zn, Pb, and Ti, probably denoting a traffic origin, with resuspension. In fact, Zn and Pb concentrations in tree leaves have been associated with traffic [43,79], since Zn is emitted by the abrasion of tires and brakes, and from lubricating oils, and Pb is emitted by exhaust gases and brakes [16]. Despite the removal of leaded fuels many years ago, Pb is a still traffic-related element that has been observed with several biomonitors [4,11,37,71,80]. The resuspension of contaminated soil can act as a source of Pb, as is probably the case, given the significant contribution of Ti for PC4.
A source apportionment of PM2.5 was also performed in the study area in 2020 [27], where a total of seven emission sources were identified by the Positive Matrix Factorization (PMF) receptor model: soil (defined by Al, Si and Ti), sea (Na, Cl and Mg), secondary sulphate (SO42−, NH4+), industry/steelworks (Fe, Mn and Ca), vehicle non-exhaust (Zn, Pb, Mn, Cu, and Fe), vehicle exhaust (BC, NO3−, and NH4+) and fuel-oil combustion (V, K). It is possible to conclude that the biomonitoring strategy with strawberry leaves reflected some of these PM2.5 emission sources, namely, soil/geogenic, industry, and traffic.

3.3.4. Spatial Distribution of Elemental Concentrations

The distribution maps of the element concentrations in the exposed strawberry leaves were created (Figure 5). The similarities between the spatial distributions obtained for the elements grouped by PCA support the contribution of the identified emission sources. Maximum concentrations of Cr, Fe, and Mn were obtained near the steelworks, with isolated hotspots in the south of the grid, while the maximum concentrations of Zn and Pb were obtained in the south, close to the highways, with isolated hotspots in the industrial area.
To confirm the influence of the steelworks in the local air quality, the study area was divided into concentric crowns with a radius of 1 to 5 km, with the center in steelworks B. Figure 6 presents mean element concentrations found in the exposed strawberry leaves versus the distance to steelworks B. Fe and Cr concentrations declined exponentially with distance from the source, according to strong models (r2 = 0.890 and 0.888 for Fe and Cr, respectively). The decrease in metal content in leaves with increasing distance from metallurgic industries was identified in other studies [14,15]. The peak levels of Fe and Cr immediately around the steelworks may be due to fugitive emissions from stockpiles, loading/unloading operations, and heavy-duty traffic.
Despite the observed relation of Mn with steelworks emissions, Mn concentrations are not significantly higher near the steelworks. Lower Mn concentrations in leaf samples at a Polish polluted site compared to others have been identified [81], and a decrease in Mn concentration in birch leaves near a smelter due to SO2 emissions has also been reported [82]. The concentrations of Pb, Zn, and also Cr increased at a distance of 4–5 km from steelworks B, which is consistent with the influence of the highway traffic.
Figure S4 shows the variability of Fe levels considering the distance from steelworks B, divided by quadrants. The southeast (SE) quadrant presented significantly higher Fe concentrations than the NW and SW quadrants, according to the Kruskal–Wallis test (p-value = 0.004 and 0.039, respectively). This fact may be due to the influence of the prevailing wind directions, which were mostly from the north (N), northwest (NW), and west (W) in the sampling period [26].

3.4. Comparison Between Biomonitors

As stated before, for each potted strawberry plant that was exposed, a transplanted lichen (Flavoparmelia caperata (L.) Hale) sample was exposed simultaneously, and the results of the biomonitoring study using lichens have been described in detail elsewhere [26]. This strategy may provide insights about whether the use of strawberry leaves as a biomonitor of air pollution affords similar results as lichens. For this, a similar data analysis to that conducted for the strawberry leaves is now performed for lichens, namely the assessment of EFs and PCA.

3.4.1. Enrichment Factors

Figure S5 presents the mean EFs assessed in the strawberry leaves and lichens simultaneously exposed in the study area. Regarding the EFs found in lichens using the reference soil composition and Si as the normalizing element, as assessed by Abecasis et al. [26], Al and Mg were found to have a predominant crustal origin (EF < 10), while Fe (10.5 ± 2.3), Ti (11.7 ± 1.6), and the remaining elements were found to show anthropogenic contributions. Nevertheless, when the EF in lichens is determined using Al as the normalizing element as presented in Figure S5 (same methodology as that used for strawberry leaves), Fe (7.3 ± 1.6) and Ti (8.2 ± 1.1) present an EF below 10, suggesting also the importance of soil origin for these elements.
Overall, comparing the mean EF values in the strawberry leaves and in lichens, both determined using Al as the normalizing element (Figure S5), it can be concluded that strawberry leaves are significantly more enriched than lichens in Si, Fe, Zr, Mn, Rb, Sr, S, As, and especially Mg, K, and Cl (p-value < 0.001), and lichens are more enriched than strawberry leaves in Ti, Cu, Pb, Ca, Zn (p-value < 0.001), and Br (p-value = 0.025), as assessed by the Wilcoxon test. The differences between the two biomonitors in the case of Cr and Se are not significant (p-value > 0.05). The different morphological and physiological characteristics (such as growth rates) of the two biomonitors play a role in the different accumulation capabilities, but the higher enrichment of strawberry leaves in plant macro- and micronutrients (K, Mg, S, and Mn) may also be due to root absorption.
Major axis regression analysis was used for comparison of the two co-located biomonitors, as described elsewhere [38,40]. The major axis regressions of the EFs were significant for Fe (p-value = 0.017), Zn (p-value = 0.001), Pb (p-value = 0.024), and Ti (p-value = 0.008), confirming that Fragaria × ananassa Duchesne ex Rozier is a useful biomonitor of air pollution associated with these elements. Slope values were nevertheless all significantly different from 1 (permutation test p-value < 0.050). No significant regression could be found for Cr (p-value > 0.050), probably due to the number of data points below DL, in lichens and/or strawberry leaves. The absence of significant regression for the remaining elements reflects that the two species must take up these elements in different ways, with a component of root uptake in the case of strawberry plants, as expected for K, Ca, Mg, S, Mn, Cu, Cl, and Si, which are nutrients or structural elements in plants.
It is not possible in our study to determine exactly what proportion of trace elements was taken up from the soil and what came from atmospheric deposition on leaves. The soil can contribute to Zn and Cu in leaves [74], as these elements are characterized by easy translocation to leaves [14]. On the other hand, the uptake of Pb and As from soil is low [10,11,43,83], the translocation of Pb from roots to leaves is limited [14,74], and thus Pb in leaves is considered to come mostly from atmospheric deposition [59,84]. Fe has very low mobility from soil [11]. Fe and Cr tend to accumulate in roots and are scarcely translocated into leaves [85] (and references therein), and hence, the observed leaf accumulation of these metals appears to be mainly due to atmospheric deposition. Moreover, Fe was already identified as a good indicator for atmospheric PM on leaves [86].

3.4.2. Principal Component Analysis

PCA was performed on the element concentrations in the lichens simultaneously exposed in the study area. Table 2 (above) provides the description of the three identified factors, for the chemical species quantified in the lichens. The same criteria for the selection of variables were adopted for strawberry leaves and lichens, except that for lichens, a low value (r = 0.324) for Pb was retained in the diagonal of the anti-image correlation matrix, affording a significant result for Bartlett’s test (p-value < 0.001) and a Kaiser–Meyer–Olkin criterion of 0.737, confirming the suitability of the data for PCA.
The three most discriminant components (PC1, PC2, and PC3) account for 75.4% of the total variance in the lichens’ concentrations. PC1 explained 46.5% of the variability and had high positive loadings for Mn, Cr, Fe, and Mg. As in the strawberry leaves, this factor can be assigned to the steelworks’ emissions, with resuspension suggested by the contribution of the crustal element Mg. This component had a non-negligible loading for Zn, and in fact, Zn is also considered a steel industry tracer [70]. PC2 explained 19.3% of the variability and had high loadings in Si, Al, Ti, and K, and a non-negligible loading in Fe, all crustal elements. PC3 explained 9.6% of the variability and had higher loadings in S, Zn, and Pb, being assigned to traffic, since namely S is considered a tracer of diesel exhaust [70].
Overall, it can be concluded that there is a similar result regarding the identification of pollution sources, since a parallel was found between the principal factors contributing to the element concentrations in strawberry leaves and lichens, with the exception of plant major nutrients component (by root uptake), which is not present in lichens.

4. Conclusions

This study aimed to identify the main pollution sources and the hotspots of air pollution in an urban-industrial area (Seixal, Portugal), applying a strategy based on a biomonitoring study of air pollution, using strawberry plant (Fragaria × ananassa Duchesne ex Rozier) leaves, where the locations of the exposed biomonitors were defined by the participation of citizens, within a citizen science project.
Strawberry leaves were found to be highly enriched in all elements, except Ti, Si, and Al, and some samples even reached phytotoxic levels for Sr and Cr. Accumulation relative to a reference site was moderate for Cr, Fe, Zr, and As, and minimal for Ba, Zn, and Ti.
Principal component analysis was performed and allowed us to discriminate a crustal source (Al, Si, K, Cl, and Mg), a source of plant major nutrients absorbed by root uptake (P, S, and Mg), and two anthropogenic sources, one associated with steelworks (Cr, Fe, and Mn) and the other with traffic dust resuspension (Zn, Pb, and Ti). Element spatial distribution allowed us to confirm these pollution sources. Exponential decreasing trends of Fe and Cr concentrations with the distance from steelworks were also identified, along with maximum concentrations of Zn and Pb close to highways, indicating a good biological response of strawberry leaves to atmospheric pollution.
The comparison of the performance of strawberry leaves and lichens as biomonitors, carried out by regression analysis of EFs, showed a significant relation for Fe, Pb, Ti, and Zn, confirming that strawberry leaves are a useful biomonitor of air pollution associated with these elements. Furthermore, PCA applied to both types of biomonitors identified the same three pollution sources—crustal, steelworks, and traffic.
Overall, the use of strawberry plant leaves in biomonitoring studies, following a citizen science approach, was confirmed to provide information on the main sources and hotspots of air pollution in the area, allowing us to involve the local community.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/plants13243587/s1. Figure S1: Daily meteorological data during the exposure period of the strawberry plants; Figure S2: Daily PM2.5 and PM10 concentrations measured in the Paio Pires monitoring station during the exposure period; Figure S3: Descriptive statistics of the element concentrations in the exposed strawberry leaves; Figure S4: Mean Fe concentration in strawberry leaves along the distance from steelworks B in each quadrant, and mean concentration per quadrant; Figure S5: Mean enrichment factor (EF) in the strawberry leaves and lichens that were simultaneously exposed in the study area. Table S1: Instrumental detection limits and results of XRF analysis of plant reference samples PTNATIAEA/19 and PTNATIAEA/20; Table S2: Spearman correlation coefficients between element concentrations in the exposed strawberry leaves.

Author Contributions

Conceptualization, S.M.A.; data curation, C.A.G., A.R.J. and S.N.; formal analysis, C.A.G. and A.R.J.; funding acquisition, S.M.A.; investigation, C.A.G., N.C., A.R.J., I.D. and Z.K.; methodology, S.M.A. and C.A.G.; supervision, S.M.A.; validation, N.C. and S.M.A.; visualization, A.N.; writing—original draft, C.A.G. and N.C.; writing—review and editing, C.A.G., N.C. and S.M.A. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Câmara Municipal do Seixal and UFSAAPP. N. Canha acknowledges the support by FCT-Fundação para a Ciência e Tecnologia, I.P. (Portugal) for his contract 2021.00088.CEECIND. The FCT support is also acknowledged by C2TN/IST (UIDB/04349/2020 + UIDP/04349/2020). The XRF measurements at ATOMKI were supported by the GINOP-2.3.3-15-2016-00029 ‘HSLab’ project. C.A.G. acknowledges Instituto Politécnico de Setúbal (IPS) for funding the APC, through the RAADRI support.

Institutional Review Board Statement

This is an observational study and no volunteers’ information has been collected. The C2TN Research Ethics Committee has confirmed that no ethical approval was required. Verbal informed consent for participation and publication was obtained from the volunteer participants when the biomonitors were distributed.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors on request.

Acknowledgments

Acknowledgements are due to all citizen participants.

Conflicts of Interest

The authors declare no conflicts of interest. The funders had no role in the design of the study, execution, interpretation of data, or in the writing of the manuscript.

References

  1. Pope, C.A.; Dockery, D.W. Health Effects of Fine Particulate Air Pollution: Lines That Connect. J. Air Waste Manag. Assoc. 2006, 56, 709–742. [Google Scholar] [CrossRef] [PubMed]
  2. Almeida, S.M.; Lage, J.; Freitas, M.D.C.; Pedro, A.I.; Ribeiro, T.; Silva, A.V.; Canha, N.; Almeida-Silva, M.; Sitoe, T.; Dionisio, I.; et al. Integration of Biomonitoring and Instrumental Techniques to Assess the Air Quality in An Industrial Area Located in the Coastal of Central Asturias, Spain. J. Toxicol. Environ. Health A 2012, 75, 1392–1403. [Google Scholar] [CrossRef] [PubMed]
  3. Canha, N.; Almeida, S.M.; Freitas, M.C.; Wolterbeek, H.T. Indoor and Outdoor Biomonitoring Using Lichens at Urban and Rural Primary Schools. J. Toxicol. Environ. Health A 2014, 77, 900–915. [Google Scholar] [CrossRef]
  4. Castanheiro, A.; Hofman, J.; Nuyts, G.; Joosen, S.; Spassov, S.; Blust, R.; Lenaerts, S.; De Wael, K.; Samson, R. Leaf Accumulation of Atmospheric Dust: Biomagnetic, Morphological and Elemental Evaluation Using SEM, ED-XRF and HR-ICP-MS. Atmos. Environ. 2020, 221, 117082. [Google Scholar] [CrossRef]
  5. Tomašević, M.; Aničić, M.; Jovanović, L.; Perić-Grujić, A.; Ristić, M. Deciduous Tree Leaves in Trace Elements Biomonitoring: A Contribution to Methodology. Ecol. Indic. 2011, 11, 1689–1695. [Google Scholar] [CrossRef]
  6. Aničić, M.; Spasić, T.; Tomašević, M.; Rajšić, S.; Tasić, M. Trace Elements Accumulation and Temporal Trends in Leaves of Urban Deciduous Trees (Aesculus hippocastanum and Tilia spp.). Ecol. Indic. 2011, 11, 824–830. [Google Scholar] [CrossRef]
  7. Baldantoni, D.; De Nicola, F.; Alfani, A. Air Biomonitoring of Heavy Metals and Polycyclic Aromatic Hydrocarbons near a Cement Plant. Atmos. Pollut. Res. 2014, 5, 262–269. [Google Scholar] [CrossRef]
  8. Bing, H.; Wu, Y.; Zhou, J.; Sun, H. Biomonitoring Trace Metal Contamination by Seven Sympatric Alpine Species in Eastern Tibetan Plateau. Chemosphere 2016, 165, 388–398. [Google Scholar] [CrossRef] [PubMed]
  9. De Nicola, F.; Maisto, G.; Prati, M.V.; Alfani, A. Leaf Accumulation of Trace Elements and Polycyclic Aromatic Hydrocarbons (PAHs) in Quercus ilex L. Environ. Pollut. 2008, 153, 376–383. [Google Scholar] [CrossRef] [PubMed]
  10. Gillooly, S.E.; Shmool, J.L.C.; Michanowicz, D.R.; Bain, D.J.; Cambal, L.K.; Shields, K.N.; Clougherty, J.E. Framework for Using Deciduous Tree Leaves as Biomonitors for Intraurban Particulate Air Pollution in Exposure Assessment. Environ. Monit. Assess. 2016, 188, 479. [Google Scholar] [CrossRef] [PubMed]
  11. Norouzi, S.; Khademi, H.; Faz Cano, A.; Acosta, J.A. Using Plane Tree Leaves for Biomonitoring of Dust Borne Heavy Metals: A Case Study from Isfahan, Central Iran. Ecol. Indic. 2015, 57, 64–73. [Google Scholar] [CrossRef]
  12. Reimann, C.; Arnoldussen, A.; Boyd, R.; Finne, T.E.; Koller, F.; Nordgulen, Ø.; Englmaier, P. Element Contents in Leaves of Four Plant Species (Birch, Mountain Ash, Fern and Spruce) along Anthropogenic and Geogenic Concentration Gradients. Sci. Total Environ. 2007, 377, 416–433. [Google Scholar] [CrossRef]
  13. Guéguen, F.; Stille, P.; Lahd Geagea, M.; Boutin, R. Atmospheric Pollution in an Urban Environment by Tree Bark Biomonitoring—Part I: Trace Element Analysis. Chemosphere 2012, 86, 1013–1019. [Google Scholar] [CrossRef]
  14. Serbula, S.M.; Radojevic, A.A.; Kalinovic, J.V.; Kalinovic, T.S. Indication of Airborne Pollution by Birch and Spruce in the Vicinity of Copper Smelter. Environ. Sci. Pollut. Res. 2014, 21, 11510–11520. [Google Scholar] [CrossRef] [PubMed]
  15. Sheppard, P.R.; Hallman, C.L.; Ridenour, G.; Witten, M.L. Spatial Patterns of Tungsten and Cobalt on Leaf Surfaces of Trees in Fallon, Nevada. Land Contam. Reclam. 2009, 17, 31–41. [Google Scholar] [CrossRef]
  16. Hofman, J.; Maher, B.A.; Muxworthy, A.R.; Wuyts, K.; Castanheiro, A.; Samson, R. Biomagnetic Monitoring of Atmospheric Pollution: A Review of Magnetic Signatures from Biological Sensors. Environ. Sci. Technol. 2017, 51, 6648–6664. [Google Scholar] [CrossRef] [PubMed]
  17. Constant, N. Role of Citizen Science in Air Quality Monitoring. In Urban Pollution; Charlesworth, S.M., Booth, C.A., Eds.; Wiley: New York, NY, USA, 2018; pp. 303–312. ISBN 978-1-119-26048-6. [Google Scholar]
  18. Conrad, C.C.; Hilchey, K.G. A Review of Citizen Science and Community-Based Environmental Monitoring: Issues and Opportunities. Environ. Monit. Assess. 2011, 176, 273–291. [Google Scholar] [CrossRef]
  19. Sauermann, H.; Vohland, K.; Antoniou, V.; Balázs, B.; Göbel, C.; Karatzas, K.; Mooney, P.; Perelló, J.; Ponti, M.; Samson, R.; et al. Citizen Science and Sustainability Transitions. Res. Policy 2020, 49, 103978. [Google Scholar] [CrossRef]
  20. Declercq, Y.; Samson, R.; Van De Vijver, E.; De Grave, J.; Tack, F.M.G.; De Smedt, P. A Multi-Proxy Magnetic Approach for Monitoring Large-Scale Airborne Pollution Impact. Sci. Total Environ. 2020, 743, 140718. [Google Scholar] [CrossRef]
  21. University of Antwerp Project AIRBEZEN. Available online: http://oost-vlaanderen.airbezen.be/ (accessed on 10 October 2024).
  22. University of Antwerp STRAWBAIRIES 2019: Air Quality Results, European BIOVEINS Project (BiodivERsA-Horizon2020). Available online: https://medialibrary.uantwerpen.be/files/8419/249c45bc-bb05-4c53-a256-1b915003fa2a.pdf (accessed on 10 October 2024).
  23. Van Dyck, L.; Bentouhami, H.; Koch, K.; Samson, R.; Weyler, J. Exposure to Indoor Ferromagnetic Particulate Matter Monitored by Strawberry Plants and the Occurrence of Acute Respiratory Events in Adults. Int. J. Environ. Res. Public Health 2019, 16, 4823. [Google Scholar] [CrossRef]
  24. Canha, N.; Justino, A.R.; Gamelas, C.A.; Almeida, S.M. Citizens’ Perception on Air Quality in Portugal—How Concern Motivates Awareness. Int. J. Environ. Res. Public Health 2022, 19, 12760. [Google Scholar] [CrossRef]
  25. Justino, A.R.; Canha, N.; Gamelas, C.; Coutinho, J.T.; Kertesz, Z.; Almeida, S.M. Contribution of Micro-PIXE to the Characterization of Settled Dust Events in an Urban Area Affected by Industrial Activities. J. Radioanal. Nucl. Chem. 2019, 322, 1953–1964. [Google Scholar] [CrossRef]
  26. Abecasis, L.; Gamelas, C.A.; Justino, A.R.; Dionísio, I.; Canha, N.; Kertesz, Z.; Almeida, S.M. Spatial Distribution of Air Pollution, Hotspots and Sources in an Urban-Industrial Area in the Lisbon Metropolitan Area, Portugal—A Biomonitoring Approach. Int. J. Environ. Res. Public Health 2022, 19, 1364. [Google Scholar] [CrossRef]
  27. Gamelas, C.A.; Canha, N.; Vicente, A.; Silva, A.; Borges, S.; Alves, C.; Kertesz, Z.; Almeida, S.M. Source Apportionment of PM2.5 before and after COVID-19 Lockdown in an Urban-Industrial Area of the Lisbon Metropolitan Area, Portugal. Urban Clim. 2023, 49, 101446. [Google Scholar] [CrossRef]
  28. Bermudez, G.M.A.; Rodriguez, J.H.; Pignata, M.L. Comparison of the Air Pollution Biomonitoring Ability of Three Tillandsia Species and the Lichen Ramalina Celastri in Argentina. Environ. Res. 2009, 109, 6–14. [Google Scholar] [CrossRef]
  29. PORDATA-Base de Dados Portugal Contemporâneo. Available online: https://www.pordata.pt/pt/estatisticas/populacao (accessed on 9 November 2024).
  30. Aboal, J.R.; Fernández, J.A.; Carballeira, A. Sampling Optimization, at Site Scale, in Contamination Monitoring with Moss, Pine and Oak. Environ. Pollut. 2001, 115, 313–316. [Google Scholar] [CrossRef]
  31. Sitko, R.; Zawisz, B. Quantification in X-Ray Fluorescence Spectrometry. In X-Ray Spectroscopy; Sharma, S.K., Ed.; InTech: London, UK, 2012; ISBN 978-953-307-967-7. [Google Scholar]
  32. Carvalho, P.M.S.; Pessanha, S.; Machado, J.; Silva, A.L.; Veloso, J.; Casal, D.; Pais, D.; Santos, J.P. Energy Dispersive X-Ray Fluorescence Quantitative Analysis of Biological Samples with the External Standard Method. Spectrochim. Acta Part B At. Spectrosc. 2020, 174, 105991. [Google Scholar] [CrossRef]
  33. Oliva, S.R.; Espinosa, A.J.F. Monitoring of Heavy Metals in Topsoils, Atmospheric Particles and Plant Leaves to Identify Possible Contamination Sources. Microchem. J. 2007, 86, 131–139. [Google Scholar] [CrossRef]
  34. Ould-Dada, Z.; Baghini, N.M. Resuspension of Small Particles from Tree Surfaces. Atmos. Environ. 2001, 35, 3799–3809. [Google Scholar] [CrossRef]
  35. APA-Portuguese Environmental Agency QualAr. Available online: https://qualar.apambiente.pt/ (accessed on 9 November 2024).
  36. Paatero, P.; Hopke, P.; Begum, B.; Biswas, S. A Graphical Diagnostic Method for Assessing the Rotation in Factor Analytical Models of Atmospheric Pollution. Atmos. Environ. 2005, 39, 193–201. [Google Scholar] [CrossRef]
  37. De Paula, P.H.M.; Mateus, V.L.; Araripe, D.R.; Duyck, C.B.; Saint’Pierre, T.D.; Gioda, A. Biomonitoring of Metals for Air Pollution Assessment Using a Hemiepiphyte Herb (Struthanthus flexicaulis). Chemosphere 2015, 138, 429–437. [Google Scholar] [CrossRef] [PubMed]
  38. Samecka-Cymerman, A.; Kolon, K.; Kempers, A.J. A Preliminary Investigation in Using Pohlia nutans and Larix decidua as Biomonitors of Air Pollution. Pol. J. Environ. Stud. 2008, 17, 121–128. [Google Scholar]
  39. Zar, J.H. Biostatistical Analysis, 5th ed.; Prentice Hall: Upper Saddle River, NJ, USA, 2010; ISBN 978-0-13-100846-5. [Google Scholar]
  40. Fernández, J.A.; Ederra, A.; Núñez, E.; Martínez-Abaigar, J.; Infante, M.; Heras, P.; Elías, M.J.; Mazimpaka, V.; Carballeira, A. Biomonitoring of Metal Deposition in Northern Spain by Moss Analysis. Sci. Total Environ. 2002, 300, 115–127. [Google Scholar] [CrossRef]
  41. Legendre, P. CRAN-Package Lmodel2. Available online: https://cran.r-project.org (accessed on 10 October 2024).
  42. Levei, L.; Cadar, O.; Babalau-Fuss, V.; Kovacs, E.; Torok, A.I.; Levei, E.A.; Ozunu, A. Use of Black Poplar Leaves for the Biomonitoring of Air Pollution in an Urban Agglomeration. Plants 2021, 10, 548. [Google Scholar] [CrossRef] [PubMed]
  43. Miri, M.; Allahabadi, A.; Ghaffari, H.R.; Fathabadi, Z.A.; Raisi, Z.; Rezai, M.; Aval, M.Y. Ecological Risk Assessment of Heavy Metal (HM) Pollution in the Ambient Air Using a New Bio-Indicator. Environ. Sci. Pollut. Res. 2016, 23, 14210–14220. [Google Scholar] [CrossRef] [PubMed]
  44. Kousehlar, M.; Widom, E. Sources of Metals in Atmospheric Particulate Matter in Tehran, Iran: Tree Bark Biomonitoring. Appl. Geochem. 2019, 104, 71–82. [Google Scholar] [CrossRef]
  45. Mijić, Z.; Stojić, A.; Perišić, M.; Rajšić, S.; Tasić, M.; Radenković, M.; Joksić, J. Seasonal Variability and Source Apportionment of Metals in the Atmospheric Deposition in Belgrade. Atmos. Environ. 2010, 44, 3630–3637. [Google Scholar] [CrossRef]
  46. Mason, B.; Mason, B.; Moore, C.B. Principles of Geochemistry, 4th ed.; Smith and Wyllie Intermediate Geology Series; Wiley: New York, NY, USA, 1982; ISBN 978-0-471-57522-1. [Google Scholar]
  47. Fernández, J.A.; Carballeira, A. A Comparison of Indigenous Mosses and Topsoils for Use in Monitoring Atmospheric Heavy Metal Deposition in Galicia (Northwest Spain). Environ. Pollut. 2001, 114, 431–441. [Google Scholar] [CrossRef] [PubMed]
  48. Fernández, J.A.; Aboal, J.R.; Carballeira, A. Use of Native and Transplanted Mosses as Complementary Techniques for Biomonitoring Mercury around an Industrial Facility. Sci. Total Environ. 2000, 256, 151–161. [Google Scholar] [CrossRef] [PubMed]
  49. Turan, D.; Kocahakimoglu, C.; Kavcar, P.; Gaygısız, H.; Atatanir, L.; Turgut, C.; Sofuoglu, S.C. The Use of Olive Tree (Olea europaea L.) Leaves as a Bioindicator for Environmental Pollution in the Province of Aydın, Turkey. Environ. Sci. Pollut. Res. 2011, 18, 355–364. [Google Scholar] [CrossRef]
  50. Elrhzaoui, G.; Divakar, P.K.; Crespo, A.; Tahiri, H.; Alaoui-Faris, F.E.E.; Khellouk, R. Spatial Mapping of Heavy Metals Using Lichen Bioaccumulation Capacity to Assess Air Contamination in Morocco. Cryptogam Biodivers. Assess. 2020, 4, 19–24. [Google Scholar]
  51. Širić, I.; Eid, E.M.; El-Morsy, M.H.E.; Osman, H.E.M.; Adelodun, B.; Abou Fayssal, S.; Mioč, B.; Goala, M.; Singh, J.; Bachheti, A.; et al. Health Risk Assessment of Hazardous Heavy Metals in Two Varieties of Mango Fruit (Mangifera indica L. Var. Dasheri and Langra). Horticulturae 2022, 8, 832. [Google Scholar] [CrossRef]
  52. Watson, D.F. A Refinement of Inverse Distance Weighted Interpolation. Geo-Processing 1985, 2, 315–327. [Google Scholar]
  53. Birke, M.; Rauch, U.; Hofmann, F. Tree Bark as a Bioindicator of Air Pollution in the City of Stassfurt, Saxony-Anhalt, Germany. J. Geochem. Explor. 2018, 187, 97–117. [Google Scholar] [CrossRef]
  54. WHO. WHO Global Air Quality Guidelines: Particulate Matter (PM2.5 and PM10), Ozone, Nitrogen Dioxide, Sulfur Dioxide and Carbon Monoxide; WHO European Centre for Environment and Health: Bonn, Germany, 2021; ISBN 978-92-4-003422-8. [Google Scholar]
  55. Gamelas, C.; Abecasis, L.; Canha, N.; Almeida, S.M. The Impact of COVID-19 Confinement Measures on the Air Quality in an Urban-Industrial Area of Portugal. Atmosphere 2021, 12, 1097. [Google Scholar] [CrossRef]
  56. Schleppi, P.; Tobler, L.; Bucher, J.B.; Wyttenbach, A. Multivariate Interpretation of the Foliar Chemical Composition of Norway Spruce (Picea abies). Plant Soil 2000, 219, 251–262. [Google Scholar] [CrossRef]
  57. Markert, B. Establishing of “Reference Plant” for Inorganic Characterization of Different Plant Species by Chemical Fingerprinting. Water. Air. Soil Pollut. 1992, 64, 533–538. [Google Scholar] [CrossRef]
  58. Cicek, A.; Koparal, A.S. Accumulation of Sulfur and Heavy Metals in Soil and Tree Leaves Sampled from the Surroundings of Tunçbilek Thermal Power Plant. Chemosphere 2004, 57, 1031–1036. [Google Scholar] [CrossRef]
  59. Solgi, E.; Keramaty, M.; Solgi, M. Biomonitoring of Airborne Cu, Pb, and Zn in an Urban Area Employing a Broad Leaved and a Conifer Tree Species. J. Geochem. Explor. 2020, 208, 106400. [Google Scholar] [CrossRef]
  60. Staszewski, T.; Łukasik, W.; Kubiesa, P. Contamination of Polish National Parks with Heavy Metals. Environ. Monit. Assess. 2012, 184, 4597–4608. [Google Scholar] [CrossRef] [PubMed]
  61. Serbula, S.M.; Miljkovic, D.D.; Kovacevic, R.M.; Ilic, A.A. Assessment of Airborne Heavy Metal Pollution Using Plant Parts and Topsoil. Ecotoxicol. Environ. Saf. 2012, 76, 209–214. [Google Scholar] [CrossRef]
  62. Padmavathiamma, P.K.; Li, L.Y. Phytoremediation Technology: Hyper-Accumulation Metals in Plants. Water. Air. Soil Pollut. 2007, 184, 105–126. [Google Scholar] [CrossRef]
  63. Calzoni, G.L.; Antognoni, F.; Pari, E.; Fonti, P.; Gnes, A.; Speranza, A. Active Biomonitoring of Heavy Metal Pollution Using Rosa Rugosa Plants. Environ. Pollut. 2007, 149, 239–245. [Google Scholar] [CrossRef]
  64. Ventura, M.G.; Freitas, M.D.C.; Pacheco, A.M.G. Selenium Levels in Mainland Portugal. Water. Air. Soil Pollut. 2005, 166, 167–179. [Google Scholar] [CrossRef]
  65. Nogueira, T.A.R.; deMelo, W.J.; Fonseca, I.M.; Marques, M.O.; He, Z. Barium Uptake by Maize Plants as Affected by Sewage Sludge in a Long-Term Field Study. J. Hazard. Mater. 2010, 181, 1148–1157. [Google Scholar] [CrossRef] [PubMed]
  66. Banu Doganlar, Z.; Doganlar, O.; Erdogan, S.; Onal, Y. Heavy Metal Pollution and Physiological Changes in the Leaves of Some Shrub, Palm and Tree Species in Urban Areas of Adana, Turkey. Chem. Speciat. Bioavailab. 2012, 24, 65–78. [Google Scholar] [CrossRef]
  67. Kuang, Y.W.; Wen, D.Z.; Zhou, G.Y.; Liu, S.Z. Distribution of Elements in Needles of Pinus massoniana (Lamb.) Was Uneven and Affected by Needle Age. Environ. Pollut. 2007, 145, 146–153. [Google Scholar] [CrossRef]
  68. Kabata-Pendias, A. Trace Elements in Soils and Plants; CRC Press: Boca Raton, FL, USA, 2010; ISBN 978-0-429-19203-6. [Google Scholar]
  69. Canha, N.; Justino, A.R.; Galinha, C.; Lage, J.; Stieghorst, C.; Revay, Z.; Alves, C.; Almeida, S.M. Elemental Characterisation of Native Lichens Collected in an Area Affected by Traditional Charcoal Production. J. Radioanal. Nucl. Chem. 2020, 325, 293–302. [Google Scholar] [CrossRef]
  70. Calvo, A.I.; Alves, C.; Castro, A.; Pont, V.; Vicente, A.M.; Fraile, R. Research on Aerosol Sources and Chemical Composition: Past, Current and Emerging Issues. Atmos. Res. 2013, 120–121, 1–28. [Google Scholar] [CrossRef]
  71. Mingorance, M.D.; Valdés, B.; Oliva, S.R. Strategies of Heavy Metal Uptake by Plants Growing under Industrial Emissions. Environ. Int. 2007, 33, 514–520. [Google Scholar] [CrossRef]
  72. Reimann, C.; Koller, F.; Frengstad, B.; Kashulina, G.; Niskavaara, H.; Englmaier, P. Comparison of the Element Composition in Several Plant Species and Their Substrate from a 1,500,000-km2 Area in Northern Europe. Sci. Total Environ. 2001, 278, 87–112. [Google Scholar] [CrossRef] [PubMed]
  73. Ataabadi, M.; Hoodaji, M.; Najafi, P. Biomonitoring of Some Heavy Metal Contaminations from a Steel Plant by above Ground Plants Tissue. Afr. J. Biotechnol. 2011, 10, 4127–4132. [Google Scholar]
  74. Maisto, G.; Alfani, A.; Baldantoni, D.; De Marco, A.; Virzo De Santo, A. Trace Metals in the Soil and in Quercus ilex L. Leaves at Anthropic and Remote Sites of the Campania Region of Italy. Geoderma 2004, 122, 269–279. [Google Scholar] [CrossRef]
  75. Schreck, E.; Foucault, Y.; Sarret, G.; Sobanska, S.; Cécillon, L.; Castrec-Rouelle, M.; Uzu, G.; Dumat, C. Metal and Metalloid Foliar Uptake by Various Plant Species Exposed to Atmospheric Industrial Fallout: Mechanisms Involved for Lead. Sci. Total Environ. 2012, 427–428, 253–262. [Google Scholar] [CrossRef] [PubMed]
  76. Mazzei, F.; D’Alessandro, A.; Lucarelli, F.; Marenco, F.; Nava, S.; Prati, P.; Valli, G.; Vecchi, R. Elemental Composition and Source Apportionment of Particulate Matter near a Steel Plant in Genoa (Italy). Nucl. Instrum. Methods Phys. Res. Sect. B Beam Interact. Mater. At. 2006, 249, 548–551. [Google Scholar] [CrossRef]
  77. Hleis, D.; Fernández-Olmo, I.; Ledoux, F.; Kfoury, A.; Courcot, L.; Desmonts, T.; Courcot, D. Chemical Profile Identification of Fugitive and Confined Particle Emissions from an Integrated Iron and Steelmaking Plant. J. Hazard. Mater. 2013, 250–251, 246–255. [Google Scholar] [CrossRef]
  78. Sammut, M.L.; Noack, Y.; Rose, J. Zinc Speciation in Steel Plant Atmospheric Emissions: A Multi-Technical Approach. J. Geochem. Explor. 2006, 88, 239–242. [Google Scholar] [CrossRef]
  79. Ugolini, F.; Tognetti, R.; Raschi, A.; Bacci, L. Quercus ilex L. as Bioaccumulator for Heavy Metals in Urban Areas: Effectiveness of Leaf Washing with Distilled Water and Considerations on the Trees Distance from Traffic. Urban For. Urban Green. 2013, 12, 576–584. [Google Scholar] [CrossRef]
  80. Simon, E.; Baranyai, E.; Braun, M.; Cserháti, C.; Fábián, I.; Tóthmérész, B. Elemental Concentrations in Deposited Dust on Leaves along an Urbanization Gradient. Sci. Total Environ. 2014, 490, 514–520. [Google Scholar] [CrossRef]
  81. Piczak, K. Metal Concentrations in Deciduous Tree Leaves from Urban Areas in Poland. Environ. Monit. Assess. 2003, 86, 273–287. [Google Scholar] [CrossRef] [PubMed]
  82. Kozlov, M.V.; Haukioja, E.; Bakhtiarov, A.V.; Stroganov, D.N. Heavy Metals in Birch Leaves around a Nickel-Copper Smelter at Monchegorsk, Northwestern Russia. Environ. Pollut. 1995, 90, 291–299. [Google Scholar] [CrossRef] [PubMed]
  83. Temmerman, L.D.; Bell, J.N.B.; Garrec, J.P.; Klumpp, A.; Krause, G.H.M.; Tonneijck, A.E.G. Biomonitoring of Air Pollutants with Plants—Considerations for the Future. In Urban Air Pollution, Bioindication and Environmental Awareness; Cuvillier Verlag: Göttingen, Germany, 2001. [Google Scholar]
  84. Hovmand, M.F.; Nielsen, S.P.; Johnsen, I. Root Uptake of Lead by Norway Spruce Grown on 210Pb Spiked Soils. Environ. Pollut. 2009, 157, 404–409. [Google Scholar] [CrossRef] [PubMed]
  85. Sawidis, T.; Breuste, J.; Mitrovic, M.; Pavlovic, P.; Tsigaridas, K. Trees as Bioindicator of Heavy Metal Pollution in Three European Cities. Environ. Pollut. 2011, 159, 3560–3570. [Google Scholar] [CrossRef] [PubMed]
  86. Reimann, C.; Arnoldussen, A.; Finne, T.E.; Koller, F.; Nordgulen, Ø.; Englmaier, P. Element Contents in Mountain Birch Leaves, Bark and Wood under Different Anthropogenic and Geogenic Conditions. Appl. Geochem. 2007, 22, 1549–1566. [Google Scholar] [CrossRef]
Plants 13 03587 g001
Figure 1. Location of the study area: (left) at a national level; (middle) spatial distribution of the strawberry plants that could be retrieved after the exposure period (green dots) and the reference background site (red dot); (right) location of industries A, B, C, and D.
Figure 1. Location of the study area: (left) at a national level; (middle) spatial distribution of the strawberry plants that could be retrieved after the exposure period (green dots) and the reference background site (red dot); (right) location of industries A, B, C, and D.
Plants 13 03587 g001
Plants 13 03587 g002
Figure 2. Modified coefficient of variation (standard deviation divided by the median) of element concentrations in strawberry leaves.
Figure 2. Modified coefficient of variation (standard deviation divided by the median) of element concentrations in strawberry leaves.
Plants 13 03587 g002
Plants 13 03587 g003
Figure 3. Accumulation Factor (AF) of the elements in exposed strawberry leaves. In the box plot, the square represents the mean, upper and lower times sign (×) represent the maximum and minimum values, and the whiskers extend to 1.5* the interquartile range. Below green line—no accumulation; green to orange line—minimal accumulation; orange to red line—moderate accumulation; above red line—significant accumulation.
Figure 3. Accumulation Factor (AF) of the elements in exposed strawberry leaves. In the box plot, the square represents the mean, upper and lower times sign (×) represent the maximum and minimum values, and the whiskers extend to 1.5* the interquartile range. Below green line—no accumulation; green to orange line—minimal accumulation; orange to red line—moderate accumulation; above red line—significant accumulation.
Plants 13 03587 g003
Plants 13 03587 g004
Figure 4. Enrichment factor (EF) of the elements in exposed strawberry leaves. Above the red line, anthropogenic sources are present.
Figure 4. Enrichment factor (EF) of the elements in exposed strawberry leaves. Above the red line, anthropogenic sources are present.
Plants 13 03587 g004
Plants 13 03587 g005
Figure 5. Spatial distribution of element concentrations in the exposed strawberry leaves in the study area (in mg·kg−1) related to (above) crustal natural origin, (middle) industry/steelworks, and (below) traffic.
Figure 5. Spatial distribution of element concentrations in the exposed strawberry leaves in the study area (in mg·kg−1) related to (above) crustal natural origin, (middle) industry/steelworks, and (below) traffic.
Plants 13 03587 g005
Plants 13 03587 g006
Figure 6. Mean elemental concentrations found in the exposed strawberry leaves versus the distance to steelworks B.
Figure 6. Mean elemental concentrations found in the exposed strawberry leaves versus the distance to steelworks B.
Plants 13 03587 g006
Table 1. Descriptive statistics of the element concentrations (in mg·kg−1, dry weight) in the strawberry leaves (n is the number of exposed samples in which the element concentration was above the DL and SD stands for standard deviation). Comparison with reference plant, typical concentrations, and toxic levels for terrestrial plants, and with the mean element concentrations found in lichens simultaneously exposed in the study area. Significant differences in the element concentrations between exposed strawberry leaves in the exposure sites and in the background site were assessed by t-test and Wilcoxon test (for variables with normal and non-normal distributions, respectively), at α = 0.050.
Table 1. Descriptive statistics of the element concentrations (in mg·kg−1, dry weight) in the strawberry leaves (n is the number of exposed samples in which the element concentration was above the DL and SD stands for standard deviation). Comparison with reference plant, typical concentrations, and toxic levels for terrestrial plants, and with the mean element concentrations found in lichens simultaneously exposed in the study area. Significant differences in the element concentrations between exposed strawberry leaves in the exposure sites and in the background site were assessed by t-test and Wilcoxon test (for variables with normal and non-normal distributions, respectively), at α = 0.050.
Strawberry Leaves
(Present Study)		Reference Plant
[57]		Typical Concentrations in PlantsToxic Levels in PlantsTransplanted Lichens
[26]
ElementnMean ± SDMedianMinMaxBackground Sitep-ValueMean ± SD
mg·kg−1 mg·kg−1
Macro elements
K4724,500 ± 770023,40012,70044,70018,0000.00019,0003500–6600 n 7900 ± 1120
Ca4722,600 ± 360023,00011,10030,20018,2000.00010,0002300–5000 n 226,000 ± 15,000
Mg474970 ± 84049703390733033200.0002000500–1300 n 1230 ± 270
Si472400 ± 12601960660615014500.0001000 6240 ± 1280
P471440 ± 680125057039009400.00020001100 n n.d.
S471140 ± 3801080630217012900.011300010–150 a 1580 ± 410
Micro elements
Cl473180 ± 2490271048014,60026500.1542000 n.d.
Fe472420 ± 1770209048010,8707800.000150640–2486 b; 42–352 i 11,800 ± 3800
Al47347 ± 873252165522910.00080200–1000 b; 10–900 i 2590 ± 450
Mn47128 ± 90111366431320.75620015–100 b; 15–160 i 406 ± 297
Zn4747.3 ± 21.340.028.0124.026.00.0005010–160 b; 20–400 c;
10–150 e,h; 15–30 f,i		80–200 ª; 70–400 b; 100 c,f; 300–400 e608 ± 454
Cu465.6 ± 1.65.03.010.04.00.000100.4–45.8 b; 2–20 c,g; 2–12 f;
3–30 h; 4–5 i		100 a; 20–30 c;
20–100 e,h; 30 f,g		163 ± 90
Trace elements
Sr47108 ± 2910931160960.00750 30 m565 ± 74
Ba4786.2 ± 22.789.030.0128.050.00.000402–13 l200–500 ln.d.
As4769.8 ± 62.751.09.0320.015.00.0000.1 30 ± 28
Ti4731.6 ± 25.826.010.0141.015.00.0005 1150 ± 230
Br4721.4 ± 7.020.09.048.029.00.0004 180 ± 16
Rb4721.4 ± 5.920.012.039.017.00.00050 70 ± 15
Zr4717.9 ± 8.116.05.039.04.00.0000.1 79 ± 20
Se479.3 ± 2.210.03.013.011.00.0000.020.05–1 k 75 ± 6
Cr189.2 ± 8.96.03.041.0<DL0.0031.50.1–5 e; 1–2 i5–10 a; 5–30 e,j52 ± 91
Pb286.0 ± 2.55.02.013.06.00.91811–13 c; 3.0–5.9 f
0.5–10 h; 0.4–2.5 i; 3–10 j		30 a,f; 20 b;
10 c; 3–20 d; 30–300 e,h		130 ± 111
a [58]; b [42]; c [59]; d [43]; e [8]; f [60]; g [61]; h [62]; i [38]; j [63]; k [64]; l [65]; m [66]; n [67] and references therein, namely [68].
Table 2. PCA results for the element concentrations in strawberry leaves and in the simultaneously exposed lichens, with factor loadings of principal components (PCs) and communalities. Loadings higher than 0.5 are in bold.
Table 2. PCA results for the element concentrations in strawberry leaves and in the simultaneously exposed lichens, with factor loadings of principal components (PCs) and communalities. Loadings higher than 0.5 are in bold.
Strawberry LeavesLichens
PC1PC2PC3PC4Communality PC1PC2PC3Communality
K0.850.0060.172−0.1060.764Mn0.9650.0880.1450.96
Al0.830.2890.067−0.0580.78Cr0.960.0680.1850.96
Si0.780.118−0.296−0.10.72Fe0.8560.4370.1540.946
Cl0.6450.070.3190.0280.523Mg0.740.3270.0770.661
Cr0.0960.9410.1060.0280.906Si0.2770.9020.0050.891
Fe0.2050.8890.0480.2030.876Al0.3470.871−0.0260.88
Mn0.0240.50.2490.0340.314Ti0.2590.8170.0270.735
P0.0290.220.853−0.0840.784K−0.4220.6580.120.625
S0.1410.2150.778−0.150.693S0.2360.3070.7340.689
Mg0.583−0.20.5840.0960.731Zn0.4740.0440.6060.594
Zn−0.1250.2430.2350.8310.82Pb−0.034−0.1240.5790.351
Pb−0.098−0.251−0.3660.7250.731
Ti0.0390.413−0.250.6650.677
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Gamelas, C.A.; Canha, N.; Justino, A.R.; Nunes, A.; Nunes, S.; Dionísio, I.; Kertesz, Z.; Almeida, S.M. Strawberry Plant as a Biomonitor of Trace Metal Air Pollution—A Citizen Science Approach in an Urban-Industrial Area near Lisbon, Portugal. Plants 2024, 13, 3587. https://doi.org/10.3390/plants13243587

AMA Style

Gamelas CA, Canha N, Justino AR, Nunes A, Nunes S, Dionísio I, Kertesz Z, Almeida SM. Strawberry Plant as a Biomonitor of Trace Metal Air Pollution—A Citizen Science Approach in an Urban-Industrial Area near Lisbon, Portugal. Plants. 2024; 13(24):3587. https://doi.org/10.3390/plants13243587

Chicago/Turabian Style

Gamelas, Carla A., Nuno Canha, Ana R. Justino, Alexandra Nunes, Sandra Nunes, Isabel Dionísio, Zsofia Kertesz, and Susana Marta Almeida. 2024. "Strawberry Plant as a Biomonitor of Trace Metal Air Pollution—A Citizen Science Approach in an Urban-Industrial Area near Lisbon, Portugal" Plants 13, no. 24: 3587. https://doi.org/10.3390/plants13243587

APA Style

Gamelas, C. A., Canha, N., Justino, A. R., Nunes, A., Nunes, S., Dionísio, I., Kertesz, Z., & Almeida, S. M. (2024). Strawberry Plant as a Biomonitor of Trace Metal Air Pollution—A Citizen Science Approach in an Urban-Industrial Area near Lisbon, Portugal. Plants, 13(24), 3587. https://doi.org/10.3390/plants13243587

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop