Effects of High and Low Aerotechnogenic Emissions of Heavy Metals on Wild Plants
Laboratory of Ecology of Plant Communities, Komarov Botanical Institute of RAS, ul. Professor Popov 2, 197022 Saint-Petersburg, Russia
*
Author to whom correspondence should be addressed.
Forests 2023, 14(8), 1650; https://doi.org/10.3390/f14081650
Submission received: 15 June 2023
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Revised: 6 July 2023
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Accepted: 1 August 2023
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Published: 15 August 2023
(This article belongs to the Section Forest Ecophysiology and Biology)
Abstract
:This article presents the results of research on the influence of polymetallic-dust aerial emissions on the pollution levels in the soil and plants by heavy metals, which have been under the impact of the “Severonickel” smelter complex on the Kola peninsula (Russia) for more than 40 years. Research using soil and plant indicators was carried out at monitoring plots in the years 1980–1999 (with high aerotechnogenic emissions) and 2000–2019 (with low aerotechnogenic emissions). The organic horizon (forest litter) of the Al-Fe-humus podzol, assimilation organs of Vaccinium myrtillus L., V. vitis-idaea L., V. uliginosum L., Empetrum hermaphroditum Hagerup, and Pinus sylvestris L. as well as tree rings of Scots pine were used for bioindication research. The content of heavy metals was determined using the AAS method. During these time periods, the emissions of polymetallic dust decreased 3.5 fold, while the level of forest litter contamination with heavy metals in the buffer and impact zones increased by 2–5 times, resulting in increased phytotoxicity of the soil and reduced habitat quality for forest plants. At present, the content of Ni and Cu in the leaves (needles) of the plant indicators in the impact zone has decreased by 3–8 times compared with 1980–1999 but their elevated levels pose a potential health risk. The decrease in atmospheric emissions of pollutants led to a 1.5-fold increase in the width of the annual rings of pine trunks in the impact zone, which may indicate the beginning of the recovery of pine-trunk-wood productivity.
1. Introduction
Aerotechnogenic pollution of the environment, its indication and control, remains one of the most important problems of modern ecology. Since the last third of the 20th century, boreal forests have been exposed to powerful gaseous and particulate pollutants, which have already led to the drying up of coniferous forests in Europe.
The Murmansk region (Russia), which occupies almost the entire Kola Peninsula, is one of the most industrialized regions of Russia; numerous enterprises of non-ferrous and ferrous metallurgy, mineral fertilizers, and building materials—as well as forestry, woodworking, and fishing industries—are located in this region. Here, the main source of environmental pollution is the “Severonickel” smelter complex, which releases air emissions including gaseous (mainly SO2) pollutants and polymetallic dust. The composition of fine polymetallic dust is dominated by metal sulfides and oxides: chalcocite Cu2S, chalcopyrite CuFeS2, pyrrhotite Fe7S8(Nix), pentlandite (Ni, Fe)9S8, covelline CuS, cuprite Cu2O, tenorite CuO, as well as Ni and Cu [1]. The maximum volumes of atmospheric emissions of polymetallic dust were emitted during the period from 1980 to the end of the 1990s. In the period 2000–2010, there has been a significant reduction in emissions of pollutants, and currently, the volume of emissions does not exceed 3 thousand tons per year.
Coniferous forests of the northern taiga (Kola peninsula, Russia), which grow in harsh climatic conditions, need special attention and care, which makes them the most vulnerable and susceptible to stress factors—one of which is technogenic pollution. Mass destruction of coniferous forests occurred in the vicinity of the “Severonickel” smelter complex in the mid-1980s, and the area of destruction of forest ecosystems continues to increase, despite a 3.5–5-fold reduction in pollutant emissions since the beginning of the 21st century. For example, with approaching the source of atmospheric emissions, the total protective cover of the grass-dwarf-shrub layer decreases from 25% to 10% and the moss-lichen layer from 80% to 10%, respectively, and in the impact zone the layer cover is represented by crustose lichens and primary thalli of species of the genus Cladonia L. [2].
In forest ecosystems, trees are phytocenosis edificators and can be considered as ecosystem engineers [3]. The tree layer significantly changes the physical and chemical properties of the environment. Tree crowns redistribute precipitation and change their chemical composition; they affect the light, heat, and wind regimes of habitats, as well as the ground cover [4,5,6,7]. At the same time, due to competitive interactions between plants, trees are differentiated according to the degree of their development and the level of vitality, which leads to a predominance of weakened and severely weakened individuals in boreal forests [8,9,10,11,12]. In the northern taiga, the share of such individuals in the vitality spectra of the Scots pine and spruce stands is 50%–70% [13,14,15,16].
In boreal forests, the ground cover regulates the water and heat regimes of the soil and also acts as a non-timber forest resource. Vascular plants, mosses, and lichens are used in the folk medicine of northern peoples and also serve as food for various animals and birds [17,18,19,20,21,22]. Wild-berry dwarf shrubs (common blueberry, common lingonberry, swamp blueberry, crowberry) are widely represented in the forests of northern taiga, where they are dominants or codominants of the ground cover. Leaves and shoots of these dwarf shrubs have antiviral, antioxidant, and antihypoxic activity and are also sources of the mineral complex necessary for human health [20,21,22,23,24,25,26,27,28].
Heavy metals, which are part of the polymetallic dust emitted into the atmosphere by industrial enterprises, are among the most dangerous and toxic environmental pollutants. Many heavy metals pose a high potential risk to human health, which is why international legislation sets strict standards for the content of heavy metals in various foods, including those of plant origin [29,30,31]. World Health Organization (WHO), in cooperation with the Food and Agriculture Organization of the United Nations (FAO) (JECFA/73/SC), has set limits for the permissible intake of some heavy metals and metalloids (As, Sb, Pb, Cd, Cr, Sn, Hg); however, the content of Ni and Cu in food products and herbal medicinal products is not regulated but the high content of these metals can also have a negative impact on human health.
The main objectives of this work:
- (i)
- Determine the current level of contamination by some heavy metals of the upper organogenic horizon of Al-Fe-humus soils, and compare it with the content of Ni, Cu, and Co in the forest litter during the period of high anthropogenic emissions into the atmosphere in order to assess the degree of phytotoxicity of soils;
- (ii)
- Carry out a comparative analysis of Ni, Cu, and, Co content in indicator plant species during periods of high (1980–1999) and low (2000–2019) aerotechnogenic emissions in the context of assessing the potential threat to human health;
- (iii)
- Evaluate the response of the radial increment of Scots pine trees in reducing airborne emissions, to assess the possibility of restoring the productivity of Scots pine trunks.
2. Material and Methods
2.1. Collection of Material
The total period of monitoring studies in the Murmansk region (Russia) exceeds 40 years (1980–2022). A network of monitoring plots was set up in lichen-green-moss pine forests located in the background, buffer, and impact zones (Figure 1). Zones were named in correspondence with Universal Nomenclature of Environmental Pollution (UNEP) [32]. Sample areas were 10–15, 30–40, and over 70 km away from the source of pollution, the “Severonickel” smelter complex (Monchegorsk, Murmansk region).
At each monitoring plot, 50 stemwood cores were taken using a Pressler drill, at a height of 1.3 m, from the modeling trees (Table 1) that, according to their vital status, belong to the category of weakened and severely weakened, which are most common in the forests of the northern taiga. Each core was packed in a labeled kraft paper bag indicating the sample plot number, tree number, height of the sample, side of light, date.
At each monitoring plot, leaves of Vaccinium myrtillus L., V. vitis-idaea L., V. uliginosum L., Empetrum hermaphroditum Hagerup, and 1-year-old needles of Pinus sylvestris L. were collected from 20 to 30 individuals, and mixed into a mean sample for each plant species. In accordance with generally accepted methods, at least 5 separate samples were used to compile an average forest litter sample. All plant and forest litter samples were dried to an air-dry state.
2.2. Laboratory Analysis
In the laboratory, the plant samples were dried in a desiccator at 105 °C to constant weight and ground using a laboratory mill. Plant samples were mineralized using “wet burning” in a mixture of nitric acid (65%) and hydrogen peroxide (30%) in a ratio of 6:1, according to the Application Manual of Microwave Digestion (SINEO, Jinan, China).
The litter samples were sieved through a sieve with a hole diameter of 1 mm. A solution of 1.0 M HCl was added to the litter sample at a ratio of 1:25, stirred with a magnetic stirrer for 1 h, and filtered through a heavy paper filter.
The total concentrations of Ni, Cu in plant material and the content of acid-soluble forms of Ni, Cu, and Co in forest litter were determined using atomic absorption spectrophotometry on a spectrometer Shimadzu AA-7000 in three replicates.
To assess the potential risk to human health, the concentration coefficient (Cc) was calculated [19]. The concentration factor is the ratio of the metal content in the leaves (needles) of plants from an aerotechnogenic pollution area to its background concentration.
In the laboratory, each pine core was placed in a special vise so that part of the core was 0.5 mm above the vise surface. The protruding part was cut off with a razor to distinguish clearly the boundaries of the annual rings. Radial increment was measured from bark to heartwood using a LINTAB 6 semi-automatic device (Frank Rinn, Heidelberg, Germany). The accuracy of annual ring width measurements was 0.01 mm. Cross-dating was performed using TSAP WIN software [33,34]. The age of the tree was determined by counting the annual rings and marking each 10th ring with a light microscope.
2.3. Statistical Data Processing
Statistical data processing was carried out in the Statistica10 for Windows and R programs using linear regression analysis and ANOVA, and nonparametric Kruskal–Wallis (H) and Mann–Whitney (z) tests were used at a significance level of p < 0.05.
3. Results
3.1. Soil Properties
The main statistical characteristics of the concentrations of acid-soluble forms of Ni, Cu, and Co, measured in the forest litter for two observation periods, are presented in Table 2. First of all, we should note the increased contamination of forest litter in the background pine forests, where the content of heavy metals increased significantly by 1.5–2 times, z = −(2.51–3.32), p < 0.01. In the background area, during both observation periods, the concentrations of acid-soluble forms of Ni and Cu in the litter were comparable with each other and characterized by a fairly wide interval of variation.
In the buffer zone, during the first observation period, the concentrations of acid-soluble forms of Ni and Cu in the litter were almost identical, while during the second period, the Cu content was more than 2 times higher than the Ni content. In the impact zone, during both observation periods, the Ni content in the litter was 1.5–2.4 times lower than the content of acid-soluble forms of Cu. The Co content was always minimal. Over the whole period of monitoring, under conditions of airborne anthropogenic pollution, the range of heavy metal content in forest litter was very wide, and the values of the coefficient of variation varied from 0 to 70%.
On the territory of the buffer and impact zones in the period 2002–2018, the content of acid-soluble forms of heavy metals increased 2–5-fold compared to the first observation period. All differences are significant z = −(2.46–4.37), p < 0.05, except for the content of Ni in the impact zone.
The results of the regression analysis of the data confirm that there has been an increase in the level of heavy metal pollution in the forest litter over the entire monitoring period from 1981 to 2022 (Table 3). Equations of linear regression with high significance approximate the trend of changes in concentrations of selected metals in the organogenic horizon of podzols, both in the background area and in the territory of the buffer and impact zones.
3.2. Heavy Metal Contents in Forest Plants
When approaching the source of pollution, the content of heavy metals in all indicator plant species increases. As an example, Figure 2 shows the dependence of Ni and Cu content in 1-year-old pine needles on distance from the “Severonickel” smelter complex. It is well-approximated by the linear regression equation (R2Ni = 0.610, p < 0.001; R2Cu = 0.692, p < 0.001).
In the background area, the Ni content in the leaves (needles) of the indicator plant species varies from 4.2 to 16.1 mg kg−1 (Table 4). The interval of variation in Cu concentrations is much narrower and amounts to 1.5–2.7 mg kg−1. The differences in their content over the two observation periods are insignificant in almost all cases, except for Empetrum hermaphroditum (Table 4).
In the buffer zone, the range of variation in the Ni and Cu content in the assimilation organs of plants is more significant, from 2.1 to 49 mg kg−1 (Table 4). One can observe a decreasing trend in Ni and Cu content in leaves of indicator plant species during the period of low aerial technogenic emission (2002–2022) but in most cases the differences in their content over the compared periods are insignificant.
On the contrary, in the impact zone, a significant decrease in the content of heavy metals in all the studied plant species was revealed (Table 4). In the second observation period in this zone, the maximum decrease in Ni and Cu content (8- and 6-times, respectively) was recorded in crowberry leaves, and the minimum (less than 3 times) in bilberry leaves. Over the whole monitoring period, the range of heavy metal concentrations varied the most in this zone and was 14–1060 mg kg−1 for Ni and 4–315 mg kg−1 for Cu.
It is important to note that in the background pine forests, the Ni and Cu contents in all indicator plant species are approximately equal (Table 4). Under conditions of aerotechnogenic pollution, the ratio of these metals shifts towards the predominance of Ni over Cu; this pattern is especially pronounced in the impact zone. The ratio of these metals is fundamentally different in forest litter and plant material (Table 2 and Table 4).
3.3. Growth-Ring Width of Pinus sylvestris Trees
The dynamics of the radial increment of Pinus sylvestris trees from the background, buffer, and impact zones had both common features and unique characteristics (Figure 3). In the period 1950–1965, growth was characterized by a fairly high degree of annual fluctuations in all monitoring sample plots. Beginning in the 1980s, in all stands, there was a gradual decrease in radial growth, and over the period 1966–1980, the values of radial increment were 1.92 ± 0.11 (background), 1.47 ± 0.08 (buffer zone), and 1.14 ± 0.01 mm yr−1 (impact zone), respectively. Between 1980 and 2020, the dynamics of the growth-ring width of Scots pine trees differed fundamentally in the studied phytocenoses. In the background, the radial increment continued to gradually decrease (Figure 3) and by the end of the observation period, it was 0.66 ± 0.03 mm yr−1, then under the conditions of aerotechnogenic pollution the dynamics of the radial increment differed significantly from that in the background. In the buffer zone for the period 1980–2021, the values of radial growth gradually decreased from 1.07 ± 0.06 mm yr−1 to 0.57 ± 0.04 mm yr−1. In the impact zone, a further decrease in the radial increment first occurred, and its minimum values (0.3 mm yr−1 on average) were recorded in the 1990s. Then, starting from 1999, the value of the radial growth began to increase, and in 2020 it was equal to 0.48 ± 0.01 mm yr−1.
A comparative analysis of data using the nonparametric Mann–Whitney criterion for periods with high (1980–1999) and reduced (2000–2019) intensity of atmospheric emissions showed that the direction of the dynamics of radial growth for the compared periods is fundamentally different (Table 5). In the background region, in the second period, there was a 2-fold decrease in radial growth. In the buffer zone, the value of the radial increment was the same for both periods, while in the impact zone, a 1.5-fold increase was revealed in the second period.
4. Discussion
4.1. Soil Phytotoxicity
All the soils studied belong to Albic Rustic Podzols, according to the World Reference Base for Soil Resources [35] classification; referring to the granulometric composition, these are sandy loamy loams. This soil type is characterized by the following features: highly acidic (pHaq = 3.9–4.5), with high hydrolytic acidity, and low content of K, P, and N, i.e., low fertility for forest plants. The forest litter, on the one hand, serves as the main source of mineral nutrition for plants; it contains the main part of the roots of both Pinus sylvestris trees and ground cover plants. At the same time, the upper organogenic horizon of the soil acts as a biogeochemical barrier for heavy metals coming from polluted air, which is an additional stress factor for the growth of vascular plants, mosses, and lichens [36,37,38].
The currently observed 1.5–2-fold increase in the content of heavy metals in forest litter in the background area indicates the long-range transport of fine polymetallic dust emitted into the atmosphere by the smelter and, consequently, leads to the expansion of the low technogenic pollution zone.
Our studies have shown that despite the significant reduction in pollutant emissions by the “Severonickel” smelter complex, the level of heavy metal pollution in forest litter in the buffer and impact zones has increased by 2.5–5 times, which causes an increase in the phytotoxicity of soils and reduces the quality of habitats for forest plants. The constant additional precipitation of polymetallic dust from polluted air onto the soil surface prevents the process of self-purification of the upper soil horizon, which, according to various authors, can stretch for tens and hundreds of years [2,36,38,39,40].
4.2. Plants as bioindicators of Heavy Metal Pollution—Assessment of Potential Risks to Human Health
Various biological objects are used to indicate aerotechnogenic pollution of the environment, such as edible mushrooms, lichens, mosses, higher plants, and even tree rings [41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56]. One of the most important factors determining the bioindicative properties of various organisms is the mineral nutrition strategy. Mosses and lichens obtain water and minerals mainly from the air, which allows them to be classical bioindicators, with their bioaccumulation of metals being strongly correlated to the content of metals in the environment [19,42]. Higher plants in terrestrial ecosystems receive mineral nutrition from the soil, mainly through the roots. In the case of aerotechnogenic pollution of the environment, with the simultaneous intake of heavy metals from polluted air and contaminated soil, the composition of atmospheric emissions becomes one of the main factors determining the level of their accumulation in plants.
The results of long-term monitoring of the content of heavy metals in higher plants showed that among the studied species, Empetrum hermaphroditum is the most informative for bioindication. The content of Ni and Cu and the concentration coefficients in the leaves of this species are the highest among the compared species (Table 4, Figure 4). It is logical to assume that the pine needles should accumulate more heavy metals than the ground cover plants because the tree crowns are high above the ground and intercept fine polymetallic dust from the polluted air; however, our research has shown that this hypothesis has not been confirmed. This can be explained by the fact that northern taiga pine forests are very sparse and pine crowns are openwork, so polymetallic dust freely penetrates almost to the forest floor and settles on the surface of the leaves of plants composing the ground cover.
A comparative analysis of the content of heavy metals in the assimilation organs of higher plants over two periods of research showed that in the period 2002–2022 in the buffer zone, the average of Ni and Cu content decreased by 1.2–3.2 times but this decrease is not always significant (Table 4). In the impact zone, during the same periods, there was a 2–8-fold decrease in the level of accumulation of heavy metals in the studied plant species. Therefore, we can conclude that the decrease in the content of heavy metals in the assimilation organs of studied plants is due to a decrease in atmospheric emissions of polymetallic dust by the “Severonickel” smelter complex, since the level of heavy metal contamination of the forest litter increases in the buffer zone and remains very high in the impact zone. During the period of high emissions of pollutants, more than 80% of the total content of Ni and Cu in plant leaves was due to air intake or dust deposition on the leaf surface [2].
To assess the potential risk to human health, we used the concentration coefficient. In the uncontaminated pine forests of the Kola Peninsula, the intervals of variation in the concentrations of heavy metals in all studied species do not exceed their normal content, which is Ni 0.1–5, and Cu 5–30 mg/kg, respectively [20,40]. The maximum values of the concentration coefficient were recorded in the impact zone during the period of high aerotechnogenic emission of pollutants (1980–1999), especially for Ni (Figure 4). Reducing the atmospheric emissions of pollutants by the “Severonickel” plant in the period 2000–2022 led to a 2–5-fold (Ni) and 2–3-fold (Cu) decrease in the concentration coefficient of heavy metals. Nevertheless, the increased content of heavy metals in plant materials can pose a threat to the health of the local population, so it is not recommended to collect medicinal plants, which include almost all of the studied species, in the impact zone.
4.3. Assessing the Potential for Regeneration of Pine Trunk-Wood Productivity
Northern taiga coniferous forests are characterized by low trunk-wood productivity due to the harsh climatic conditions of the Subarctic. The width of the annual rings of pine trees is usually no more than 1–2 mm and under natural conditions (background, unpolluted areas of the Kola Peninsula) the radial growth of coniferous trunk wood decreases with increasing age, which is explained by an increase in the diameter of the trunk [14]. This pattern is confirmed by our research.
Aerotechnogenic pollution, as a rule, causes a decrease in the width of annual rings of woody plants but the degree of its impact manifests itself in different ways [57]. The smallest values of the radial increment of Pinus sylvestris trunk were recorded in the impact zone compared to its trend in the background of the Kola Peninsula, especially during the period of high intensity of aerotechnogenic emission (1980–1995). With a sharp decrease in the atmospheric emissions of pollutants, starting from 2000, an increase in radial growth of 10%–15% is noted, and in the period 2000–2014, a 2-fold increase is recorded [58,59]. For the period 2000–2019, we observe a 1.5-fold increase in the radial growth of weakened and severely weakened Scots pine trees in the impact zone of the “Severonickel” smelter complex, compared with the period of 1980–1999. In the impact zone, the average value of pine radial growth over the last 10 years is 0.60 mm and does not differ significantly from its value in the background area, indicating the beginning of the process of restoration of pine-trunk-wood productivity.
5. Conclusions
The drastic reduction in airborne anthropogenic emissions by the smelter between 2000 and 2020 resulted in the following changes in the forest ecosystems of northern taiga:
- (1)
- The weak pollution zone continues to increase due to the long-range transport of polymetallic dust;
- (2)
- Phytotoxicity of the upper organogenic horizon (forest litter) of Al-Fe-humus soil increases, which reduces the quality of habitat for wild plants;
- (3)
- The content of Ni and Cu in the assimilative organs of plants decreased but their increased concentrations are a threat to human health;
- (4)
- The width of the annual rings of pine wood has stopped differing from its background value, which indicates the beginning of restoring the productivity of pine forest stands.
Author Contributions
Conceptualization, I.L. and P.K.; Methodology, I.L. and P.K.; Software, P.K.; Investigation, I.L. and P.K.; Resources, I.L. All authors have read and agreed to the published version of the manuscript.
Funding
This study was partly supported by the Russian Science Foundation (Project No. 23-26-00193).
Acknowledgments
The authors are grateful to the researcher A.I. Belyaeva of the Botanical Institute of RAS for help with chemical analysis; to Stephen M. Mayfield for valuable advice and assistance in preparing the manuscript; to anonymous reviewers for their comments and suggestions.
Conflicts of Interest
The authors declare no conflict of interest.
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Figure 1.
A schematic map of the study area; (A) Kola Peninsula (Russia); (B) buffer zone (red line outline) and impact zones (blue line outline).
Figure 1.
A schematic map of the study area; (A) Kola Peninsula (Russia); (B) buffer zone (red line outline) and impact zones (blue line outline).
Figure 2.
Content (mg kg−1) of Ni and Cu in 1-year-old needles of Pinus sylvestris according to the distance from the “Severonickel” smelter complex (2008 data).
Figure 2.
Content (mg kg−1) of Ni and Cu in 1-year-old needles of Pinus sylvestris according to the distance from the “Severonickel” smelter complex (2008 data).
Figure 3.
Width of annual rings of Scots pine trees from the background, buffer, and impact zones.
Figure 4.
Concentration coefficients of Ni and Cu in assimilation organs of plants from the impact zone in the periods with high (1980–1999) and low (2002–2022) aerotechnogenic emissions.
Figure 4.
Concentration coefficients of Ni and Cu in assimilation organs of plants from the impact zone in the periods with high (1980–1999) and low (2002–2022) aerotechnogenic emissions.
Table 1.
Characteristics of Pinus sylvestris model trees in the background, buffer, and impact zones.
Table 1.
Characteristics of Pinus sylvestris model trees in the background, buffer, and impact zones.
| Zone | Age (1.3 m), Years | Diameter (1.3 m), cm | Height, m |
|---|---|---|---|
| Background | 52 ± 4 * (45–59) | 15.3 ± 2.6 (11.0–21.2) | 10.6 ± 1.0 (9.0–12.4) |
| Buffer | 59 ± 6 (40–65) | 15.4 ± 4.4 (8.4–24.6) | 11.4 ± 2.5 (6.0–15.5) |
| Impact | 63 ± 4 (45–72) | 12.5 ± 2.8 (9.2–19.0) | 8.1 ± 1.3 (6.0–10.5) |
Note: *—mean ± standard deviation, (min.–max.).
Table 2.
Main statistics of the content (mg kg−1) of acid-soluble forms of Ni, Cu, Co in forest litter from the background, buffer, and impact zones.
Table 2.
Main statistics of the content (mg kg−1) of acid-soluble forms of Ni, Cu, Co in forest litter from the background, buffer, and impact zones.
| Zone | Period | Metal | Mean | SD | Min. | Max. | CV [%] |
|---|---|---|---|---|---|---|---|
| Background | 1981–1997 | Ni | 9.1 | 3.8 | 3.4 | 16 | 42 |
| Cu | 9.2 | 4.6 | 2.8 | 18 | 49 | ||
| Co | 1.0 | 0 | 1.0 | 1.0 | 0 | ||
| 2002–2018 | Ni | 13.3 | 6.1 | 7.5 | 22 | 45 | |
| Cu | 17.9 | 4.8 | 13.1 | 27 | 27 | ||
| Co | 1.2 | 0.2 | 1.0 | 1.5 | 18 | ||
| Buffer | 1981–1997 | Ni | 49 | 17.3 | 17.8 | 68 | 35 |
| Cu | 54 | 31.4 | 13.7 | 110 | 58 | ||
| Co | 1.3 | 0.53 | 1.0 | 2.2 | 40 | ||
| 2002–2018 | Ni | 118 | 51.4 | 68 | 238 | 44 | |
| Cu | 264 | 123 | 174 | 547 | 46 | ||
| Co | 3.4 | 0.59 | 2.5 | 4.4 | 17 | ||
| Impact | 1981–1997 | Ni | 490 | 233 | 127 | 880 | 47 |
| Cu | 713 | 392 | 99 | 1200 | 55 | ||
| Co | 7.4 | 5.2 | 2.3 | 14.8 | 70 | ||
| 2002–2018 | Ni | 546 | 146 | 282 | 800 | 27 | |
| Cu | 1330 | 439 | 820 | 2180 | 33 | ||
| Co | 14.8 | 4.4 | 8.5 | 21.6 | 30 |
Note. Here and in Table 4: mean is mean value; SD is standard deviation; Min. and Max. is minimum and maximum value; CV is coefficient of variation.
Table 3.
Results of linear regression analysis.
| Zone | Metal | N | a | b | R2 | p |
|---|---|---|---|---|---|---|
| Background | Ni | 27 | 0.212 | −412.2 | 0.2586 | 0.0157 |
| Cu | 27 | 0.399 | −783.8 | 0.6316 | 0.00001 | |
| Co | 15 | 0.007 | −13.7 | 0.3278 | 0.0324 | |
| Buffer | Ni | 27 | 2.587 | −5088 | 0.4333 | 0.0002 |
| Cu | 27 | 7.878 | −15,589 | 0.5636 | 0.00001 | |
| Co | 17 | 0.068 | −134.0 | 0.7545 | 0.00001 | |
| Impact | Ni | 27 | 3.732 | −6945 | 0.0716 | 0.1772 |
| Cu | 27 | 23.21 | −45364 | 0.3641 | 0.0009 | |
| Co | 17 | 0.228 | −443.7 | 0.3344 | 0.0150 |
Note: N is number of samples; a, b are the coefficients of regression equations; R2 is coefficient of determination; p is significance level.
Table 4.
Main statistics of Ni and Cu concentrations (mg kg−1) in indicator plant species from the background, buffer, and impact zones.
Table 4.
Main statistics of Ni and Cu concentrations (mg kg−1) in indicator plant species from the background, buffer, and impact zones.
| Specie | Metal | Mean | SD | CV [%] | Min | Max | z (p) |
|---|---|---|---|---|---|---|---|
| Background | |||||||
| Pinus sylvestris | Ni | 5.0 2.3 | 2.5 0.6 | 50 26 | 2.0 | 7.8 | 2.143 (0.085) |
| Cu | 4.3 2.3 | 2.2 0.6 | 51 26 | 1.5 | 6.8 | 1.760 (0.139) | |
| Vaccinium myrtillus | Ni | 5.0 3.8 | 2.6 0.4 | 52 11 | 3.3 | 8.0 | 1.069 (0.326) |
| Cu | 7.3 6.6 | 1.8 3.3 | 25 50 | 2.8 | 11.7 | 0.299 (0.775) | |
| Vaccinium vitis-idaea | Ni | 5.0 2.3 | 2.6 0.4 | 52 17 | 2.0 | 7.8 | 2.316 (0.060) |
| Cu | 4.8 4.4 | 1.2 2.4 | 25 55 | 2.5 | 8.6 | 0.241 (0.818) | |
| Vaccinium uliginosum | Ni | 2.8 3.0 | 0.5 0.9 | 18 30 | 2.0 | 4.2 | –0.276 (0.790) |
| Cu | 5.4 3.7 | 0.6 2.3 | 11 62 | 2.2 | 8.8 | 1.222 (0.257) | |
| Empetrum hermaphroditum | Ni | 12.9 5.7 | 2.9 2.7 | 22 47 | 3.2 | 16.1 | 3.439 (0.018) |
| Cu | 9.3 3.1 | 1.6 0.5 | 17 16 | 2.7 | 10.5 | 7.532 (0.001) | |
| Buffer zone | |||||||
| Pinus sylvestris | Ni | 39.0 12.0 | 10.6 2.6 | 27 22 | 8.4 | 49 | 5.037 (0.004) |
| Cu | 18.1 4.9 | 10.4 1.5 | 57 31 | 3.4 | 30 | 2.603 (0.048) | |
| Vaccinium myrtillus | Ni | 24.1 18.9 | 6.8 5.6 | 28 30 | 8.0 | 31.8 | 1.221 (0.262) |
| Cu | 10.7 8.7 | 4.5 3.9 | 42 45 | 3.4 | 15.8 | 0.718 (0.496) | |
| Vaccinium vitis-idaea | Ni | 24.2 11.0 | 4.2 4.1 | 17 37 | 7.5 | 28.3 | 4.550 (0.003) |
| Cu | 7.8 5.9 | 2.1 2.2 | 27 37 | 3.5 | 10.0 | 1.283 (0.240) | |
| Vaccinium uliginosum | Ni | 11.7 8.2 | 2.0 3.2 | 17 39 | 2.8 | 11.8 | 1.462 (0.204) |
| Cu | 9.3 5.2 | 2.0 2.3 | 22 44 | 2.1 | 10.7 | 2.163 (0.083) | |
| Empetrum hermaphroditum | Ni | 32.7 24.5 | 12.0 10.3 | 37 42 | 13.2 | 45.0 | 0.845 (0.446) |
| Cu | 11.7 7.5 | 3.6 1.6 | 31 21 | 6.0 | 15.5 | 1.816 (0.143) | |
| Impact zone | |||||||
| Pinus sylvestris | Ni | 147 37.5 | 39.7 18.5 | 27 49 | 20.6 | 190 | 5.480 (0.002) |
| Cu | 65.5 12.3 | 32.7 5.7 | 50 46 | 4.8 | 103 | 3.755 (0.009) | |
| Vaccinium myrtillus | Ni | 119 41.1 | 23.2 11.6 | 19 28 | 24.9 | 136 | 7.350 (0.001) |
| Cu | 31.2 13.2 | 8.1 5.6 | 26 42 | 6.3 | 40 | 4.123 (0.003) | |
| Vaccinium vitis-idaea | Ni | 91 30 | 33.1 12.7 | 36 42 | 14.4 | 117 | 4.470 (0.002) |
| Cu | 23.5 10.7 | 5.7 6.2 | 24 58 | 4.2 | 25.1 | 3.416 (0.009) | |
| Vaccinium uliginosum | Ni | 114 23.6 | 39.6 4.3 | 35 18 | 21.5 | 30.2 | 5.960 (0.002) |
| Cu | 33.2 9.2 | 6.3 4.7 | 19 51 | 5.9 | 34.8 | 6.650 (0.001) | |
| Empetrum hermaphroditum | Ni | 576 72 | 220 38.5 | 38 53 | 36.5 | 1060 | 12.072 (0.000) |
| Cu | 169 30 | 74 9.4 | 44 31 | 12.4 | 315 | 5.866 (0.001) | |
Note: above the line—the average content of metal in the leaves (needles) of plants for the period 1980–1999, below the line—the same for the period 2002–2022; z—Mann–Whitney test; p—significance level.
Table 5.
Basic statistics of the growth-ring width (mm) of Pinus sylvestris at a height of 1.3 m in the background, buffer, and impact zones.
Table 5.
Basic statistics of the growth-ring width (mm) of Pinus sylvestris at a height of 1.3 m in the background, buffer, and impact zones.
| Zone | Period | Mean | SD | Min. | Max. | CV [%] | z (p) |
|---|---|---|---|---|---|---|---|
| Background | 1980–1999 | 1.462 | 0.414 | 0.758 | 2.125 | 28 | 4.842 (<0.001) |
| 2000–2019 | 0.685 | 0.160 | 0.402 | 1.008 | 23 | ||
| Buffer zone | 1980–1999 | 0.755 | 0.166 | 0.530 | 1.069 | 22 | –0.555 (0.58) |
| 2000–2019 | 0.758 | 0.124 | 0.549 | 0.993 | 16 | ||
| Impact zone | 1980–1999 | 0.401 | 0.133 | 0.271 | 0.703 | 33 | –4.071 (<0.001) |
| 2000–2019 | 0.611 | 0.094 | 0.391 | 0.736 | 15 |
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Lyanguzova, I.; Katjutin, P. Effects of High and Low Aerotechnogenic Emissions of Heavy Metals on Wild Plants. Forests 2023, 14, 1650. https://doi.org/10.3390/f14081650
AMA Style
Lyanguzova I, Katjutin P. Effects of High and Low Aerotechnogenic Emissions of Heavy Metals on Wild Plants. Forests. 2023; 14(8):1650. https://doi.org/10.3390/f14081650
Chicago/Turabian StyleLyanguzova, Irina, and Paul Katjutin. 2023. "Effects of High and Low Aerotechnogenic Emissions of Heavy Metals on Wild Plants" Forests 14, no. 8: 1650. https://doi.org/10.3390/f14081650
APA StyleLyanguzova, I., & Katjutin, P. (2023). Effects of High and Low Aerotechnogenic Emissions of Heavy Metals on Wild Plants. Forests, 14(8), 1650. https://doi.org/10.3390/f14081650
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