Open AccessArticle

Photosynthetic Efficiency of Plants as an Indicator of Tolerance to Petroleum-Contaminated Soils

Piotr Dąbrowski

Ilona Małuszyńska

Marcin J. Małuszyński

Bogumiła Pawluśkiewicz

Tomasz Gnatowski

^1,*

Aneta H. Baczewska-Dąbrowska

² and

Hazem M. Kalaji

Institute of Environmental Engineering, Warsaw University of Life Sciences, 02-776 Warsaw, Poland

Polish Academy of Sciences Botanical Garden-Center for Conservation of Biological Diversity, 02-973 Warsaw, Poland

Institute of Biology, Warsaw University of Life Sciences, 02-776 Warsaw, Poland

Author to whom correspondence should be addressed.

Sustainability 2024, 16(24), 10811; https://doi.org/10.3390/su162410811

Submission received: 6 November 2024 / Revised: 3 December 2024 / Accepted: 5 December 2024 / Published: 10 December 2024

(This article belongs to the Section Pollution Prevention, Mitigation and Sustainability)

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Figure 1
Weather conditions during the experiment period. "> Figure 2
Induction curves of chlorophyll a fluorescence of investigated plants under control soil and petroleum-contaminated soil. Dactylis glomerata L. var Amba (DGA), Lolium perenne L. var. Maja (LPM) and Nira (LPN), Poa pretensis L. var. Appalachian (PPA), Phleum pretense L. var. Egida (PPE), Trifolium repens L. var Grass. Huia (TRH), relative units, n = 8. "> Figure 3
Delayed fluorescence induction curves of investigated plants under control soil and petroleum-contaminated soil. Dactylis glomerata L. var Amba (DGA), Lolium perenne L. var. Maja (LPM) and Nira (LPN), Poa pretensis L. var. Appalachian (PPA), Phleum pretense L. var. Egida (PPE), Trifolium repens L. var Grass. Huia (TRH), relative units, n = 8. "> Figure 4
Modulated light reflection at 820 nm of investigated plants under control soil and petroleum-contaminated soil. Dactylis glomerata L. var Amba (DGA), Lolium perenne L. var. Maja (LPM) and Nira (LPN), Poa pretensis L. var. Appalachian (PPA), Phleum pretense L. var. Egida (PPE), Trifolium repens L. var Grass. Huia (TRH), relative units, n = 8. "> Figure 5
Principal component analysis for control experiment (a) and petroleum contamination soil (b). Dactylis glomerata L. var Amba (DGA), Lolium perenne L. var. Maja (LPM) and Nira (LPN), Poa pretensis L. var. Appalachian (PPA), Phleum pretense L. var. Egida (PPE), Trifolium repens L. var Grass. Huia (TRH). "> Figure 6
T-Student test results of all measured parameters of tested plant species. Dactylis glomerata L. var Amba (DGA), Lolium perenne L. var. Maja (LPM) and Nira (LPN), Poa pretensis L. var. Appalachian (PPA), Phleum pretense L. var. Egida (PPE), Trifolium repens L. var Grass. Huia (TRH). The ns refer to non-significant differences, and asterisks *, **, ***, **** refer to different significance levels represented by p-values lower or equal to 0.05, 0.01, 0.001 and 0.0001 respectively. "> Figure 7
Boxplot results of the parameters Area (a), MRmin (b), and I2 (c) indicating statistically significant (or no-significant) differences in the fluorescence variables enable the impact of the contamination treatment on the state of the analyzed species. Dactylis glomerata L. var Amba (DGA), Lolium perenne L. var. Maja (LPM) and Nira (LPN), Poa pretensis L. var. Appalachian (PPA), Phleum pretense L. var. Egida (PPE), Trifolium repens L. var Grass. Huia (TRH). The blue box and circles regard control experiment, whereas the green box and triangles indicate the Petroleum contamination experiment. ">

Versions Notes

Abstract

Significant efforts have been made to develop environmentally friendly remediation methods to restore petroleum-damaged ecosystems. One such approach is cultivating plant species that exhibit high resistance to contamination. This study aimed to assess the impact of petroleum-derived soil pollutants on the photosynthetic performance of selected plant species used in green infrastructure development. A pot experiment was conducted using both contaminated and uncontaminated soils to grow six plant species under controlled conditions. Biometric parameters and chlorophyll a fluorescence measurements were taken, followed by statistical analyses to compare plant responses under stress and control conditions. This study is the first to simultaneously analyze PF, DF, and MR₈₂₀ signals in plant species exposed to petroleum contamination stress. The results demonstrated that petroleum exposure reduced the activity of both PSII and PSI, likely due to increased nonradiative energy dissipation in PSII antenna chlorophylls, decreased antenna size, and/or damage to the photosynthetic apparatus. Additionally, petroleum contamination affected the electron transport chain efficiency, limiting electron flow between PSII and PSI. The most resistant species to petroleum-induced stress were Lolium perenne, Poa pratensis, and Trifolium repens.

Keywords:

photosynthesis; phytoremediation; soil contaminations; chlorophyll a fluorescence

1. Introduction

Petroleum contamination is a major environmental concern driven by global industrialization, the ongoing growth of crude oil production, and consumption worldwide [1]. Petroleum is composed primarily of asphaltenes, saturated and aromatic hydrocarbons, and various non-hydrocarbon chemicals, many of which are hazardous [2]. These petroleum-derived substances have been extensively studied over the past few decades for their detrimental effects on the environment and living organisms [2]. Petroleum is a complex mixture of hydrocarbons composed mainly of carbon, hydrogen, oxygen, sulfur, and trace metals, which are toxic to many organisms, including plants [3].

Efforts are underway to develop environmentally friendly remediation techniques to restore ecosystems damaged by different anthropogenic pollutions [4,5].

Soil contamination by petroleum often impedes plant growth, with research showing that petroleum pollution reduces seed germination [6], plant growth rates, and overall yield [7]. These adverse effects vary depending on the plant species or variety and the level of soil contamination [7]. The varying efficiency of phytodegradation among different plant species, and even among cultivars, has been confirmed by Schwitzguébel [8]. Xie et al. [9] also note that phytoremediation is significantly more effective when using a mixture of plant species rather than a single species, with the process’s success largely dependent on the selected species.

Therefore, plant species must be chosen based on their specific resistance to the prevailing environmental conditions [10]. The accelerated breakdown of organic pollutants in soil can help alleviate the inhibitory effects of contamination on plant growth. However, to date, there has been limited research on how petroleum contamination affects the photosynthetic performance of different plant species.

The noxious effects of petroleum contamination on key physiological processes in plants (photosynthesis, water balance, mineral uptake, or dark respiration) are not thoroughly described in the current literature. To date, the resistance of specific plant species has primarily been evaluated based on morphological traits [11]. However, modern techniques now allow for assessing environmental impacts on plants before visible symptoms appear. One of the most critical functions in plants is photosynthesis, which is highly sensitive to environmental stressors [11]. Analyzing photosynthesis is one of the most reliable methods for evaluating how adverse environmental conditions affect plant development and growth [11].

To prevent stress, plants initiate several mechanisms, such as heat dissipation or fluorescence emission, to manage excess energy absorbed by chlorophyll. Chlorophyll a fluorescence (ChFl) measurements are widely used to study the response of photosynthetic organisms to stress under many conditions [12,13,14]. The dynamics of ChFl provide valued insights into the structural and functional aspects of electron transport during photosynthesis [15,16].

The fluorescence of a dark-adapted leaf is measured over 1 s after illumination, starting from initial values (F_o) and reaching maximum values (F_m), forming a curve with five key points (labeled O, K, J, I, P) [17,18]. Based on its kinetics, it is possible to analyze the photochemical efficiency of the photosynthetic apparatus. It delivers valuable insights into the structural and functional characteristics of components involved in photosynthetic electron transport, particularly photosystem II (PSII). From these curves, several parameters can be calculated, which are categorized into energy fluxes for light absorption (ABS), excitation energy trapping (TR), and the conversion of excitation energy into electron transport (ET) per reaction center (RC) in the measured sample area (CS).

All electron transfer processes between the donor and acceptor sides of PSII’s reaction centers (RCs) and all redox reactions involving electron transport between PSII and photosystem I (PSI) are reversible. Electron buildup in the transport chain between PSII and PSI causes retransfer of electrons and the possibility of recombination within the PSII RCs. This results in the reoccupation of the excited chlorophyll state in the PSII antenna through rapid energy transfer, leading to renewed excitation of the RC. Unlike prompt fluorescence (PF), in which excited chlorophyll molecules immediately emit light, delayed fluorescence (DF) occurs later, releasing energy before the original photochemical reaction fully utilizes the excitation energy [19]. DF, which decays in phases ranging from nanoseconds to minutes, is released exclusively by PSII [19,20]. Like PF, DF emission forms a transient fluorescence curve characterized by multiple maxima (I) and minima (D), whose magnitude and quantity are determined by the kinetic elements of DF [19,20].

The modulated reflectance (MR) signal, measured at 820 nm, indicates changes in the redox states of PSI RCs and plastocyanin (PC), providing insights into electron transport near plastoquinone (PQ) and PSI acceptors [21].

Several studies have confirmed the feasibility of simultaneously assessing photosynthetic efficiency under varying environmental conditions, including abiotic and biotic stressors, using PF, DF, and MR measurements [22,23]. However, limited research exists on how petroleum contamination affects grassland herbaceous plants. The capability of chlorophyll a fluorescence to sense changes in PSII and PSI in these species under petroleum contamination is not well documented.

In this study, PF, DF, and MR₈₂₀ signals were measured simultaneously using the multifunctional plant efficiency analyzer (MPEA-2; Hansatech Instruments, Pentney, UK) on the seeds of five plant species exposed to stress from petroleum contamination. It was hypothesized that petroleum-induced stress could affect multiple sites along the photosynthetic electron transport chain, leading to varying responses among plant species.

The primary objective of this research was to conduct a detailed in vivo analysis and comparison of changes in PSII photochemistry induced by petroleum contamination in seven species of herbaceous plants. This was achieved by analyzing parameters derived from prompt and delayed chlorophyll a fluorescence recordings and MR₈₂₀ signals. The study aimed to identify potential marker parameters for petroleum-induced stress by observing changes in the photosynthetic apparatus. Additionally, we evaluated whether chlorophyll a fluorescence data might be used to distinguish plant species with greater resistance to petroleum-contaminated soils. Biomass production was also correlated with these findings. The results are expected to enhance our understanding of the photosynthetic performance of these plant species under stress and expand our knowledge of the photosynthetic system’s response to petroleum contamination.

2. Materials and Methods

2.1. Experimental Conditions

The soil used in the study was contaminated with petroleum products due to a pipeline spill caused by its damage on 27 June 2020. It came from the Leśny Zakład Doświadczalny SGGW w Rogowie, the Forest Experimental Station at Rogów, Warsaw University of Life Sciences. Damage to the hose that was erroneously connected to PERN S.A.’s fuel pipeline through the Płock–Koluszki connection was the cause of the soil contamination. An oil and gasoline mixture leaked into the ground environment (inside Rogów PGR, Rogów municipality, geographical coordinates N 51.798503, E 19.843632). In order to conduct the tests, soil from the top layer of the soil profile—which was kept in a dump during the rescue operation—was removed, and its polycyclic aromatic hydrocarbon (PAH) concentration was examined. The total hydrocarbons in the soil from the contaminated region were determined to be C6–C12 and C12–C35. The sum of hydrocarbons C12–C35 was determined in accordance with the standard PN-EN ISO 16703:2011, Soil quality—Determination of hydrocarbon content in the range C10 to C40 by gas chromatography [24]. The total of C6–C12 hydrocarbons and the sum of C12–C35 hydrocarbons exceeded the allowable content of hydrocarbons by more than nine times, or 40%, according to the findings. The control variant was soil taken from above the contamination site in the ravine area (geographical coordinates N 51.797902, E 19.844337). Plants, stones, and visible invertebrate parts were first removed to prepare the examined soil material for further analytical procedures. The samples were dried at room temperature for five weeks to create an air-stable, dry bulk. A 2 mm diameter sieve was used to filter the dried materials. The granulometric composition and other physical and chemical properties of the soil were then ascertained by applying standard soil techniques as outlined by Małuszyński and Małuszyńska [25].

The pot experiment was conducted from 6 June–30 September 2021 in the Warsaw University of Life Sciences plant growth facility under weather conditions (Figure 1) without natural rainfall influence. Pots (96 pcs. with a capacity of 500 cm³ each) were filled with soil at 700 g per pot. Half of them were the control variant (soil not contaminated with petroleum derivatives—C), and the other half was the variant with soil collected from the oil pipeline spill site (P). The pots were sown with five species in pure sowing in 8 replicates for each research variant, i.e., Dactylis glomerata L. var. Amba (DGA), Lolium perenne L. var. Maja (LPM), Lolium perenne L. var. Nira (LPN), Poa pratensis L. var. Appalachian (PPA), Pleum pratense L. var. Aegida (PPE), and Trifolium repens L. var. Grasslands huia (TRH). The number of seeds of individual species was calculated considering the sowing rate per 1 ha, thousand seed weight, and vase area (86.55 cm²). It was, respectively, for DGA—15 pcs., LPM and LPN—16 pcs., PPA—55 pcs., PPE—20 pcs., and TRH—20 pcs.

After the growing season, the aboveground mass of the plants that grew in the pot was cut and dried to an air-dry mass. The belowground biomass was separated from the soil by washing the soil monoliths from each pot on a sieve with a mesh size of 0.2 mm [26]. The dried biomass was weighed on a Mettler PM200 scale and expressed in grams per pot. The biomass structure in the pot was determined by calculating the ratio of belowground biomass to aboveground biomass (R/P) and aboveground to underground biomass (P/R).

2.2. Chlorophyll a Fluorescence Measurements

Chlorophyll a fluorescence measurements were conducted on 27 August 2021. Eight leaves from each treatment were dark adapted for 30 min before measurements. The M-PEA Chlorophyll Fluorescence Measurement System (Multi-Function Plant Efficiency Analyzer, Hansatech, Pentney, UK) was utilized, following this measurement protocol: a measurement duration of 1.0 s, actinic light intensity of 3000 µmol·m⁻²·s⁻¹, and wavelength of 635 ± 10 nm. The M-PEA fluorimeter allowed for the simultaneous measurement of 3 fluorescence signals: prompt chlorophyll a fluorescence (PF), delayed chlorophyll a fluorescence (DF), and 820 nm light reflection (MR₈₂₀). The JIP-test was employed to recalculate characteristic points of photoinduced chlorophyll a fluorescence transients into specific parameters [18]. Also, the characteristic points (I₁, I₂, and D₂) of the DF curve and their ratios ((I₁ − D₂)/D₂ and I₁/I₂) were analyzed [20]. The MR signal was measured simultaneously with PF and DF. The MRt is the modulated 820 nm reflection intensity at time t, and MR₀ is the value of the 820 nm reflection of the sample at the onset of the actinic illumination. The ratio MR_t/MR₀ was calculated. The minimum (MR_min) and maximum (MR_max) were used to calculate the ∆MR_fast (difference between MR₀ and MR_min) and ∆MR_slow (difference between MR_max and MR_min) parameters. All measured and calculated parameters are listed in Table 1.

2.3. Statistical Analysis

The significance of differences between means and multivariate correlation analysis PCA method [27] were used for statistical analyses, which allowed for determining the relationships between biometric parameters of plants and environmental elements represented by chlorophyll a fluorescence parameters. The analysis was carried out separately for plants growing in their natural state and for plants subjected to stress resulting from the occurrence of petroleum derivatives in the soil. In addition, k-means [28] was used to establish homogeneous groups of observations (plant species taking into account repetitions). A so-called biplot principal component analysis (PCA) illustrating the mutual relationships between explanatory variables and observations represented by plant species, considering repetitions, was used to visualize the research results [29].

The t-Student independent samples test [30] was used to assess statistically significant differences between the biometric and fluorescence test parameters of plants (C—control) and plants exposed to the stress of petroleum substances (P—petroleum). Consequently, the statistical analysis results in boxplots and the heat map illustrate statistically significant differences between the control (C) and the treatment (P) were developed.

3. Results

3.1. Morphological Parameters

Petroleum contamination significantly influenced the analyzed morphological parameters of tested plant species (Table 2). The average value of belowground mass in non-stressed plants was 2.31 g fresh weight/pot. Petroleum contamination caused a significant decrease in its value to 1.30 g fresh weight/pot. The average aboveground mass in non-stressed plants was 1.75 g fresh weight/pot. Petroleum contamination caused a significant decrease in its value to 0.61 g fresh weight/pot. The plant root ratio (P/R) in non-stressed plants was 0.79 and increased under petroleum contamination to 0.88. The root plant (R/P) ratio in non-stressed plants was 1.85 and increased under petroleum contamination to 2.35.

3.2. Induction Curves of Chlorophyll a Fluorescence Under Petroleum Contamination

Contamination with petroleum-derived substances influenced OJIP curve course (Figure 2). Differences were also noted between the various species in response to the contamination. In both studied varieties of L. perenne, P. pretensis, and P. pretense, a reduction in the course of the curves was visible starting from point J. In contrast, in D. glomerata such changes were noticeable along the entire curve length (from point O to P). In T. repens, fluorescence values were increased at points J and I.

From the delayed fluorescence (DF) curves measured in plants, it can be concluded that contamination with petroleum-derived substances was also affected in comparison to control on this (Figure 3). The value of point I₁ was significantly reduced in all studied species except for the L. perenne var. Nira and T. repens. In P. pretensis, the value of this point decreased by about 20%, while in the other species, it decreased by 30–40%. No significant changes in the value of point I₂ were observed in P. pretensis, whereas the most significant changes were found in the Lolium perenne var. Maja and P. pretense. Significant changes in the value of point D₂ were observed only in these species.

The kinetics of modulated light reflection at 820 nm was also modified by contamination with petroleum-derived substances (Figure 4). The MR_min point of the curve was the most reduced under the influence of contamination compared to the control in D. glomerata. Similar, though not as significant, changes were also observed in L. perenne var. Maja, P. pretensis, and P. pretense. No changes in the value of this point were found in L. perenne var. Nira and T. repens. The MR_max point of the curve was also significantly reduced compared to the control, but only in two species: T. repens and D. glomerata.

3.3. Relationship Between Chlorophyll a Fluorescence and Morphological Parameters Under Petroleum Contamination

The vector graphs (Figure 5) show the relative “contribution” of each input variable to the formation of the principal components (Dim1 and Dim2). The vector magnitude indicates the influence on the corresponding ChFl parameter, and the vector direction depends on this impact on the Dim1 and Dim2 values. First, it should be underlined that under control conditions, the formation of the principal components was different compared to stressed plants. Also, the analyzed ChFl parameters had diverse sensitivity to petroleum contamination and contributions to forming principal components. Under control conditions, the first principal component (Dim1) modifications explained 59.6% of total changes, and the second component (Dim2) reflected 11.8%. In this treatment, the parameters can be divided into three groups. The first group is Area, Δ_Ro, φ_Eo, φ_Ro, φ_Po, ψ_Eo, PI_abs, PI_tot, ΔMR_slow, ΔMR_fast, MR_max, I₁, I₂ and (I₁ − D₂)/D₂. Those parameters are correlated with plant mass and P/R ratio. The second group contains t_Fm, F_o, N, F_m, F_v, D₂, and MR₀. Those parameters were correlated with root mass. The last group contains φ_Do, DI_o/RC, ABS/RC, TR_o/RC, M_o, V_j, V_i, MR_min, and I₁/I₂. There was a correlation with the R/P ratio.

Under petroleum contamination, the first principal component (Dim1) modifications explained 51.8% of total variability, and the second component (Dim2) reflected 15.9%. Like control conditions, the bioindicator variables can be divided into three groups in this treatment. The main group (2) contains Area, F_o, F_m, F_v, Δ_Ro, φ_Eo, φ_Ro, φ_Po, ψ_Eo, PI_abs, PI_tot, ΔMR_slow, ΔMR_fast, MR_max, I₁, I₂, D₂, I₁/I₂ and (I₁ − D₂)/D₂. Those parameters were correlated with plant mass and P/R ratio. The next group (3) contains DI_o/RC t_Fm, φ_Do, N, and Sm. Those parameters were correlated with root mass and R/P ratio. The blue variables group contains ABS/RC, Et_o/RC, TR_o/RC, M_o, V_j, V_i, and MR_min.

This analysis made it possible to correlate chlorophyll a fluorescence parameters with different plant species. A robust correlation was observed between the fluorescence parameters and Trifolium repens (TRH). These relationships were noted regardless of whether or not the clover was subjected to stress. Essentially, the same variables allowed for a clear distinction of non-grass plants (Figure 5a).

In the control group, it can be observed that grasses of the Lolium perenne varieties primarily dominated the identified cluster 2 and showed a correlation with the MR₈₂₀ parameter, represented by MR_min. This group of observations was negatively correlated with the PF variables group (e.g., F_v and F_m) and the DF variables group (I₁ and D₂). The plant group dominated by DGA (cluster 3) was mainly correlated with PF parameters (F_o, N, and t_Fm) and primarily explained DIM2.

In the case of contaminated soil (Figure 5b), the TRH group (homogeneous group, Cluster 2) was identified similarly to the control group. The main variables characterizing this group include fluorescence parameters such as ϕ_Ro, Area, ψ_Eo, PI_abs, PI_tot, and ΔMR_slow. Clusters 1 and 3 consist of a mix of different plant species, making it difficult to determine the typical grouping of observations in these clusters. However, it should be noted that Cluster 3 is located almost at the center of the biplot (Figure 5b), indicating that the fluorescence parameters oscillate around average values for this group of observations. Despite significant species diversity, the first group of plants subjected to oil contamination is mainly dominated by Lolium perenne plants, which explains the first PCA component. The plants in this group were mainly associated with MR_min, V_i, and M_o parameters. The explanatory variables group 3 (in gray in Figure 5b) was negatively correlated with the plants forming Cluster 3.

Based on the t-Student test, it can be concluded that there is a difference in resistance to petroleum contamination among the six studied species (Figure 6). The species with minor variability in morphological and physiological parameters were P. pratensis and L. perenne var. Nira, while the species with the most significant variability was D. glomerata. The most variable morphological parameter across all species was plant mass (it was significantly changed between control and petroleum contamination in all species). In contrast, the above-ground to below-ground biomass ratios were not sensitive to this stress. Among the JIP test parameters, Area was the most sensitive (this parameter was changed in all tested plant species), followed by F_v (this parameter did not change only in T. repens). Many of these parameters were significantly altered in two species: F_v/F_m, V_j, M_o, Sm, ABS/RC, RE_o/RC, DI_o/RC, ψ_Eo, Φ_Po, Φ_Eo, Φ_Do, as well as PI_abs and PI_tot. Among the parameters obtained from modulated light reflection at 820 nm, ΔMR_slow, and ΔMR_fast were the most sensitive, while MR_o was the least sensitive. I₁ and I₂ were the most sensitive among the DF parameters, while the I₁/I₂ ratio was the least sensitive.

One of the study’s main objectives is to find the crucial photosynthesis parameters reflected in recognizing the contamination stress. Based on the results of the t-test analysis (Figure 6), three parameters representing three different chlorophyll a fluorescence signals (PF, DF, and MR₈₂₀) were selected to identify the petroleum contamination stress. The Area parameters seem to be the crucial fluorescence parameter enabling recognition of species stress upon petroleum contamination. The p-value indicates that concerning the analyzed species, the parameter Area is significantly less upon the contamination treatment than in the control experiment (Figure 7).

From the MR₈₂₀ and DF groups, the parameters MR_min and I₂ can help recognize the plant petroleum stress. This also indirectly indicated soil contamination due to the appearance of stress in most of the analyzed plant species.

4. Discussion

The rising exploration of petroleum products has increased spill accidents during extraction, refining, and transportation, which leads to significant soil contamination. The primary effects of such pollution are alterations in soil physicochemical properties, such as reduced availability of oxygen, water, and nutrients. This situation negatively impacts plant development, growth, biomass, and seed germination rates [31]. In our study, the negative effect of petroleum contamination on morphological parameters was confirmed. These findings agree with previous studies [6,32].

This study aimed to conduct a detailed in vivo analysis and comparison of changes in PSII photochemistry and plant growth inhibition caused by petroleum contamination stress in five species of herbaceous plants. This was achieved by analyzing parameters from prompt and delayed chlorophyll a fluorescence recordings and the MR₈₂₀ signal. The goal was to identify changes in the photosynthetic apparatus and find potential marker parameters of petroleum-induced stress.

It has been well documented that plants can uptake organic pollutants from polluted soils via the soil-to-plant pathway, where roots absorb and translocate these compounds throughout the plant [33]. Given their proximity to petroleum contaminants, roots are particularly vulnerable to the adverse effects of petroleum, including water, anaerobic, osmotic, and oxidative stress due to nutrient deficiencies [34]. Petroleum pollution also affects root structure development [35], leading to adverse changes in plant water status [35,36]. Consistent with these conclusions, our results showed that root mass production was negatively affected by petroleum contamination.

Numerous studies have demonstrated the potential for measuring PF, DF, and modulated reflectance to reveal changes in photosynthetic efficiency under various stress conditions [22,23]. However, there is no information about the effects of petroleum contamination on photosynthesis in specific plant species, particularly the ability of ChFl to detect changes in PSII and PSI in seedlings. This study used the multifunctional plant efficiency analyzer (MPEA-2; Hansatech Instruments, Pentney, UK) to measure PF, DF, and MR₈₂₀ signals in plants subjected to petroleum-induced stress.

We confirmed the hypothesis that petroleum stress can affect multiple sites within the photosynthetic electron transport chain, with species-specific variations, notably in all tested species except Trifolium repens L. var. Grassland huia, a decline in the OJIP curve from the J to P point was observed. Changes in the J–I phase, which likely reflect reductions in intersystem electron carriers Quinone Binding site (Q_B), Plastoquinone (PQ), and Cytochrome b6f complex (Cyt b6/f), were found to be mainly influenced by the size of the PQ pool and the reaction rates that follow [37]. The I–P phase, associated with the reduction of Oxidized Plastocyanin (PC⁺), primary reaction center chlorophyll in PSI (P700⁺), and PSI acceptors, suggested a chloroplast disruption, possibly due to the erosion of ferredoxin and the diversion of electrons to alternative acceptor pools [38].

Additionally, the Area parameter, representing the area above the ChFl induction curve during the rapid phase, decreased in all tested species. This decline indicates a blockage in electron transfer from PSII reaction centers to the quinone pool, pointing to stress-induced disruptions in PSII function [39]. Reductions in the F_m and F_v parameters were also observed in all species except Trifolium repens L. var. Grassland huia suggests an increase in inactive PSII reaction centers and impaired PSII activity [40]. Low values of F_v indicate that excitation energy is being dissipated as heat, a typical response to environmental stress [41,42,43].

Petroleum contamination also significantly affected the DF signal. The first induction maximum (I₁) was altered in all species except Lolium perenne var. Nira and Trifolium repens. This parameter correlates with the formation and dissipation of “light-emitting states” in PSII, which are essential for DF emission. The second maximum (I₂), influenced in all species except Poa pratensis, is associated with reoxidation of the PQ pool and partial reopening of closed PSII reaction centers during PSI activation [40]. However, the ratios of I₁/I₂ and (I₁ − D₂)/D₂ were not as significantly impacted by petroleum contamination, though changes in the (I₁ − D₂)/D₂ ratio suggest reduced electron transport chain efficiency and PSII activity [19].

The MR₈₂₀ signal was significantly altered in all species tested, reflecting modifications in the electron flow between PSII and PSI. Petroleum contamination affected both the ∆MR_slow and ∆MR_fast parameters, reliable indicators of PSI status [21]. These changes indicate alterations in P700⁺ redox state kinetics under strong stress, warranting further investigation [42,44].

5. Conclusions

To our knowledge, this is the first study to simultaneously analyze PF, DF, and MR₈₂₀ signals in these plant species grown under petroleum contamination stress. While the photosynthetic machinery’s response to this type of stress is highly complex, wide-ranging analyses of these three chlorophyll a fluorescence signals provide valuable tools for diagnosing the influence of petroleum contamination on the photosynthetic process. Our results demonstrated that petroleum exposure decreased the activity of both PSII and PSI. This reduction may be attributed to increased nonradiative energy dissipation by PSII antenna chlorophylls, decreased PSII antenna size, and/or reduced photosynthetic apparatuses with fully closed PSII reaction centers (RCs).

DF analysis further revealed that petroleum contamination may lead to the photochemical accumulation of certain redox states and a decrease in the plastoquinone (PQ) pool. Additionally, the efficiency of the electron transport chain was compromised by the loss of PSII activity and damage to PSI function. A significant effect on the MR signal was observed across all tested plant species, which may indicate a limitation in electron flow from PSII to PSI or on the acceptor side of PSI. Our results also indicated the impaired PSII capacity to transfer electrons to the cross-system electron transport chain, disruption in the connection between PSII and PSI, and/or inhibition of the PSI acceptor site.

The decreased reduction activity of PSI could also result from Significant differences in the resistance of the tested species to petroleum-induced stress. Plants resistant to petroleum contamination are characterized by their ability to maintain higher photosynthetic efficiency despite the stress caused by pollution. The study showed that some species, such as Trifolium repens L. var. Grassland huia, Lolium perenne L. var. Nira and Poa pratensis L. var. Appalachian exhibited smaller changes in the OJIP curve, suggesting less sensitivity to damage in the photosynthetic system. Additionally, these plants better preserved parameters related to PSII function, such as F_v and F_m, and showed fewer noticeable changes in the DF and MR signals, indicating less disruption in the electron transport chain and photosynthetic apparatus function. These findings may aid in selecting plant species with higher photosynthetic performance potential under stress conditions, which is critical for the effective reclamation of post-industrial soils contaminated by petroleum. However, to more precisely assess the impact of petroleum contamination, it is recommended to extend future research to include a broader range of contamination concentrations. Also, exposure of plants to petroleum contamination under controlled laboratory conditions may not reflect the actual field conditions. In natural conditions, the reaction of plants to petroleum contamination can be more complex. Moreover, the study did not consider the potential interactions with other types of stresses that may be present in the same environment as petroleum-based contaminants, which could further impact the results.

Author Contributions

Conceptualization, P.D., B.P., M.J.M. and I.M.; methodology, B.P., M.J.M., I.M. and T.G.; validation, T.G.; investigation, B.P., M.J.M. and I.M.; data curation, P.D., A.H.B.-D., M.J.M. and I.M.; writing—original draft preparation, P.D.; writing—review and editing, H.M.K.; visualization, T.G. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Polish Ministry of Science and Higher Education for statutory activities of the Institute of Environmental Engineering, Warsaw University of Life Sciences—SGGW.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Conflicts of Interest

The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

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Figure 1. Weather conditions during the experiment period.

Figure 2. Induction curves of chlorophyll a fluorescence of investigated plants under control soil and petroleum-contaminated soil. Dactylis glomerata L. var Amba (DGA), Lolium perenne L. var. Maja (LPM) and Nira (LPN), Poa pretensis L. var. Appalachian (PPA), Phleum pretense L. var. Egida (PPE), Trifolium repens L. var Grass. Huia (TRH), relative units, n = 8.

Figure 3. Delayed fluorescence induction curves of investigated plants under control soil and petroleum-contaminated soil. Dactylis glomerata L. var Amba (DGA), Lolium perenne L. var. Maja (LPM) and Nira (LPN), Poa pretensis L. var. Appalachian (PPA), Phleum pretense L. var. Egida (PPE), Trifolium repens L. var Grass. Huia (TRH), relative units, n = 8.

Figure 4. Modulated light reflection at 820 nm of investigated plants under control soil and petroleum-contaminated soil. Dactylis glomerata L. var Amba (DGA), Lolium perenne L. var. Maja (LPM) and Nira (LPN), Poa pretensis L. var. Appalachian (PPA), Phleum pretense L. var. Egida (PPE), Trifolium repens L. var Grass. Huia (TRH), relative units, n = 8.

Figure 5. Principal component analysis for control experiment (a) and petroleum contamination soil (b). Dactylis glomerata L. var Amba (DGA), Lolium perenne L. var. Maja (LPM) and Nira (LPN), Poa pretensis L. var. Appalachian (PPA), Phleum pretense L. var. Egida (PPE), Trifolium repens L. var Grass. Huia (TRH).

Figure 6. T-Student test results of all measured parameters of tested plant species. Dactylis glomerata L. var Amba (DGA), Lolium perenne L. var. Maja (LPM) and Nira (LPN), Poa pretensis L. var. Appalachian (PPA), Phleum pretense L. var. Egida (PPE), Trifolium repens L. var Grass. Huia (TRH). The ns refer to non-significant differences, and asterisks *, **, ***, **** refer to different significance levels represented by p-values lower or equal to 0.05, 0.01, 0.001 and 0.0001 respectively.

Figure 7. Boxplot results of the parameters Area (a), MRmin (b), and I2 (c) indicating statistically significant (or no-significant) differences in the fluorescence variables enable the impact of the contamination treatment on the state of the analyzed species. Dactylis glomerata L. var Amba (DGA), Lolium perenne L. var. Maja (LPM) and Nira (LPN), Poa pretensis L. var. Appalachian (PPA), Phleum pretense L. var. Egida (PPE), Trifolium repens L. var Grass. Huia (TRH). The blue box and circles regard control experiment, whereas the green box and triangles indicate the Petroleum contamination experiment.

Table 1. The list of measured and calculated parameters, modified based on information included in [20].

JIP Test Parameters
t_Fm	Time (in ms) to reach the maximal fluorescence FP (meaningful only when F_P = F_m)
Area	Total complementary area between the fluorescence induction curve and F = FP (meaningful only when F_P = F_m)
F_o ≅ F50 µs or ≅ F20 µs	Fluorescence when all PSII RCs are open (≅to the minimal reliable recorded fluorescence)
F_m (= F_P)	Maximal fluorescence, when all PSII RCs are closed (=F_P when the actinic light intensity is above 500 µmol(photon) m⁻² s¹ and provided that all RCs are active as Q_A-reducing)
F_v ≡ F_m − F_o	Maximal variable fluorescence
F_v/F_m	The maximum quantum yield for primary photochemistry
ABS/RC = M_o × (1/V_J) × (1/ϕ_Po)	Absorption flux (exciting PSII antenna Chl a molecules) per RC (also used as a unit-less measure of PSII apparent antenna size)
TR_o/RC = M_o × (1/V_J)	Trapped energy flux (leading to Q_A reduction), per RC
RE_o/RC = M_o × (1/V_J) × (1 − V_I)	Electron flux reducing end electron acceptors at the PSI acceptor side, per RC
ET_o/RC = M_o × (1/V_J) × (1 − V_J)	Electron transport flux (further than Q_A⁻), per RC
DI_o/RC = (ABS/RC) − (TR_o/RC)	An RC does not intercept energy flux, dissipate in heat, fluorescence, or transfer to other systems at time t = 0.
ϕ_Po ≡ TR_o/ABS = [1 − (F_o/F_m)]	The maximum quantum yield for primary photochemistry
ϕ_Eo ≡ ET_o/ABS = [1 − (F_o/F_m)] × (1 − V_J)	Quantum yield for electron transport (ET)
ϕ_Ro ≡ RE_o/ABS = [1 − (F_o/F_m)] × (1 − V_I)	Quantum yield for reduction of end electron acceptors at the PSI acceptor side (RE)
ψ_Eo ≡ ET_o/TR_o = (1 − V_J)	Efficiency/probability that an electron moves further than Q_A⁻
δ_Ro ≡ RE_o/ET_o = (1 − V_I)/(1 − V_J)	Efficiency/probability with which an electron from the intersystem electron carriers is transferred to reduce end electron acceptors at the PSI acceptor side (RE)
N = S_m × (M_o/V_J)	Turnover number (expresses how many times Q_A is reduced in the time interval from 0 to t_Fm)
S_m = (Area)/(F_m − F_o)	Normalized total area above the OJIP curve
PI_abs	Performance index for energy conservation from photons absorbed by PSII until the reduction of intersystem electron acceptors
PI_tot	Total performance index for energy conservation from photons absorbed by PSII until the reduction of PSII end electron acceptors
Delayed chlorophyll a fluorescence parameters
I₁ and I₂	Maxima of DF induction curve
D₂	Minimum of DF induction curve
Modulated at 820 nm reflection parameters
MR_o	Modulated 820 nm reflection intensity at time “0”.
MR_min	Minimum of modulated 820 nm reflection intensity
MR_max	Maximum of modulated 820 nm reflection intensity
∆MR_fast	Fast phase (oxidation) of reflection intensity = MR_o − MR_min
∆MR_slow	Slow phase (reduction) of reflection intensity = MR_max − MR_min

Table 2. The generalized biometric parameters of the investigated plants under control soil and petroleum-contaminated soil: belowground mass (roots mass, g fresh weight/pot), aboveground mass (plant mass, g fresh weight/pot), and their ratios. The data were averaged for all tested plant species. The ns refer to non-significant differences, and asterisks *, **, *** refer to different significance levels represented by p-values lower or equal to 0.05, 0.01, and 0.001, respectively.

Treatment	Variable	Min	Max	Mean	Stdev	p Value
Roots mass	Control	0.89	5.26	2.31	0.87	***
	Petroleum contamination	0.46	6.54	1.75	1.55	***
Plant mass	Control	0.17	2.80	0.79	0.60	***
	Petroleum contamination	0.36	5.88	1.85	1.11	***
P/R	Control	0.62	2.54	1.30	0.44	ns
P/R	Petroleum contamination	0.15	2.09	0.61	0.42	ns
R/P	Control	0.12	3.30	0.88	0.91	ns

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Dąbrowski, P.; Małuszyńska, I.; Małuszyński, M.J.; Pawluśkiewicz, B.; Gnatowski, T.; Baczewska-Dąbrowska, A.H.; Kalaji, H.M. Photosynthetic Efficiency of Plants as an Indicator of Tolerance to Petroleum-Contaminated Soils. Sustainability 2024, 16, 10811. https://doi.org/10.3390/su162410811

AMA Style

Dąbrowski P, Małuszyńska I, Małuszyński MJ, Pawluśkiewicz B, Gnatowski T, Baczewska-Dąbrowska AH, Kalaji HM. Photosynthetic Efficiency of Plants as an Indicator of Tolerance to Petroleum-Contaminated Soils. Sustainability. 2024; 16(24):10811. https://doi.org/10.3390/su162410811

Chicago/Turabian Style

Dąbrowski, Piotr, Ilona Małuszyńska, Marcin J. Małuszyński, Bogumiła Pawluśkiewicz, Tomasz Gnatowski, Aneta H. Baczewska-Dąbrowska, and Hazem M. Kalaji. 2024. "Photosynthetic Efficiency of Plants as an Indicator of Tolerance to Petroleum-Contaminated Soils" Sustainability 16, no. 24: 10811. https://doi.org/10.3390/su162410811

APA Style

Dąbrowski, P., Małuszyńska, I., Małuszyński, M. J., Pawluśkiewicz, B., Gnatowski, T., Baczewska-Dąbrowska, A. H., & Kalaji, H. M. (2024). Photosynthetic Efficiency of Plants as an Indicator of Tolerance to Petroleum-Contaminated Soils. Sustainability, 16(24), 10811. https://doi.org/10.3390/su162410811

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