Plant Family-Specific Impacts of Petroleum Pollution on Biodiversity and Leaf Chlorophyll Content in the Amazon Rainforest of Ecuador

Phytoremediation potential of Bermuda grass (Cynodon dactylon (L.) pers.) in soils co-contaminated with polycyclic aromatic hydrocarbons and cadmium

Soils co-contaminated with polycyclic aromatic hydrocarbons (PAHs) and cadmium (Cd) have serious environmental impacts and are highly toxic to humans and ecosystems. Phytoremediation is an effective biotechnology for the remediation and restoration of PAH- and Cd-polluted soils. Pot experiments were conducted to investigate the individual and combined effects of PAHs (1238.62 mg kg-1) and Cd (23.1 mg kg-1) on the phytoremediation potential of Bermuda grass grown in contaminated soils. Bermuda grass exhibited a significant decrease in plant growth rate, leaf pigment content, root activity, plant height and biomass and a remarkable increase in malondialdehyde content and electrolyte leakage when grown in PAH- and Cd-contaminated soils compared with grass grown in uncontaminated soils. The activity of soil enzymes, including urease, alkaline phosphatase, sucrose, and fluorescein diacetate hydrolysis, were reduced in soil with PAH and Cd stress. Furthermore, the toxicity of combined PAHs and Cd on Bermuda grass growth and soil enzyme activity was much higher than that of PAH or Cd stress alone, suggesting a synergistic effect of PAHs and Cd on cytotoxicity. To scavenge redundant reactive oxygen species and avoid oxidative damage, Bermuda grass increased plant catalase, superoxide dismutase, and peroxidase activity and soluble sugar and proline content. The bioconcentration factor of Cd in Bermuda grass grown under Cd alone and combined PAH and Cd exposure was greater than 1 for both, suggesting that Bermuda grass has a high Cd accumulation ability. Under PAH alone and combined PAH and Cd exposure conditions, a higher PAH removal rate (41.5-56.8%) was observed in soils planted with Bermuda grass than in unplanted soils (24.8-29.8%), indicating that Bermuda grass has a great ability to degrade PAHs. Bermuda grass showed great phytoremediation potential for the degradation of PAHs and phytoextraction of Cd in co-contaminated soils.

Is the anemia in men an effect of the risk of crude oil contamination?

Pollution from oil spills can seriously affect many ecosystem processes and human health. Many articles have evaluated the impact of oil spills on human health. However, most of these articles focus on occupational exposure. The effect on people living in the areas affected by oil pollution is rarely studied. Approximately 640 million people worldwide live in areas at risk of oil pollution. Thus, studying the impact of this pollution on human health should be a priority. Here, we evaluate the presence of anemia in relation to crude oil exposure in men living in areas at risk of oil contamination in the Ecuadorian Amazon (Orellana and Napo). We evaluated the hematological and biochemical parameters of 135 participants. We divided the participants into three groups according to exposure: low, medium, and high. Our results showed a significant association between exposure risk and hemoglobin and hematocrit concentration. Groups with medium- and high- contamination exposure had levels below normal values in hemoglobin and hematocrit in more than 30% and 26% of the population, respectively. In conclusion, we found that crude oil affected human health, and the prevalence of the anemia in men was dependent of the level of contamination.

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Biochemical and hematological parameters were analyzed of population of Amazon of Ecuador.

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A relationship between exposure to crude oil and anemia in men was observed.

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GLM did not show effects on monocytes, granulocytes, lymphocytes, or red blood cells.

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OR significant effects of the level of risk on hematocrit were evidenced.

Mutual effects of crude oil and plants in contaminated soil: a field study

The effect of oil contamination on growth of mono- and dicotyledonous plants (clover and ryegrass), on the one hand, and the effect of plants on oil biodegradation in soil, on the other hand, were studied in a long-term field experiment. It was found that plants respond differently to oil contamination of soddy-podzolic soil. Clover was more resistant to oil than ryegrass. Biosynthesis of photosynthetic pigments (chlorophylls and carotenoids) was not disturbed in clover, and the plant yield was fully restored by the end of the third growing season. The content of oxidative enzymes in clover leaves was 2-10 times higher than in ryegrass. Biological activity of soil planted with clover was 1.5-2 times higher correlating with the biochemical parameters of plants. Higher basal respiration in soil planted with clover corresponded to the enhanced oil biodegradation. The differences in the carbon of oil products between soils planted with clover and ryegrass appeared at the end of the third growing season at high doses of oil (5 and 10 L m^-2).

A novel approach for long-term spectral monitoring of desert shrubs affected by an oil spill

Crude oil pollution is a global environmental concern since it persists in the environment longer than most conventional carbon sources. In December 2014, the hyper-arid Evrona Nature Reserve, Israel, experienced large-scale contamination when crude oil spilled. The overarching goal of the study was to investigate the possible changes, caused by an accidental crude oil spill, in the leaf reflectance and biochemical composition of four natural habitat desert shrubs. The specific objectives were (1) to monitor the biochemical properties of dominant shrub species in the polluted and control areas; (2) to study the long-term consequences of the contamination; (3) to provide information that will assist in planning rehabilitation actions; and (4) to explore the feasibility of vegetation indices (VIs), along with the machine learning (ML) technique, for detecting stressed shrubs based on the full spectral range. Four measurement campaigns were conducted in 2018 and 2019. Along with the various stress indicators, field spectral measurements were performed in the range of 350-2500 nm. A regression analysis to examine the relation of leaf reflectance to biochemical contents was carried out, to reveal the relevant wavelengths in which polluted and control plants differ. Vegetation indices applied in previous studies were found to be less sensitive for indirect detection of long-term oil contamination. A novel spectral index, based on indicative spectral bands, named the "normalized blue-green stress index" (NBGSI), was established. The NBGSI distinguished significantly between shrubs located in the polluted and in the control areas. The NBGSI showed a strong linear correlation with pheophytin a. Machine learning classification algorithms obtained high overall prediction accuracy in distinguishing between shrubs located in the oil-polluted and the control sites, indicating internal component differences. The findings of this study demonstrate the efficacy of indirect and non-destructive spectral tools for detecting and monitoring oil pollution stress in shrubs.

Monitoring natural and anthropogenic plant stressors by hyperspectral remote sensing: Recommendations and guidelines based on a meta-review

This review outlines the advances achieved in monitoring natural and anthropogenic plant stressors by hyperspectral remote sensing over the last 50 years. A broad diversity of methods based on field and imaging spectroscopy were developed in that field for precision farming and environmental monitoring purposes. From the 466 articles reviewed, we identified the main factors to consider to achieve accurate monitoring of plant stress, namely: The plant species and the stressor to monitor, the goal (detection or quantification), and scale (field or broad-scale) of monitoring, and the need for controlled experiments. Based on these factors, we then provide recommendations and guidelines for the development of reliable methods to monitor 11 major biotic and abiotic plant stressors. For each stressor, the effects on plant health and reflectance are described and the most suited spectral regions, scale, spatial resolution, and processing approaches to achieve accurate monitoring are presented. As a perspective, we discuss two major components that should be implemented in future methods to improve stress monitoring: The discrimination of plant stressors with similar effects on plants and the transferability of the methods across scales.

Politics of the natural vegetation to balance the hazardous level of elements in marble polluted ecosystem through phytoremediation and physiological responses

The current paper evaluates the phytoremediation ability and physiological responses of selected resistant plant species to the hazardous levels of elements in the marble waste polluted ecosystem. Preliminary results demonstrate that all the indicator/resistant plant species i.e., Ailanthus altissima, Arundo donax, Cynodon dactylon, Erigeron canadensis, Cannabis sativa, Ficus carica, Lathyrus aphaca, Morus alba, Populus alba, Robinia pseudoacacia and Vitex negundo were the best Phyto-extractors and Phyto-stabilizers for most of the heavy metals in general and Mg, Ca, Fe, Cu and Na in particular (at p < 0.05). Structural Equation Modeling confirmed that marble waste pollution has a direct and significant (R² =0.80) impact on proline synthesis and hence a role in combating the pollution. Chlorophyll content decreased by 4% in studied plant species when the concentration of pollutants increased. It is concluded that the studied bio-indicators - the abundant plant species of the Marble Waste Polluted Systems (MWPS) have a significant role in its remediation. Increasing proline accumulation and decreasing chlorophyll contents with an increase in pollution in the studied plants show resilience of the ecosystem in response to the external lithospheric toxicities. It is recommended that the recognized plant species could be planted abundantly to remediate the MWPS around the marble processing and other such industries and their catchments.

Differences in the effects of single and mixed species of AMF on the growth and oxidative stress defense in Lolium perenne exposed to hydrocarbons

Arbuscular mycorrhizal fungi (AMF) are ubiquitous mutualistic plant symbionts that promote plant growth and protect them from abiotic stresses. Studies on AMF-assisted phytoremediation have shown that AMF can increase plant tolerance to the presence of hydrocarbon contaminants by improving plant nutrition status and mitigating oxidative stress. This work aimed to evaluate the impact of single and mixed-species AMF inocula (Funneliformis caledonium, Diversispora varaderana, Claroideoglomus walkeri), obtained from a contaminated environment, on the growth, oxidative stress (DNA oxidation and lipid peroxidation), and activity of antioxidative enzymes (superoxide dismutase, catalase, peroxidase) in Lolium perenne growing on a substrate contaminated with 0/0-30/120 mg phenol/polynuclear aromatic hydrocarbons (PAHs) kg^-1. The assessment of AMF tolerance to the presence of contaminants was based on mycorrhizal root colonization, spore production, the level of oxidative stress, and antioxidative activity in AMF spores. In contrast to the mixed-species AMF inoculum, single AMF species significantly enhanced the growth of host plants cultured on the contaminated substrate. The effect of inoculation on the level of oxidative stress and the activity of antioxidative enzymes in plant tissues differed between the AMF species. Changes in the level of oxidative stress and the activity of antioxidative enzymes in AMF spores in response to contamination also depended on AMF species. Although the concentration of phenol and PAHs had a negative effect on the production of AMF spores, low (5/20 mg phenol/PAHs kg^-1) and medium (15/60 mg phenol/PAHs kg^-1) substrate contamination stimulated the mycorrhizal colonization of roots. Among the studied AMF species, F. caledonium was the most tolerant to phenol and PAHs and showed the highest potential in plant growth promotion. The results presented in this study might contribute to the development of functionally customized AMF-assisted phytoremediation strategies with indigenous AMF, more effective than commercial AMF inocula, as a result of their selection by the presence of contaminants.

Study of Boraginaceae plants for phytoremediation of oil-contaminated soil

Long-term field observations of the natural vegetation cover in industrial and adjacent areas has revealed that the Boraginaceae was one of the main plant family representatives of which were noted in oil-contaminated area. In this study against the background of the previously well characterized plant families Poaceae and Fabaceae, the phytoremediation potential of Boraginaceae plants was investigated under the field conditions and described. Among the members of this family, Lithospermum arvense, Nonea pulla, Asperugo procumbens, Lappula myosotis, and Echium vulgare were the most common in oil-contaminated areas. N. pulla was the most tolerant to hydrocarbons and, along with L. arvense and E. vulgare, actively stimulated the soil microorganisms, including hydrocarbon-oxidizing ones, in their rhizosphere. A comparative assay confirmed that the plants of the Fabaceae family as a whole more efficiently enrich the soil both with available nitrogen and with pollutant degradation genes. Nevertheless, the comparatively high ammonium nitrogen content in the rhizosphere of N. pulla and E. vulgare allows these species to be singled out to explain their high rhizosphere effect, and to suggest their remediation potential for oil-contaminated soil.Novelty statement Against the background of the previously well characterized plant families Poaceae and Fabaceae, the remediation potential of Boraginaceae plants was described for the first time. Overall, this study contributes to understanding the differences in remediation potential of plants at the family level and suggests the monitoring pollutant degradation genes as an informative tool to the search for plant promising for use in the cleanup of oil-contaminated soil.

An insight into the current oil spills and on-site bioremediation approaches to contaminated sites in Nigeria

Land oil spills in Nigeria have a long history of contaminating the soil, groundwater, vegetation, and streams with spill extension being the primary of numerous ordeals. These have left the host communities of oil fields and pipelines in crucial need of soil rehabilitation. Thus, this review provides insights into the current state of land oil spills and the effectiveness of on-site remediation approaches across communities. A total of 44 incidents of land oil spills of ≥ 500 bbl, amounting to 53,631 bbl between 2011 and 2019, was recorded by the Shell Petroleum Development Company, which primarily attributed to 83% of the total sabotage. Over 73% of the 53,631 bbl spills were unrecovered from the spill areas, which had deleterious impacts on farmlands, fishponds, rivers, and residential areas. Remediation by enhanced natural attenuation (RENA) is a feasible technique for restoring petroleum hydrocarbon-contaminated sites, but it might be ineffective when limited to tiling, windrows, and fertilizer applications due to the presence of non-biodegradable residues and contaminants beyond the aeration depth. However, bioremediation techniques ranging from non-supplemented in-situ and fertilizer supplemented in-situ to mixed in-situ and ex-situ bio-cells supplemented RENA are feasible approaches for spill sites. However, challenging limitations with regard to RENA application failures in the region include delayed responses to spill emergency, large amounts of un-recovered spilled oil, and un-implemented legislative guidelines for spill cleanup. Nevertheless, the temperature, moisture, nutrient, oxygen, and pH of the soil are essential parameters to be considered when implementing a landfarming remediation approach.

Monitoring oil contamination in vegetated areas with optical remote sensing: A comprehensive review

The monitoring of soil contamination deriving from oil and gas industry remains difficult in vegetated areas. Over the last decade, optical remote sensing has proved helpful for this purpose. By tracking alterations in vegetation biochemistry through its optical properties, multi- and hyperspectral remote sensing allow detecting and quantifying crude oil and petroleum products leaked following accidental leakages or bad cessation practices. Recent advances in this field have led to the development of various methods that can be applied either in the field using portable spectroradiometers or at large scale on airborne and satellite images. Experiments carried out under controlled conditions have largely contributed to identifying the most important factors influencing the detection of oil (plant species, mixture composition, etc.). In a perspective of operational use, an important effort is still required to make optical remote sensing a reliable tool for oil and gas companies. The current methods used on imagery should extend their scope to a wide range of contexts and their application to upcoming satellite-embedded hyperspectral sensors should be considered in future studies.

Unburnable and Unleakable Carbon in Western Amazon: Using VIIRS Nightfire Data to Map Gas Flaring and Policy Compliance in the Yasuní Biosphere Reserve

In the Amazon Rainforest, a unique post-carbon plan to mitigate global warming and to protect the exceptional bio-cultural diversity was experimented in 2007–2013 by the Ecuadorian government. To preserve the rainforest ecosystems within the Yasuní-ITT oil block, the release of 410 million metric tons of CO2 would have been avoided. The neologism “yasunization” emerged as an Amazonian narrative on “unburnable carbon” to be replicated worldwide. Considering the unburnable carbon, petroleum-associated gas flaring represents the unleakable part. Flaring is an irrational practice that consists of burning waste gases, representing not only a leak of energy but also a pollution source. The general aim of the paper is to monitor gas flaring as a tool, revealing, at the same time, the implementation of environmental technologies in the oil sector and the compliance of sustainable policies in the Amazon region and the Yasuní Biosphere Reserve. Specific objectives are: (i) identifying and estimating gas flaring over seven years (2012–2018); (ii) mapping new flaring sites; iii) estimating potentially affected areas among ecosystems and local communities. We processed National Oceanic and Atmospheric Administration (NOAA) Nightfire annual dataset, based on the elaboration of imagery from the Visible Infrared Imaging Radiometer Suite (VIIRS) and developed a GIS-based novel simple method to identify new flaring sites from daily detections. We found that 23.5% of gas flaring sites and 18.4% of volumes of all oil industries operating in Ecuador are located within the Yasuní Biosphere Reserve (YBR). Moreover, we detected 34 additional flaring sites not included in the NOAA dataset—12 in the YBR and one in Tiputini field, a key area for biological and cultural diversity conservation. We also found that at least 10 indigenous communities, 18 populated centers and 10 schools are located in the potentially affected area. Gas flaring can be used as a policy indicator to monitor the implementation of sustainable development practices in complex territories.

Estimating persistent oil contamination in tropical region using vegetation indices and random forest regression

The persistence of soil contamination after cessation of oil activities remains a major environmental issue in tropical regions. The assessment of the contamination is particularly difficult on vegetated sites, but promising advances in reflectance spectroscopy have recently emerged for this purpose. This study aimed to exploit vegetation reflectance for estimating low concentrations of Total Petroleum Hydrocarbons (TPH) in soils. A greenhouse experiment was carried out for 42 days on Cenchrus alopecuroides (L.) under realistic tropical conditions. The species was grown on oil-contaminated mud pit soils from industrial sites, with various concentrations of TPH. After 42 days, a significant decrease in plant growth and leaf chlorophyll and carotenoid contents was observed for plants exposed to 5-19 g kg^-1 TPH in comparison to the controls (p < 0.05). Conversely, pigment contents were higher for plants exposed to 1 g kg^-1 TPH (hormesis phenomenon). These modifications proportionally affected the reflectance of C. alopecuroides at leaf and plant scales, especially in the visible region around 550 and 700 nm. 33 vegetation indices were used for linking the biochemical and spectral responses of the species to oil using elastic net regressions. The established models indicated that chlorophylls a and b and β-carotene were the main pigments involved in the modifications of reflectance (R² > 0.7). The same indices also succeeded in estimating the concentrations of TPH using random forest regression, at leaf and plant scales (RMSE = 1.46 and 1.63 g kg^-1 and RPD = 5.09 and 4.44, respectively). Four out of the 33 indices contributed the most to the models (>75%). This study opens up encouraging perspectives for monitoring the cessation of oil activities in tropical regions. Further researches will focus on the application of our approach at larger scale, on airborne and satellite imagery.

Effectiveness of the Zea mays-Streptomyces association for the phytoremediation of petroleum hydrocarbons impacted soils

Restoring polluted sites by petroleum hydrocarbons is a challenge because of their complexity and persistence in the environment. The main objective of the present study was to investigate the performance of plant-actinobacteria system for the remediation of crude petroleum and pure-polycyclic aromatic hydrocarbons (PAHs) contaminated soils. The endophytic strain Streptomyces sp. Hlh1 was tested for its ability to degrade model PAHs (phenanthrene, pyrene and anthracene) in liquid minimal medium. Streptomyces sp. Hlh1 demonstrated the ability to grow on PAHs as sole carbon and energy source, reaching hydrocarbons removal of 63%, 93% and 83% for phenanthrene, pyrene and anthracene, respectively. Maize plant was chosen to study the impact of Streptomyces sp. Hlh1 inoculation on the dissipation of contaminants and plant growth. Thus, maize seedlings grown in soils contaminated with crude petroleum and pure-PAHs were inoculated with Streptomyces sp. Hlh1. Results showed that the endophyte inoculation increased contaminants removal. Maximum hydrocarbons removal (70%) was achieved in inoculated and planted soil contaminated with crude oil, while 61%, 59%, and 46% of hydrocarbons dissipation were registered for phenanthrene, pyrene and anthracene, respectively. These degradations rates were significantly higher compared to non-inoculated systems in all the treatments evaluated. Further, it was revealed that hydrocarbons (C₈-C₃₀) were efficiently degraded in plant-Streptomyces Hlh1 system. Moreover, the inoculation with the actinobacteria resulted significant plant development and enhanced photosynthetic pigments compared to plants grown in the other experimental conditions. The present study provide evidence that the inoculation of maize plants with Streptomyces sp. Hlh1 play a remarkable role in the removal of petroleum hydrocarbons, enhancing plant development in contaminated soils.

Toward Quantifying Oil Contamination in Vegetated Areas Using Very High Spatial and Spectral Resolution Imagery

Recent remote sensing studies have suggested exploiting vegetation optical properties for assessing oil contamination, especially total petroleum hydrocarbons (TPH) in vegetated areas. Methods based on the tracking of alterations in leaf biochemistry have been proposed for detecting and quantifying TPH under controlled and field conditions. In this study, we expand their use to airborne imagery, in order to monitor oil contamination at a larger scale. Airborne hyperspectral images with very high spatial and spectral resolutions were acquired over an industrial site with oil-contamination (mud pits) and control sites both colonized by Rubus fruticosus L. The method of oil detection exploiting 14 vegetation indices succeeded in classifying the sites in the case of high TPH contamination (overall accuracy ≥ 91.8%). Two methods, based on either the PROSAIL (PROSPECT + SAIL) radiative transfer model or elastic net multiple regression, were also developed for quantifying TPH. Both methods were tested on reflectance measurements in the field, at leaf and canopy scales, and on the image, and achieved accurate predictions of TPH concentrations (RMSE ≤ 3.28 g/kg−1 and RPD ≥ 1.90). The methods were validated on additional sites and open up promising perspectives of operational application for oil and gas companies, with the emergence of new hyperspectral satellite sensors.

Application of PROSPECT for estimating total petroleum hydrocarbons in contaminated soils from leaf optical properties

Recent advances in hyperspectral spectroscopy suggest making use of leaf optical properties for monitoring soil contamination in oil production regions by detecting pigment alterations induced by Total Petroleum Hydrocarbons (TPH). However, this provides no quantitative information about the level of contamination. To achieve this, we propose an approach based on the inversion of the PROSPECT model. 1620 leaves from five species were collected on a site contaminated by 16 to 77 g.kg^-1 of TPH over a 14-month period. Their spectral signature was measured and used in PROSPECT model inversions to retrieve leaf biochemistry. The model performed well for simulating the spectral signatures (RMSE < 2%) and for estimating leaf pigment contents (RMSE ≤ 2.95 μg.cm^-2 for chlorophylls). Four out of the five species exhibited alterations in pigment contents when exposed to TPH. A strong correlation was established between leaf chlorophyll content and soil TPH concentrations (R² ≥ 0.74) for three of them, allowing accurate predictions of TPH (RMSE =3.20 g.kg^-1 and RPD = 5.17). The accuracy of predictions varied by season and improved after the growing period. This study demonstrates the capacity of PROSPECT to estimate oil contamination and opens up promising perspectives for larger-scale applications.

Spectroscopic determination of leaf chlorophyll content and color for genetic selection on Sassafras tzumu

BACKGROUND

Reflectance spectroscopy, like IR, VIS-NIR, combined with chemometric, has been widely used in plant leaf chemical analysis. But less studies have been made on the application of NIR reflectance spectroscopy to plant leaf color and pigments analysis and the possibility of using it for genetic breeding selection. Here, we examine the ability of NIR reflectance spectroscopy to determine the plant leaf color and chlorophyll content in Sassafras tzumu leaves and use the prediction results for genetic selection. Fresh and living tree leaves were used for NIR spectra collection, leaf color parameters (a*, b* and L*) and chlorophyll content were measured with standard analytical methods, partial least squares regression (PLSR) were used for model construction, the coefficient of determination (R²) [cross-validation ( R CV 2 ) and validation ( R V 2 )] and root mean square error (RMSE) [cross-validation (RMSE_CV) and validation (RMSE_V)] were used for model performance evaluation, significant Multivariate Correlation algorithm was applied for model improvement, to find out the most important region related to the leaf color parameters and chlorophyll model, which have been simulated 100 times for accuracy estimation.

RESULTS

Leaf color parameters (a*, b* and L*) and chlorophyll content were well predicted by NIR reflectance spectroscopy on fresh leaves in vivo. The mean R CV 2 and RMSE_CV of a*, b*, L* and chlorophyll content were (0.82, 4.43), (0.63, 3.72), (0.61, 2.35) and (0.86, 0.13%) respectively. Three most important NIR regions, including 1087, 1215 and 2219 nm, which were highly related to a*, b*, L* and chlorophyll content were found. NIR reflectance spectra technology can be successfully used for genetic breeding program. High heritability of a*, b*, L* and chlorophyll content (h ² = 0.77, 0.89, 0.78, 0.81 respectively) were estimated. Several families with bright red color or bright yellow color were selected.

CONCLUSIONS

NIR spectroscopy is promising for the rapid prediction of leaf color and chlorophyll content of living fresh leaves. It has the ability to simultaneously measure multiple plant leaf traits, potentially allowing for quick and economic prediction in situ.

Radial and Bilateral Fluctuating Asymmetry of Iris pumila Flowers as Indicators of Environmental Stress

In this study we compared the biomonitoring potential of various types of flower asymmetry indices in Iris pumila (Dwarf Bearded Iris). We chose 197 naturally growing clones from the arid steppe habitat in the largest sandy area in Europe (Deliblato Sands Nature Reserve), and we transplanted two replicates of each clone to a polluted highway site with a heavy traffic flow. After a period of acclimatization, lower levels of photosynthetic pigment concentrations and higher stomatal density and specific leaf area in transplants verified that the chosen highway site was indeed more stressful and therefore suitable for estimation of the flower asymmetry biomonitoring potential. We analyzed radially and bilaterally symmetrical flower structures (radial fluctuating asymmetry (RA) and bilateral fluctuating asymmetry (FA)) on three perianth parts—falls, standards, and styles—and calculated various asymmetry indices based on linear and geometric morphometrics. Despite utilizing a heavily polluted environment and fairly large sample sizes, only one asymmetry index was significantly higher on the polluted site with demonstrated stressful effects on utilized plants, indicating that flower asymmetry was not an efficient method for biomonitoring in the case of I. pumila RA and FA indices.

Detection and discrimination of various oil-contaminated soils using vegetation reflectance

The use of hyperspectral spectroscopy for oil detection recently sparked a growing interest for risk assessment over vegetated areas. In a perspective of image applications, we conducted a greenhouse experiment on a brownfield-established species, Rubus fruticosus L. (bramble), to evaluate the potential of vegetation reflectance to detect and discriminate among various oil-contaminated soils. The species was grown for 32 days on four different soils with mixtures of petroleum hydrocarbons and heavy metals. Additional plants were grown on either uncontaminated control or water-deficient soils for comparison. Repeated reflectance measurements indicated modified spectral signatures under both oil and water-deficit exposure, from leaf to multi-plant scales. The amplitude of the response varied with mixture composition, exposure time, acquisition scale and spectrum region. Reflectance changes were linked to alterations in chlorophyll, carotenoid and water contents using vegetation indices. These indices were used to catch spectral similarities among acquisition scales and to discriminate among treatments using Kendall's coefficient of concordance (W) and regularized logistic regression. Of the 33 vegetation indices tested, 14 were concordant from leaf to multi-plant scales (W > 0.75, p < 0.05) and strongly related to leaf biochemistry (R² > 0.7). The 14 indices allowed discriminating between each mixture and the control treatment with no or minor confusions (≤5%) at all acquisition scales, depending on exposure time. Some of the mixtures remained difficult to discriminate among them and from the water-deficit treatment. The approach was tested at the canopy scale under natural conditions and performed well for identifying bramble exposed to either one of the experimentally-tested mixtures (90% accuracy) or to uncontaminated soil (83% accuracy). This study provided better understanding of vegetation spectral response to oil mixtures and opens up promising perspectives for future applications.

Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods

An unprecedented spectroscopic data stream will soon become available with forthcoming Earth-observing satellite missions equipped with imaging spectroradiometers. This data stream will open up a vast array of opportunities to quantify a diversity of biochemical and structural vegetation properties. The processing requirements for such large data streams require reliable retrieval techniques enabling the spatiotemporally explicit quantification of biophysical variables. With the aim of preparing for this new era of Earth observation, this review summarizes the state-of-the-art retrieval methods that have been applied in experimental imaging spectroscopy studies inferring all kinds of vegetation biophysical variables. Identified retrieval methods are categorized into: (1) parametric regression, including vegetation indices, shape indices and spectral transformations; (2) nonparametric regression, including linear and nonlinear machine learning regression algorithms; (3) physically based, including inversion of radiative transfer models (RTMs) using numerical optimization and look-up table approaches; and (4) hybrid regression methods, which combine RTM simulations with machine learning regression methods. For each of these categories, an overview of widely applied methods with application to mapping vegetation properties is given. In view of processing imaging spectroscopy data, a critical aspect involves the challenge of dealing with spectral multicollinearity. The ability to provide robust estimates, retrieval uncertainties and acceptable retrieval processing speed are other important aspects in view of operational processing. Recommendations towards new-generation spectroscopy-based processing chains for operational production of biophysical variables are given.

Dynamics of natural revegetation of hydrocarbon-contaminated soil and remediation potential of indigenous plant species in the steppe zone of the southern Volga Uplands

The result of monitoring of natural vegetation growing on oil-contaminated (2.0-75.6 g/kg) and uncontaminated (0.04-2.0 g/kg) soils of a petroleum refinery for a period of 13 years is presented. Floristic studies showed that the families Poaceae, Asteraceae, Fabaceae, and eventually Brassicaceae were predominant in the vegetation cover of both types of soils. Over time, the projective vegetation cover of the contaminated sites increased from 46 to 90%; the species diversity increased twofold: in the ecological-cenotic structure of the flora, the number of ruderal plant species decreased; and the number of steppe, i.e., zonal, plant species increased. Using 62 dominant plant species, we conducted a field study of plant characteristics such as resistance to oil pollution, the ability to enrich the rhizosphere soil with microorganisms and bioavailable mineral nitrogen, and reduction of the concentration of petroleum hydrocarbons. The results enable us to characterize the phytoremediation potential (PRP) of the native plants and identify species that, probably, played a key role in the natural restoration of oil-contaminated soils. Statistical analysis showed correlations between the PRP constituents, and the leading role of rhizosphere microorganisms in the rhizodegradation of petroleum hydrocarbons was proven. A conditional value of PRP was proposed which allowed the investigated plants to be ranked in 11 classes. The study of a large sample of plant species showed that some plants held promise for the use in reclamation of soils in arid steppe zone, and that other species can be used for the rehabilitation of saline soils and semideserts.