The effect of oil sludge contamination on morphological and physiological characteristics of some tree species

Biomass partitioning of plants under soil pollution stress

Polluted sites are ubiquitous worldwide but how plant partition their biomass between different organs in this context is unclear. Here, we identified three possible drivers of biomass partitioning in our controlled study along pollution gradients: plant size reduction (pollution effect) combined with allometric scaling between organs; early deficit in root surfaces (pollution effect) inducing a decreased water uptake; increased biomass allocation to roots to compensate for lower soil resource acquisition consistent with the optimal partitioning theory (plant response). A complementary meta-analysis showed variation in biomass partitioning across published studies, with grass and woody species having distinct modifications of their root: shoot ratio. However, the modelling of biomass partitioning drivers showed that single harvest experiments performed in previous studies prevent identifying the main drivers at stake. The proposed distinction between pollution effects and plant response will help to improve our knowledge of plant allocation strategies in the context of pollution.

An empirical study with different levels of soil pollution and a meta-analysis provide insight into the drivers of plant biomass partitioning under soil pollution stress.

Beneficial microbiomes for bioremediation of diverse contaminated environments for environmental sustainability: present status and future challenges

Over the past few decades, the rapid development of agriculture and industries has resulted in contamination of the environment by diverse pollutants, including heavy metals, polychlorinated biphenyls, plastics, and various agrochemicals. Their presence in the environment is of great concern due to their toxicity and non-biodegradable nature. Their interaction with each other and coexistence in the environment greatly influence and threaten the ecological environment and human health. Furthermore, the presence of these pollutants affects the soil quality and fertility. Physicochemical techniques are used to remediate such environments, but they are less effective and demand high costs of operation. Bioremediation is an efficient, widespread, cost-effective, and eco-friendly cleanup tool. The use of microorganisms has received significant attention as an efficient biotechnological strategy to decontaminate the environment. Bioremediation through microorganisms appears to be an economically viable and efficient approach because it poses the lowest risk to the environment. This technique utilizes the metabolic potential of microorganisms to clean up contaminated environments. Many microbial genera have been known to be involved in bioremediation, including Alcaligenes, Arthrobacter, Aspergillus, Bacillus, Burkholderia, Mucor, Penicillium, Pseudomonas, Stenotrophomonas, Talaromyces, and Trichoderma. Archaea, including Natrialba and Haloferax, from extreme environments have also been reported as potent bioresources for biological remediation. Thus, utilizing microbes for managing environmental pollution is promising technology, and, in fact, the microbes provide a useful podium that can be used for an enhanced bioremediation model of diverse environmental pollutants.

Long-term dynamics of plant communities after biological remediation of oil-contaminated soils in far north

We studied the long-term dynamics of plant communities after bio and phytoremediation of oil-polluted soils. Nine plots located in European Northeast and treated using various bioremediation methods were monitored from 2002 to 2014. Geobotanical descriptions (relevés) of each plot were performed in 2006 and 2014, and Grime’s theoretical CSR (competition–stress–ruderality) framework was used to assess the vegetation state and dynamics. We observed a clear shift of communities from pioneer (where ruderal species were prevalent) to stable (where competitor species were dominant) states. However, the remediation type did not significantly impact the vegetation recovery rate. After 12 years, all methods led to a 55–90% decrease in the oil content of the soil and a recovery of the vegetation cover. The plant communities contained mainly cereals and sedges which significantly differed from the original tundra communities before the oil spill. The control plot, treated only by mechanical cleaning, had minimum oil degradation rate (50%) and vegetation recovery rates, although, in CSR terms, its vegetation assemblage resembled the background community. Cereals (Agrostis gigantea, Deschampsia cespitosa, Phalaris arundinacea, and Poa pratensis), sedges (Carex canescens, Carex limosa, and Eriophorum vaginatum), and shrubs (Salix) were found to be the most effective species for phytoremediation, exhibiting high community productivity under the harsh northern conditions.

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.