Importance of soil physical characteristics for petroleum hydrocarbons phytoremediation: A review

Petroleum and petrochemical hydrocarbons for some places are serious sources of environmental pollutants. To remediate these contaminants, phytoremediation, a relatively low cost and an environmental friendly technique is recommended more widely, now more than ever. Successful and effective applying of hydrocarbons phytoremediation depends mainly on the soil and plant types and conditions and microbial activities and the interactions between these three factors. Although for the last several decades, various plant and organism’s species for the phytoremediation processes have been extensively studied, evaluating and characterizing soil properties, as an important objective for sustainable remediation and land use management, which had negligible considerations. An ideal soil for phytoremediation should have proper physical, chemical and biological characteristics to let the plant grow well and produce biomass as high as possible. It also should provide favorable conditions for microbial activities to perform efficient remediation. Soil physical characteristics such as texture, structure, water status and aeration are important factors affecting the microbial activities and consequently the degree of remediation potential. A better understanding of soil physical properties in conjunction with proper soil-plant-microbe management could be exploited to enhance the remediation of hydrocarbon contaminated soils and thus sustainable healthy environment.Keywords: Phytoremediation, petroleum, hydrocarbo

Contrasting transport and fate of hydrophilic and hydrophobic bacteria in wettable and water-repellent porous media: Straining or attachment?

Acknowledgements This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Grant agreement no. 101026287. We acknowledge University of Aberdeen, UK for supporting this project.Peer reviewedPublisher PD

Phosphorous and Temperature Effects on Nodal and Seminal Root Morphology of Two Maize Genotypes

Phenotypic plasticity (gene by environment interaction) of root systems is one possible mechanism of plant adaptation to nonuniform distribution of soil phosphorous. The purpose of this study was to examine the effects of phosphorous and temperature on the root morphology and P uptake of two maize (Zea mays L.) genotypes that have differences in early growth and phenotypic plasticity. Three studies were conducted using CM37, a genotype with good early growth and high phenotypic plasticity and W153R, a genotype with poor seedling growth and low phenotypic plasticity. The effects of P (5, 45, and 300 mg kg-1) on root morphology and P uptake were examined for the first six growth stages. Subsequent studies examined the effect of soil temperature (15, 20, and 25 °C) and placement of P, on nodal and seminal root morphology. The mechanistic Barber-Cushman nutrient uptake model was used to predict P uptake based on root morphology and P supply characteristics and compared to the observed P uptake. A Maddock (sandy, mixed Udorthentic Haploborolls) soil was used. Phosphorous as NH4H2PO4 was used as the P source. Phosphorous increased root growth and development earlier and to a greater degree for CM37. The formation of lateral roots on CM37 was increased by both temperature and P to a greater extent than on W153R. Fertilizing the nodal root compartment increased root growth and development more than localizing P in the seminal compartment. Phosphorous increased nodal root length more at the highest soil temperature in both genotypes. Phosphorous uptake was more accurately predicted for low soil P levels, nodal roots, and W153R. The ability of root system to respond to differences in soil environments increases the ability of the roots to acquire localized sources of phosphorous. Selecting or breeding genotypes with higher phenotypic plasticity could increase root system response by increasing the formation of roots in a favorable environment thus increasing root surface area and P uptake. This could result in a crop with higher rates of growth and development under less than ideal conditions and thus a higher yield

Effects of Applied Biochar and Municipal Solid Waste Compost on Saline Soil Properties and Sorghum Plant Attributes

The hypothesis is that incorporating saline soil with biochar or compost reduces the deteriorating effects of salinity. The pot experiment was irrigated with waters with different salinities (4.5 and 9 dS m-1) and a silty clay soil in pots was thoroughly mixed with 1.5% w/w of biochar, 1.5% w/w of municipal solid waste compost and the mixtures of 0.5 × 0.5% w/w of the two mentioned substances. Irrigation was provided to realize 0.15 leaching fractions for equilibrating the soil salinity. Soil and plants were analysed after two months (T1) and three months (T2) after sowing. Saline irrigation water decreased SAR (~45%) and SOC (~5.5%), respectively for T2 compared with T1. The biochar treatment reduced the amount of ECe in T1 and T2. Both irrigating with saline water and amendments greatly changed the amount of leaf water potential (LWP), chlorophyll and proline leaf. LWP and proline were increased by 17 and 76%, respectively, with increasing irrigation water salinity, while the leaf chlorophyll content was significantly decreased (~52%). The overall finding was that incorporating the saline soil of the region with biochar showed more potential to enhance soil properties and sorghum production

Soil water repellency changes with depth and relationship to physical properties within wettable and repellent soil profiles

This study explored the effect of soil water repellency (SWR) on soil hydrophysical properties with depth. Soils were sampled from two distinctly wettable and water repellent soil profiles at depth increments from 0-60 cm. The soils were selected because they appeared to either wet readily (wettable) or remain dry (water repellent) under field conditions. Basic soil properties (MWD, SOM, θv) were compared to hydrophysical properties (Ks, Sw, Se, Sww, Swh, WDPT, RIc, RIm and WRCT) that characterise or are affected by water repellency. Our results showed both soil and depth affected basic and hydrophysical properties of the soils (p <0.001). Soil organic matter (SOM) was the major property responsible for water repellency at the selected depths (0-60). Water repellency changes affected moisture distribution and resulted in the upper layer (0-40 cm) of the repellent soil to be considerably drier compared to the wettable soil. The water repellent soil also had greater MWDdry and Ks over the entire 0-60 cm depth compared to the wettable soil. Various measures of sorptivity, Sw, Se, Sww, Swh, were greater through the wettable than water repellent soil profile, which was also reflected in field and dry WDPT measurements. However, the wettable soil had subcritical water repellency, so the range of data was used to compare indices of water repellency. WRCT and RIm had less variation compared to WDPT and RIc. Estimating water repellency using WRCT and RIm indicated that these indices can detect the degree of SWR and are able to better classify SWR degree of the subcritical-repellent soil from the wettable soil

Estimating wet soil aggregate stability from easily available properties in a highly mountainous watershed

A comparison study was carried out with the purpose of verifying when the adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN), generalized linear model (GLM), and multiple linear regression (MLR) models are appropriate for prediction of soil wet aggregate stability (as quantified by the mean weight diameter, MWD) in a highly mountainous watershed (Bazoft watershed, southwestern Iran). Three different sets of easily available properties were used as inputs. The first set (denoted as SP) consisted of soil properties including clay content, calcium carbonate equivalent, and soil organic matter content. The second set (denoted as TVA) included topographic attributes (slope and aspect) and the normalized difference vegetation index (NDVI). The third set (denoted as STV) was a combination of soil properties, slope, and NDVI. The ANN and ANFIS models predicted MWD more accurately than the GLM and MLR models. Estimation of MWD using TVA data set resulted in the lowest model efficiency values. The observed model efficiency values for the developed MLR, GLM, ANN, and ANFIS models using the SP data set were 60.76, 62.98, 77.68 and 77.15, respectively. Adding slope and NDVI to soil data (i.e. STV data set) improved the predictions of all four methods. The obtained correlation coefficient values between the predicted and measured MWD for the developed MLR, GLM, ANN, and ANFIS models using STV data set were 0.24, 0.35, 0.84 and 0.73, respectively. In conclusion, the ANN and ANFIS models showed greater potential in predicting soil aggregate stability from soil and site characteristics, whereas linear regression methods did not perform well.ISSN:0341-8162ISSN:1872-688