Engineering soil organic matter quality: Biodiesel Co-Product (BCP) stimulates exudation of nitrogenous microbial biopolymers

Biodiesel Co-Product (BCP) is a complex organic material formed during the transesterification of lipids. We investigated the effect of BCP on the extracellular microbial matrix or ‘extracellular polymeric substance’ (EPS) in soil which is suspected to be a highly influential fraction of soil organic matter (SOM). It was hypothesised that more N would be transferred to EPS in soil given BCP compared to soil given glycerol. An arable soil was amended with BCP produced from either 1) waste vegetable oils or 2) pure oilseed rape oil, and compared with soil amended with 99% pure glycerol; all were provided with 15N labelled KNO3. We compared transfer of microbially assimilated 15N into the extracellular amino acid pool, and measured concomitant production of exopolysaccharide. Following incubation, the 15N enrichment of total hydrolysable amino acids (THAAs) indicated that intracellular anabolic products had incorporated the labelled N primarily as glutamine and glutamate. A greater proportion of the amino acids in EPS were found to contain 15N than those in the THAA pool, indicating that the increase in EPS was comprised of bioproducts synthesised de novo. Moreover, BCP had increased the EPS production efficiency of the soil microbial community (μg EPS per unit ATP) up to approximately double that of glycerol, and caused transfer of 21% more 15N from soil solution into EPS-amino acids. Given the suspected value of EPS in agricultural soils, the use of BCP to stimulate exudation is an interesting tool to consider in the theme of delivering sustainable intensification

Proportional contributions to organic chemical mixture effects in groundwater and surface water

Semi-quantitative GC-MS and LC-MS measurements of organic chemicals in groundwater and surface waters were used to assess the overall magnitude and contribution of the most important substances to calculated mixture hazard. Here we use GC-MS and LC-MS measurements taken from two separate national monitoring programs for groundwater and surface water in England, in combination with chronic species sensitivity distribution (SSD) HC50 values published by Posthuma et al. (2019, Environ. Toxicol. Chem, 38, 905–917) to calculate individual substance hazard quotients and mixture effects using a concentration addition approach. The mixture analysis indicated that, as anticipated, there was an increased hazard from the presence of a cocktail of substances at sites compared to the hazard for any single chemical. The magnitude of the difference between the hazard attributed to the most important chemical and the overall mixture effect, however, was not large. Thus, the most toxic chemical contributed ≥ 20% of the calculated mixture effect in >99% of all measured groundwater and surface water samples. On the basis of this analysis, a 5 fold assessment factor placed on the risk identified for any single chemical would offer a high degree of in cases where implementation of a full mixture analysis was not possible. This finding is consistent with previous work that has assessed chemical mixture effects within field monitoring programs and as such provides essential underpinning for future policy and management decisions on how to effectively and proportionately manage mixture risks

TopCap: a tool to quantify soil surface topology and subsurface structure

The surface of a material such as soil, as characterised by its topology and roughness, typically has a profound effect on its functional behaviour. Whilst non-destructive imaging techniques such as X-ray Computed Tomography (CT) have been extensively employed in recent years to characterise the internal architecture of soil, less attention has been paid to the morphology of the soil surface, possibly as other techniques such as scanning electron microscopy (SEM) and atomic force microscopy (AFM) are viewed as more appropriate. However, X-ray CT exploration of the surface of a soil also permits analysis immediately below its surface and beyond into the sample, contingent on its thickness. This provides important information such as how a connected structure might permit solute infiltration or gaseous diffusion through the surface and beyond into the subsurface matrix. A previous limitation to this approach had been the inability to segment and quantify the actual 3-D structural complexity at the surface, rather than a predefined geometrically simplistic volume immediately below it. To overcome this we formulated TopCap, a novel algorithm that operates with ImageJ as a plugin, which automatically captures the actual 3D surface morphology, segments the pore structure within the acquired 3D volume, and provides a series of incisive morphological measurements of the associated porous architecture. TopCap provides rapid, automated analysis of the immediate surface of materials and beyond, and whilst developed in the context of soil, is applicable to any 3D image volume

Soil seal development under simulated rainfall: structural, physical and hydrological dynamics

This study delivers new insights into rainfall-induced seal formation through a novel approach in the use of X-ray Computed Tomography (CT). Up to now seal and crust thickness have been directly quantified mainly through visual examination of sealed/crusted surfaces, and there has been no quantitative method to estimate this important property. X-ray CT images were quantitatively analysed to derive formal measures of seal and crust thickness. A factorial experiment was established in the laboratory using open-topped microcosms packed with soil. The factors investigated were soil type (three soils: silty clay loam - ZCL, sandy silt loam - SZL, sandy loam - SL) and rainfall duration (2-14 minutes). Surface seal formation was induced by applying artificial rainfall events, characterised by variable duration, but constant kinetic energy, intensity, and raindrop size distribution. Soil porosities derived from CT scans were used to quantify the thickness of the rainfall-induced surface seals and reveal temporal seal micro-morphological variations with increasing rainfall duration. In addition, the water repellency and infiltration dynamics of the developing seals were investigated by measuring water drop penetration time (WDPT) and unsaturated hydraulic conductivity (Kun). The range of seal thicknesses detected varied from 0.6 - 5.4 mm. Soil textural characteristics and OM content played a central role in the development of rainfall-induced seals, with coarser soil particles and lower OM content resulting in thicker seals. Two different trends in soil porosity vs. depth were identified: i) for SL soil porosity was lowest at the immediate soil surface, it then increased constantly with depth till the median porosity of undisturbed soil was equalled; ii) for ZCL and SL the highest reduction in porosity, as compared to the median porosity of undisturbed soil, was observed in a well-defined zone of maximum porosity reduction c. 0.24 - 0.48 mm below the soil surface. This contrasting behaviour was related to different dynamics and processes of seal formation which depended on the soil properties. The impact of rainfall-induced surface sealing on the hydrological behaviour of soil (as represented by WDTP and Kun) was rapid and substantial: an average 60% reduction in Kun occurred for all soils between 2 and 9 minutes rainfall, and water repellent surfaces were identified for SZL and ZCL. This highlights that the condition of the immediate surface of agricultural soils involving rainfall-induced structural seals has a strong impact in the overall ability of soil to function as water reservoir

Completely dielectric E-field sensor based on a Lithium Niobate MZI

We report an IO electric field sensor based on a particular cofiguration of Mach-Zehnder interferometer. The device, fabricated on a Lithium Niobate substrate, is totally dielectric, without any presence of metallic electrodes. The Mach-Zehnder was achieved through soft proton exchange and poling technologies. A modulation depth of 92% and an half wave voltage of 107.8 V were measured using a triangular voltage signal

Sensore di campo elettrico completamente dielettrico in ottica integrata.

We report an electric field sensor based on a particular cofiguration of Mach-Zehnder interferometer (MZI). The device, fabricated on a Lithium Niobate substrate, is totally dielectric, without any presence of metallic electrodes. A modulation depth of 92% and an half wave voltage of 107.8 V were measured using a triangular voltage signal

Worst-case ranking of organic chemicals detected in groundwaters and surface waters in England

The Environment Agency has been using Gas Chromatography–Mass Spectrometry (GC–MS) and Accurate-mass Quadrupole Time-of-Flight (Q-TOF) / Liquid Chromatography-Mass Spectrometry (LC-MS) target screen analysis to semi-quantitatively measure organic substances in groundwater and surface water since 2009 for GC–MS and 2014 for LC-MS. Here we use this data to generate a worst-case “risk” ranking of the detected substances. Three sets of hazard values relating to effects on aquatic organisms, namely Water Framework Directive EQSs, NORMAN Network PNECs (hereafter NORMAN PNEC) and chronic Species Sensitivity Distribution (SSD) HC50s from Posthuma et al., (2019) were used for the assessment. These hazard values were compared to the highest measured concentration for each chemical to generate a worst-case hazard quotient (HQ). Calculated HQs for each metric were ranked, averaged and multiplied by rank for detection frequency to generate an overall ordering based on HQ and occurrence. This worst-case approach was then used to generate ranking lists for GC–MS and LC-MS detected substances in groundwater and surface water. Pesticides in the top 30 overall ranked list included more legacy pesticides in groundwater and more current use actives in surface water. Specific uses were linked to some high rankings (e.g. rotenone for invasive species control). A number of industrial and plastics associated chemicals were ranked highly in the groundwater dataset, while more personal care products and pharmaceuticals were highly ranked in surface waters. Perfluoroalkyl and Polyfluoroalkyl Substances (PFAS) compounds were commonly highly ranked in both environmental compartments. The approach confirmed high rankings for some substance (e.g. selected pesticides) from previous prioritization exercises, but also identified novel substance for consideration (e.g. some PFAS compounds and pharmaceuticals). Overall our approach provided a simple approach using readily accessible data to identify substances for further and more detailed assessment