Assessing Effects of Salinity on the Performance of a Low-Cost Wireless Soil Water Sensor.

Low-cost, accurate soil water sensors combined with wireless communication in an internet of things (IoT) framework can be harnessed to enhance the benefits of precision irrigation. However, the accuracy of low-cost sensors (e.g., based on resistivity or capacitance) can be affected by many factors, including salinity, temperature, and soil structure. Recent developments in wireless sensor networks offer new possibilities for field-scale monitoring of soil water content (SWC) at high spatiotemporal scales, but to install many sensors in the network, the cost of the sensors must be low, and the mechanism of operation needs to be robust, simple, and consume low energy for the technology to be practically relevant. This study evaluated the performance of a resistivity-capacitance-based wireless sensor (Sensoterra BV, 1018LE Amsterdam, Netherlands) under different salinity levels, temperature, and soil types in a laboratory. The sensors were evaluated in glass beads, Oso Flaco sand, Columbia loam, and Yolo clay loam soils. A nonlinear relationship was exhibited between the sensor measured resistance (Ω) and volumetric soil water content (θ). The Ω-θ relationship differed by soil type and was affected by soil solution salinity. The sensor was extremely sensitive at higher water contents with high uncertainty, and insensitive at low soil water content accompanied by low uncertainty. The soil solution salinity effects on the Ω-θ relationship were found to be reduced from sand to sandy loam to clay loam. In clay soils, surface electrical conductivity (ECs) of soil particles had a more dominant effect on sensor performance compared to the effect of solution electrical conductivity (ECw). The effect of temperature on sensor performance was minimal, but sensor-to-sensor variability was substantial. The relationship between bulk electrical conductivity (ECb) and volumetric soil water content was also characterized in this study. The results of this study reveal that if the sensor is properly calibrated, this low-cost wireless soil water sensor has the potential of improving soil water monitoring for precision irrigation and other applications at high spatiotemporal scales, due to the ease of integration into IoT frameworks.

Nutrient pollutant loading and source apportionment along a Mediterranean river.

Rivers are increasingly being subjected to increased anthropogenic pollution stresses that undermine their designated uses and negatively affect sensitive coastal regions. The degradation of river water quality is attributed to both point and nonpoint sources of pollution. In this study, we determine the relative contribution of point and nonpoint pollutant loads in the Beirut River basin, a poorly monitored seasonal Mediterranean river. Water quality samples were collected on a weekly basis over 2 consecutive years (2016 and 2017) from four sampling sites that represent a gradient of increasing urbanization. Flow-concentration models were first developed to estimate total phosphorus (TP), total nitrogen (TN), and total suspended solids (TSS) loads reaching the different sub-basins. The performance of the regression models varied by location and by pollutant, with improved performance in the downstream sections (adjusted R2 66% for TP and 59% for TN). Loads were also determined using the Beale's ratio method, which generally underestimated the loads as compared with the regression-based models. The relative contribution of the nonpoint source loads were then quantified using the Open Nonpoint Source Pollution and Erosion Comparison Tool (OpenNSPECT). The results showed that point sources were the main cause of water quality impairment across the entire basin, with load contributions varying between 75% in the headwaters and 98% in the urbanized downstream sections. The adopted modeling approach in this study provides an opportunity to better understand pollutant load dynamics in poorly monitored basins and a mechanism to apportion pollution loads between point and nonpoint sources.

Are there universal soil responses to cover cropping? A systematic review.

Cover cropping is commonly acknowledged to promote soil health in agriculture. However, contradictory findings on the benefits of cover crops for soil health, crop productivity, economic and ecological factors, as well as the influence of inherent soil parameters on such benefits exist in the scientific literature. Here, we critically assessed evidence of cover crop benefits through a systematic review of the published literature. To access relevant papers, we searched the literature for cover crops and soil health indicators using Scopus (1996-2020), ScienceDirect (1996-2020) and Google scholar (1970-1996) with specific keywords and combinations. Only English research papers including experimental plots and control groups were considered. We analyzed 102 unique peer-reviewed papers and 1494 corresponding unique plots encompassing various cover crops, soil textures, climates, management systems and experimental duration (1-3 years, 4-6 years, 7-10 years and over 10 years). Strong evidence suggests that cover crops can enhance soil structure and promote soil health by improving soil physical and chemical properties, including saturated hydraulic conductivity (mean net change of 105.6 %), total organic carbon (10.1 %), and total nitrogen (20.2 %). On the other hand, cover crops exhibit weak effects on properties like bulk density and microporosity with fairly low values of net change. In most cases, cover crops increase the soil carbon content, including microbial biomass carbon (19.5 %) and particulate organic carbon (49.5 %). In this systematic review, we found limited studies on the effect of cover crops on soil health as influenced by soil texture, regional climate, rainfall and duration of the cover crop practices. The paucity of long-term regional systematic research of soil physics, chemistry and biology makes it difficult to forecast future implications of cover crops on soil health indicators.

Coupling time-lapse ground penetrating radar surveys and infiltration experiments to characterize two types of non-uniform flow.

Understanding linkages between heterogeneous soil structures and non-uniform flow is fundamental for interpreting infiltration processes and improving hydrological simulations. Here, we utilized ground-penetrating radar (GPR) as a non-invasive technique to investigate those linkages and to complement current traditional methods that are labor-intensive, invasive, and non-repeatable. We combined time-lapse GPR surveys with different types of infiltration experiments to create three-dimensional (3D) diagrams of the wetting dynamics. We carried out the GPR surveys and validated them with in situ observations, independent measurements and field excavations at two experimental sites. Those sites were selected to represent different mechanisms that generate non-uniform flow: (1) preferential water infiltration initiated by tree trunk and root systems; and (2) lateral subsurface flow due to soil layering. Results revealed links between different types of soil heterogeneity and non-uniform flow. The first experimental site provided evidence of root-induced preferential flow paths along coarse roots, emphasizing the important role of coarse roots in facilitating preferential water movement through the subsurface. The second experimental site showed that water infiltrated through the restrictive layer mainly following the plant root system. The presented approach offers a non-invasive, repeatable and accurate way to detect non-uniform flow.

Detecting infiltrated water and preferential flow pathways through time-lapse ground-penetrating radar surveys.

The objective of this paper was to identify the incidence and extent of preferential flow at two experimental areas located in Lyon, France. We used time-lapse ground-penetrating radar (GPR) surveys in conjunction with automatized single-ring infiltration experiments to create three-dimensional (3D) representations of infiltrated water. In total we established three 100 cm × 100 cm GPR grids and used differenced radargrams from pre- and post-infiltration surveys to detect wetting patterns. The analyzed time-lapse GPR surveys revealed the linkage between nonuniform flow and heterogeneous soil structures and plant roots. At the first experimental area, subsurface coarse gravels acted as capillary barriers that concentrated flow into narrow pathways via funneled flow. At the second experimental area, the interpolated 3D patterns closely matched direct observation of dyed patterns, thereby validating the applied protocol. They also highlighted the important role of plant roots in facilitating preferential water movement through the subsurface. The protocol presented in this study represents a valuable tool for improving the hydraulic characterization of highly heterogeneous soils, while also alleviating some of the excessive experimental efforts currently needed to detect preferential flow pathways in the field.

Transdisciplinary interventions for environmental sustainability.

This paper presents a case study of a transdisciplinary research based on an ex-post assessment of the environmental and socio-behavioral contexts of solid waste management in Lebanese peri-urban communities. Lessons learned are compiled into the Transdisciplinary Interventions for Environmental Sustainability conceptual framework. The approach starts with building a team of researchers and non-academic partners, continues with co-creating solution-oriented knowledge, and ends by integrating and applying the produced knowledge. The co-created knowledge includes the environmental and socio-behavioral ex-post assessment's results. The former reveals low air pollution levels, evidence of waste-related water contamination, and higher self-reported frequencies of ill-health symptoms and diseases closer to the landfill. The latter indicates that the community's perception about waste production differs from the real accounting of generated waste. Nine lessons are identified: (1) inherent common interest between the researchers and the community, (2) flexible interdisciplinary research team, (3) representative citizen committee, (4) contextually-informed outreach coordinator, (5) iterative research process accounting for the shifting socio-political context, (6) common expectations of the research process, (7) boundary objects leading to spin-off activities in the same setting, (8) effective communication strategy, and (9) ex-post assessment of subsequent societal and scientific impacts. The non-phased framework links all nine pointers in a logical order to ease scalability. The study answers a global need for a unified, clear, broadly adopted framework for transdisciplinarity and a deeper understanding of factors ensuring full-circle knowledge co-creation in waste-related contexts in the global South. The study offers managerial and research implications and suggests avenues for further research.

Data-Worth Assessment for a Three-Dimensional Optimal Design in Nonlinear Groundwater Systems.

Groundwater model predictions are often uncertain due to inherent uncertainties in model input data. Monitored field data are commonly used to assess the performance of a model and reduce its prediction uncertainty. Given the high cost of data collection, it is imperative to identify the minimum number of required observation wells and to define the optimal locations of sampling points in space and depth. This study proposes a design methodology to optimize the number and location of additional observation wells that will effectively measure multiple hydrogeological parameters at different depths. For this purpose, we incorporated Bayesian model averaging and genetic algorithms into a linear data-worth analysis in order to conduct a three-dimensional location search for new sampling locations. We evaluated the methodology by applying it along a heterogeneous coastal aquifer with limited hydrogeological data that is experiencing salt water intrusion (SWI). The aim of the model was to identify the best locations for sampling head and salinity data, while reducing uncertainty when predicting multiple variables of SWI. The resulting optimal locations for new observation wells varied with the defined design constraints. The optimal design (OD) depended on the ratio of the start-up cost of the monitoring program and the installation cost of the first observation well. The proposed methodology can contribute toward reducing the uncertainties associated with predicting multiple variables in a groundwater system.

Performance optimization and validation of ADM1 simulations under anaerobic thermophilic conditions.

In this study, two experimental sets of data each involving two thermophilic anaerobic digesters treating food waste, were simulated using the Anaerobic Digestion Model No. 1 (ADM1). A sensitivity analysis was conducted, using both data sets of one digester, for parameter optimization based on five measured performance indicators: methane generation, pH, acetate, total COD, ammonia, and an equally weighted combination of the five indicators. The simulation results revealed that while optimization with respect to methane alone, a commonly adopted approach, succeeded in simulating methane experimental results, it predicted other intermediary outputs less accurately. On the other hand, the multi-objective optimization has the advantage of providing better results than methane optimization despite not capturing the intermediary output. The results from the parameter optimization were validated upon their independent application on the data sets of the second digester.