DeepAngle: Fast calculation of contact angles in tomography images using deep learning

DeepAngle is a machine learning-based method to determine the contact angles of different phases in the tomography images of porous materials. Measurement of angles in 3--D needs to be done within the surface perpendicular to the angle planes, and it could become inaccurate when dealing with the discretized space of the image voxels. A computationally intensive solution is to correlate and vectorize all surfaces using an adaptable grid, and then measure the angles within the desired planes. On the contrary, the present study provides a rapid and low-cost technique powered by deep learning to estimate the interfacial angles directly from images. DeepAngle is tested on both synthetic and realistic images against the direct measurement technique and found to improve the r-squared by 5 to 16% while lowering the computational cost 20 times. This rapid method is especially applicable for processing large tomography data and time-resolved images, which is computationally intensive. The developed code and the dataset are available at an open repository on GitHub (https://www.github.com/ArashRabbani/DeepAngle)

Quantifying the impacts of groundwater abstraction on Ganges river water infiltration into shallow aquifers under the rapidly developing city of Patna, India

Study region Patna is located on the southern bank of the River Ganges in Bihar, India. Rapid population growth over the past few decades has driven an increase in groundwater abstraction from aquifers under the city. Study focus This study exeplores the pumping-induced water exchange between the River Ganges and groundwater under transient conditions between 2009 and 2015, using a numerical simulation. The deterministic water exchange model within an uncertainty quantification was used to reveal the controlling factors affecting river water infiltration. New hydrological insights for the region Modelling reveals that under baseline (eno pumping) conditions, the dominant (~ 91% of the year) flow direction is from the aquifer to the river, which reverses (~ 9% of the year) when the river stage is high. When a municipal pumping well is implemented, river water infiltration into the aquifer increases to 68% of the year. The groundwater pumping rate is found to be the most important factor affecting the river water infiltration, whilst the groundwater table level is most sensitive to the well distance from the river, followed by pumping rate. Optimizing the location, depth and pumping rate of new wells in the area could mitigate fluvial contamination of the aquifer and help maintain groundwater levels

The impact of phosphorus on projected Sub-Saharan Africa food security futures

Sub-Saharan Africa must urgently improve food security. Phosphorus availability is one of the major barriers to this due to low historical agricultural use. Shared socioeconomic pathways (SSPs) indicate that only a sustainable (SSP1) or a fossil fuelled future (SSP5) can improve food security (in terms of price, availability, and risk of hunger) whilst nationalistic (SSP3) and unequal (SSP4) pathways worsen food security. Furthermore, sustainable SSP1 requires limited cropland expansion and low phosphorus use whilst the nationalistic SSP3 is as environmentally damaging as the fossil fuelled pathway. The middle of the road future (SSP2) maintains today’s inadequate food security levels only by using approximately 440 million tonnes of phosphate rock. Whilst this is within the current global reserve estimates the market price alone for a commonly used fertiliser (DAP) would cost US$ 130 ± 25 billion for agriculture over the period 2020 to 2050 and the farmgate price could be two to five times higher due to additional costs (e.g. transport, taxation etc.). Thus, to improve food security, economic growth within a sustainability context (SSP1) and the avoidance of nationalist ideology (SSP3) should be prioritised

Efficiency of phosphorus resource use in Africa as defined by soil chemistry and the impact on crop production

By 2050 the global population will be 9.7 billion, placing an unprecedented burden on the world’s soils to produce extremely high food yields. Phosphorus (P) is crucial to plant growth and mineral fertilizer is added to soil to maintain P concentrations, however this is a finite resource, thus efficient use is critical. Plants primarily uptake P from a labile (available) P pool and not from the stable solid phase; transfer between these pools limits bioavailability. Transfer is controlled by soil properties which vary between soil types. The dynamic phosphorus pool simulator (DPPS) quantifies crop production and soil P relationships by utilising the transfer. This approach effectively models crop uptake from soil inputs, but it does not quantify the efficiency use. This study incorporates geochemical techniques within DPPS to quantify the efficiency of fertilizer-P use based on soil chemistry

Soil chemistry aspects of predicting future phosphorus requirements in Sub-Saharan Africa

Phosphorus (P) is a finite resource and critical to plant growth and therefore food security. Regional‐ and continental‐scale studies propose how much P would be required to feed the world by 2050. These indicate that sub‐Saharan Africa soils have the highest soil P deficit globally. However, the spatial heterogeneity of the P deficit caused by heterogeneous soil chemistry in the continental scale has never been addressed. We provide a combination of a broadly adopted P‐sorption model that is integrated into a highly influential, large‐scale soil phosphorus cycling model. As a result, we show significant differences between the model outputs in both the soil‐P concentrations and total P required to produce future crops for the same predicted scenarios. These results indicate the importance of soil chemistry for soil‐nutrient modelling and highlight that previous influential studies may have overestimated P required. This is particularly the case in Somalia where conventional modelling predicts twice as much P required to 2050 as our new proposed model. Plain language summary Improving food security in Sub‐Saharan Africa over the coming decades requires a dramatic increase in agricultural yields. Global yield increase has been driven by, amongst other factors, the widespread use of fertilisers including phosphorus. The use of fertilisers in Sub‐Saharan Africa is often prohibitively expensive and thus the most efficient use of phosphorus should be targeted. Soil chemistry largely controls phosphorus efficiency in agriculture, for example iron and aluminium which exist naturally in soil reduce the availability of phosphate to plants. Yet soil chemistry has not been included in several influential large‐scale modelling studies which estimate phosphorus requirements in Sub‐Saharan Africa to 2050. In this study we show that predictions of phosphorus requirement to feed the population of Sub‐Saharan Africa to 2050 can significantly change if soil chemistry is included (e.g. Somalia with up to 50% difference). Our findings are a new step towards making predictive decision‐making tool for phosphorus fertiliser management in Sub‐Saharan Africa considering the variability of soil chemistry

Interplay of biofilm growth, NAPL biodegradation and micro-scale heterogeneity in natural attenuation of aquifers delineated by pore-network modelling

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