Phytomanagement of a metal-contaminated agricultural soil with Sorghum bicolor, humic / fulvic acids and arbuscular mycorrhizal fungi near the former Pb/Zn metaleurop Nord smelter.

As many contaminated agricultural soils can no longer be used for food crops, lignocellulosic energy crops matter due to their ability to grow on such soils and to produce biomass for biosourced materials and biofuels, thereby reducing the pressure on the limited arable lands. Sorghum bicolor (L.) Moench, can potentially produce a high biomass suitable for producing bioethanol, renewable gasoline, diesel, and sustainable aircraft fuel, despite adverse environmental conditions (e.g. drought, contaminated soils). A 2-year field trial was carried out for the first time in the northern France for assessing sorghum growth on a Cd, Pb and Zn-contaminated agricultural soil amended with humic/fulvic acid, alone and paired with arbuscular mycorrhizal fungi. Sorghum produced on average (in t DW ha-1): 12.4 in year 1 despite experiencing a severe drought season and 15.3 in year 2. Humic/fulvic acids (Lonite 80SP®) and arbuscular mycorrhizal fungi did not significantly act as biostimulants regarding the shoot DW yield and metal uptake of sorghum. The annual shoot Cd, Pb and Zn removals averaged 0.14, 0.20 and 1.97 kg ha-1, respectively. Sorghum cultivation and its metal uptake induced a significant decrease in 0.01 M Ca(NO3)2-extractable soil Cd, Pb and Zn concentrations by 95%, 73% and 95%, respectively, in year 2. Soluble and exchangeable soil Cd, Pb and Zn would be progressively depleted in subsequent crops, which should result in lower pollutant linkages and enhanced ecosystem services. This evidenced sorghum as a relevant plant species for phytomanaging the large area (750 ha) with metal-contaminated soil near the former Pb/Zn Metaleurop Nord smelter, amidst ongoing climate change. The potential bioethanol yield of the harvested sorghum biomass was 5589 L ha-1. Thus sorghum would be a promising candidate for bioethanol production, even in this northern French region.

Integrating social vulnerability into high-resolution global flood risk mapping.

High-resolution global flood risk maps are increasingly used to inform disaster risk planning and response, particularly in lower income countries with limited data or capacity. However, current approaches do not adequately account for spatial variation in social vulnerability, which is a key determinant of variation in outcomes for exposed populations. Here we integrate annual average exceedance probability estimates from a high-resolution fluvial flood model with gridded population and poverty data to create a global vulnerability-adjusted risk index for flooding (VARI Flood) at 90-meter resolution. The index provides estimates of relative risk within or between countries and changes how we understand the geography of risk by identifying 'hotspots' characterised by high population density and high levels of social vulnerability. This approach, which emphasises risks to human well-being, could be used as a complement to traditional population or asset-centred approaches.

Plant testing with hemp and miscanthus to assess phytomanagement options including biostimulants and mycorrhizae on a metal-contaminated soil to provide biomass for sustainable biofuel production.

The need of biofuels from biomass, including sustainable aviation fuel, without using agricultural land dedicated to food crops, is in constant demand. Strategies to intensify biomass production using mycorrhizal fungi, biostimulants and their combinations could be solutions for improving the cultivation of lignocellulosic plants but still lack well-established validation on metal-contaminated soils. This study aimed to assess the yield of Miscanthus x giganteus J.M. Greef & Deuter and Cannabis sativa L. grown on a metal-contaminated agricultural soil (11 mg Cd, 536 mg Pb and 955 mg Zn kg-1) amended with biostimulants and/or arbuscular mycorrhizal fungi, and the shoot Cd, Pb and Zn uptake. A pot trial was carried out with soil collected from a field near a former Pb/Zn smelter in France and six treatments: control (C), protein hydrolysate (a mixture of peptides and amino acids, PH), humic/fulvic acids (HFA), arbuscular mycorrhizae fungi (AMF), PH combined with AMF (PHxAMF), and HFA combined with AMF (HFAxAMF). Metal concentrations in the soil pore water (SPW), pH and electrical conductivity were measured over time. Miscanthus and hemp shoots were harvested on day 90. Both PH and PHxAMF treatments increased SPW Cd, Pb, and Zn concentrations (e.g. by 26, 1.9, and 22.9 times for miscanthus and 9.7, 4.7, and 19.3 times for hemp in the PH and PHxAMF treatments as compared to the control one, respectively). This led to phytotoxicity and reduced shoot yield for miscanthus. Conversely, HFA and HFAxAMF treatments decreased SPW Cd and Zn concentrations, increasing shoot yields for hemp and miscanthus. Shoot Cd, Pb, and Zn uptakes peaked for PH and PHxAMF hemp plants (in µg plant-1, Cd: 310-334, Pb: 34-38, and Zn: 232-309 for PHxAMF and PH, respectively), while lowest values occurred for PH miscanthus plants mainly due to low shoot yield. Overall, this study suggested that humic/fulvic acids can be an effective biostimulant for increasing shoot biomass production in a metal-contaminated soil. These results warrant further investigations of the HFAxAMF in field trials.

Mapping urban living standards and economic activity in developing countries with energy data.

Urban data deficits in developing countries impede evidence-based planning and policy. Could energy data be used to overcome this challenge by serving as a local proxy for living standards or economic activity in large urban areas? To answer this question, we examine the potential of georeferenced residential electricity meter data and night-time lights (NTL) data in the megacity of Karachi, Pakistan. First, we use nationally representative survey data to establish a strong association between electricity consumption and household living standards. Second, we compare gridded radiance values from NTL data with a unique dataset containing georeferenced median monthly electricity consumption values for over 2 million individual households in the city. Finally, we develop a model to explain intra-urban variation in radiance values using proxy measures of economic activity from Open Street Map. Overall, we find that NTL data are a poor proxy for living standards but do capture spatial variation in population density and economic activity. By contrast, electricity data are an excellent proxy for living standards and could be used more widely to inform policy and support poverty research in cities in low- and middle-income countries.

High-resolution rural poverty mapping in Pakistan with ensemble deep learning.

High resolution poverty mapping supports evidence-based policy and research, yet about half of all countries lack the survey data needed to generate useful poverty maps. To overcome this challenge, new non-traditional data sources and deep learning techniques are increasingly used to create small-area estimates of poverty in low- and middle-income countries (LMICs). Convolutional Neural Networks (CNN) trained on satellite imagery are emerging as one of the most popular and effective approaches. However, the spatial resolution of poverty estimates has remained relatively coarse, particularly in rural areas. To address this problem, we use a transfer learning approach to train three CNN models and use them in an ensemble to predict chronic poverty at 1 km2 scale in rural Sindh, Pakistan. The models are trained with spatially noisy georeferenced household survey containing poverty scores for 1.67 million anonymized households in Sindh Province and publicly available inputs, including daytime and nighttime satellite imagery and accessibility data. Results from both hold-out and k-fold validation exercises show that the ensemble provides the most reliable spatial predictions in both arid and non-arid regions, outperforming previous studies in key accuracy metrics. A third validation exercise, which involved ground-truthing of predictions from the ensemble model with original survey data of 7000 households further confirms the relative accuracy of the ensemble model predictions. This inexpensive and scalable approach could be used to improve poverty targeting in Pakistan and other low- and middle-income countries.