Sludge transforms into biochar: Doping calcium induces phosphorus transforming into a plant-available speciation

The mass-produced sewage sludge (SS) worldwide is regarded as an important phosphorus (P) pool with a P-content of 2-3% (dry basis). Pyrolytic conversion of SS into P-rich biochar has multiple environmental benefits: toxicity elimination, carbon sequestration and soil fertilization. It has been proved that P transforms into insoluble speciation such as Ca2P2O7 during pyrolysis, and this would be influenced significantly by inherent minerals such as Ca, Mg, Fe, Al, etc [1, 2]. With a purpose of enhancing biochar’s fertilizer efficiency to plant, we selected calcium (Ca) as an additive to SS and expected their thermal-chemical interaction would induce P transforming into a plant-available speciation. The sequential extraction experiments showed that after pyrolysis (biochar: SS500) the percent of the insoluble phosphates (HCl-extracted P) increased significantly from 8.28% to 76.6%, while the readily soluble P species being extracted by water, NaHCO3 and NaOH decreased sharply. Doping CaCl2 strengthened this transformation and the produced biochars at pyrolysis temperature of 500oC with 20% (w/w) Ca-doping (biochar: SS-Ca500) contained 84.1% insoluble phosphates and 5.28% Fe/Al mineral adsorbed P (NaOH-extracted P). It indicated that Ca could compete for more P than Fe/Al during pyrolysis. Instrumental analysis (XRD, NMR) showed that Ca promoted more formation of pyrophosphate and short-chain polyphosphates such as Ca5(PO4)3(OH), Ca5(PO4)3Cl, which are species facilitating plant-uptake while avoiding dissolution loss. This study gave an insight into P speciation transformation during biochar formation and suggested that P availability in biochars are controllable by doping minerals to structure a safe slow-release P fertilizer benefiting plant growth. Please click Additional Files below to see the full abstract

Mesoscopic Interactions and Species Coexistence in Evolutionary Game Dynamics of Cyclic Competitions

Date of Acceptance: 27/11/2014Peer reviewedPublisher PD

On the use of explainable AI for susceptibility modeling: Examining the spatial pattern of SHAP values

Hydro-morphological processes (HMP, any natural phenomenon contained within the spectrum defined between debris flows and flash floods) are globally occurring natural hazards which pose great threats to our society, leading to fatalities and economical losses. For this reason, understanding the dynamics behind HMPs is needed to aid in hazard and risk assessment. In this work, we take advantage of an explainable deep learning model to extract global and local interpretations of the HMP occurrences across the whole Chinese territory. We use a deep neural network architecture and interpret the model results through the spatial pattern of SHAP values. In doing so, we can understand the model prediction on a hierarchical basis, looking at how the predictor set controls the overall susceptibility as well as doing the same at the level of the single mapping unit. Our model accurately predicts HMP occurrences with AUC values measured in a ten-fold cross-validation ranging between 0.83 and 0.86. This level of predictive performance attests for an excellent prediction skill. The main difference with respect to traditional statistical tools is that the latter usually lead to a clear interpretation at the expense of high performance, which is otherwise reached via machine/deep learning solutions, though at the expense of interpretation. The recent development of explainable AI is the key to combine both strengths. In this work, we explore this combination in the context of HMP susceptibility modeling. Specifically, we demonstrate the extent to which one can enter a new level of data-driven interpretation, supporting the decision-making process behind disaster risk mitigation and prevention actions

Cross-level Attention with Overlapped Windows for Camouflaged Object Detection

Camouflaged objects adaptively fit their color and texture with the environment, which makes them indistinguishable from the surroundings. Current methods revealed that high-level semantic features can highlight the differences between camouflaged objects and the backgrounds. Consequently, they integrate high-level semantic features with low-level detailed features for accurate camouflaged object detection (COD). Unlike previous designs for multi-level feature fusion, we state that enhancing low-level features is more impending for COD. In this paper, we propose an overlapped window cross-level attention (OWinCA) to achieve the low-level feature enhancement guided by the highest-level features. By sliding an aligned window pair on both the highest- and low-level feature maps, the high-level semantics are explicitly integrated into the low-level details via cross-level attention. Additionally, it employs an overlapped window partition strategy to alleviate the incoherence among windows, which prevents the loss of global information. These adoptions enable the proposed OWinCA to enhance low-level features by promoting the separability of camouflaged objects. The associated proposed OWinCANet fuses these enhanced multi-level features by simple convolution operation to achieve the final COD. Experiments conducted on three large-scale COD datasets demonstrate that our OWinCANet significantly surpasses the current state-of-the-art COD methods

Rapid Regeneration Offsets Losses from Warming-Induced Tree Mortality in an Aspen-Dominated Broad-Leaved Forest in Northern China

Worldwide tree mortality as induced by climate change presents a challenge to forest managers. To successfully manage vulnerable forests requires the capacity of regeneration to compensate for losses from tree mortality. We observed rapid regeneration and the growth release of young trees after warming-induced mortality in a David aspen-dominated (Populus davidiana) broad-leaved forest in Inner Mongolia, China, as based on individual tree measurements taken in 2012 and 2015 from a 6-ha permanent plot. Warming and drought stress killed large trees 10–15 m tall with a total number of 2881 trees during 2011–2012, and also thinned the upper crowns. David aspen recruitment increased 2 times during 2012–2015 and resulted in a high transition probability of David aspen replacing the same or other species, whereas the recruitment of Mongolian oak (Quercus mongolica) was much lower: it decreased from 2012 to 2015, indicating that rapid regeneration represented a regrowth phase for David aspen, and not succession to Mongolian oak. Further, we found that the recruitment density increased with canopy openness, thus implying that warming-induced mortality enhanced regeneration. Our results suggest that David aspen has a high regrowth ability to offset individual losses from warming-induced mortality. This important insight has implications for managing this vulnerable forest in the semi-arid region of northern China

Impact-based probabilistic modeling of hydro-morphological processes in China (1985–2015)

Hydro-morphological processes (HMP, any natural phenomenon contained within the spectrum defined between debris flows and flash floods) pose a relevant threat to infrastructure, urban and rural settlements and to lives in general. This has been widely observed in recent years and will likely become worse as climate change will influence the spatio-temporal pattern of precipitation events. The modelling of where HMP-driven hazards may occur can help define the appropriate course of actions before and during a crisis, reducing the potential losses that HMPs cause in their wake. However, the probabilistic information on locations prone to experience a given hazard is not sufficient to depict the risk our society may incur. To cover this aspect, modeling the loss information could open up to better territorial management strategies. In this work, we made use of the HMP catalogue of China from 1985 to 2015. Specifically, we implemented the Light Gradient Boosting (LGB) classifier to model the impact level that locations across China have suffered from HMPs over the thirty-year record. We obtained six impact levels as a combination of financial and life losses, whose classes we used as separate target variables for our LGB. In doing so, we estimated spatial probabilities of certain HMP impact, something that has yet to be tested in the natural hazard community, especially over such a large spatial domain. The results we obtained are encouraging, with each of the six impact categories being separately classified with excellent to outstanding performance (the worst case corresponds to a mean AUC = 0.862, whereas the best case corresponds to a mean AUC of 0.915). The good predictive performance our model produced suggest that the cartographic output could be useful to inform authorities of locations prone to human and infrastructural losses of specific magnitudes.</p