Nitrogen-mediated volatilisation of defensive metabolites in tomato confers resistance to herbivores.

Plants synthesise a vast array of volatile organic compounds (VOCs), which serve as chemical defence and communication agents in their interactions with insect herbivores. Although nitrogen (N) is a critical resource in the production of plant metabolites, its regulatory effects on defensive VOCs remain largely unknown. Here, we investigated the effect of N content in tomato (Solanum lycopersicum) on the tobacco cutworm (Spodoptera litura), a notorious agricultural pest, using biochemical and molecular experiments in combination with insect behavioural and performance analyses. We observed that on tomato leaves with different N contents, S. litura showed distinct feeding preference and growth and developmental performance. Particularly, metabolomics profiling revealed that limited N availability conferred resistance upon tomato plants to S. litura is likely associated with the biosynthesis and emission of the volatile metabolite α-humulene as a repellent. Moreover, exogenous application of α-humulene on tomato leaves elicited a significant repellent response against herbivores. Thus, our findings unravel the key factors involved in N-mediated plant defence against insect herbivores and pave the way for innovation of N management to improve the plant defence responses to facilitate pest control strategies within agroecosystems.

Enhanced photocatalytic degradation of tetracycline by a H2O2-assisted Bi3NbO7/Bi2Sn2O7 composite under visible light.

A Z-scheme BNO/BSO composite photocatalyst has been successfully prepared using an in situ solvothermal method. The phase component, microstructure and optical properties of the as-prepared samples were characterized using X-ray diffractometry, scanning electron microscopy, transmission electron microscopy, X-ray photoelectron spectroscopy, photoluminescence spectroscopy and other means. The photocatalytic performance of the BNO/BSO composite was evaluated via the degradation of the typical antibiotic tetracycline (TC) under hydrogen peroxide assistance and visible light irradiation. The "cata + H2O2 + vis" system shows the best photocatalytic activity, and its apparent rate constant reaches 0.03164 min-1, which is 4.9 times and 5.7 times that of the "cata + vis" system and the "cata + H2O2" system, respectively. Compared with pristine that of BSO alone, the reaction rate constant of the 15% BNO/BSO composite increases 1.8 times. The enhanced photocatalytic activity is attributed to the construction of a unique Z-scheme-type heterojunction, which effectively suppresses the recombination of electron-hole pairs. In addition, the addition of H2O2 promotes the generation of more active species. Moreover, a possible photocatalytic degradation mechanism is also further proposed.

Effects of dairy processing sludge and derived biochar on greenhouse gas emissions from Danish and Irish soils.

Globally, to ensure food security bio-based fertilizers must replace a percentage of chemical fertilizers. Such replacement must be deemed sustainable from agronomic and greenhouse gas (GHG) emission perspectives. For agronomic performance several controlled protocols are in place but not for testing GHG emissions. Herein, a pre-screening tool is presented to examine GHG emissions from bio-waste as fertilizers. The various treatments examined are as follows: soil with added mineral nitrogen (N, 140 kg N ha-1) fertilizer (MF), the same amount of MF combined with dairy processing sludge (DS), sludge-derived biochar produced at 450 °C (BC450) and 700 °C (BC700) and untreated control (CK). These treatments were combined with Danish (sandy loam) or Irish (clay loam) soils, with carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) emissions and soil inorganic-N contents measured on selected days. During the incubation, biochar mitigated N2O emissions by regulating denitrification. BC450 reduced N2O emissions from Danish soil by 95.5% and BC700 by 97.7% compared to emissions with the sludge application, and for Irish soil, the N2O reductions were 93.6% and 32.3%, respectively. For both soils, biochar reduced CO2 emissions by 50% as compared to the sludge. The lower N2O reduction potential of BC700 for Irish soil could be due to the high soil organic carbon and clay content and pyrolysis temperature. For the same reasons emissions of N2O and CO2 from Irish soil were significantly higher than from Danish soil. The temporal variation in N2O emissions was correlated with soil inorganic-N contents. The CH4 emissions across treatments were not significantly different. This study developed a simple and cost-effective pre-screening method to evaluate the GHG emission potential of new bio-waste before its field application and guide the development of national emission inventories, towards achieving the goals of circular economy and the European Green Deal.

Hydrothermal carbonization (HTC) of dairy waste: effect of temperature and initial acidity on the composition and quality of solid and liquid products.

Background: Hydrothermal carbonization (HTC) of dairy processing waste was performed to investigate the effect of temperature and initial pH on the yield and composition of the solid (hydrochar) and liquor produced. All hydrochars met the EU requirements of organo-mineral solid fertilizers defined in the Fertilizing Products Regulation in terms of phosphorus (P) and mineral content. Methods: Laboratory scale HTC was performed using pressurized reactors, and the products (solid and liquid) were collected, stored and analyzed for elemental composition and nutrient content using Inductively coupled plasma optical emission spectroscopy (ICP-OES), ultraviolet-visible spectrophotometry (UV-Vis) and other analytic techniques. Results: Maximum hydrochar yield (60.67%) was observed at T=180℃ and pH=2.25, whereas the maximum P-recovery was 80.38% at T=220℃ and pH=4.6. The heavy metal content of the hydrochars was mostly compliant with EU limitations, except for Ni at T=220℃ and pH=8.32. Meanwhile, further study of Chromium (Cr) species is essential to assess the fertilizer quality of the hydrochars. For the liquid product, the increase in temperature beyond 200℃, coupled with an increase in initial acidity (pH=2.25) drove P into the liquor. Simultaneously, increasing HTC temperature and acidity increased the concentration of NO 3 - and NH 4 + in the liquid products to a maximum of 278 and 148 mg/L, respectively, at T=180℃ and pH=4.6. Furthermore, no direct relation between final pH of liquor and NH 4 + concentration was observed. Conclusions: HTC allows for the production of hydrochar as a potential fertilizer material that requires further processing. Adjusting HTC conditions enhanced P-recovery in the hydrochar, while retrieving higher nitrate concentrations in the liquid product. Optimizing HTC for the production of qualified hydrochars requires further treatment of Cr content, studying the availability of P in the products and enhancing the hydrochar yield for economic feasibility.

Investigating the performance of satellite-based models in estimating the surface PM2.5 over China.

Accurate estimation of surface PM2.5 concentration is critical for the assessment of PM2.5 exposure and associated health impacts. Due to the limited spatial coverage of ground monitoring stations, most studies often use the satellite products to estimate surface PM2.5 concentration by constructing a comprehensive relationship between satellite-retrieved aerosol optical depth (AOD) and ground-based measured PM2.5 concentration with machine learning (ML) technologies. However, uncertainties of ML-based models may lead to considerable biases in PM2.5 estimation, which need carefully examined. Here we evaluate the accuracy of estimated PM2.5 concentration from two popular ML-models (i.e., Random Forest and the BP Neural Network) which were trained and tested using hourly data of satellite-retrieved AOD from HIMAWARI, ground-based measured PM2.5 from China National Environmental Monitoring Center, ERA5 meteorological conditions, and other auxiliary variables for a whole year of 2017 over China. We propose a new validation method considering the spatial pattern of the data during the validation. The results suggest that the traditional validation methods may overestimate the performance of the models on estimating the PM2.5 at the area with sparse in-situ measurements. Moreover, the spatial distribution pattern of the training data will largely affect the evaluation of models performance, which should be carefully considered. For future study, at least a site-specifically validation is needed rather than only using random sampling validation.

Equilibrium sampling informs tissue residue and sediment remediation for pyrethroid insecticides in mariculture: A laboratory demonstration.

Mariculture product safety in relation to sediment quality has attracted increasing attention because of the accumulation of potentially hazardous chemicals, including pyrethroid insecticides, in sediment. Passive sampling has been widely used to assess the bioavailability of sediment-associated hydrophobic organic contaminants and predict their body residue in benthic organisms. Therefore, in this study, we introduced polydimethylsiloxane (PDMS) polymer as a biomimetic "chemometer" for freely-dissolved concentrations (Cfree) to assess the efficacy of different carbon sorbents in reducing the bioavailability of pyrethroids in the process of sediment remediation. Black carbon (BC)-based materials (e.g., charcoal, biochar, and activated carbon) showed the advantageous sorption capacity over humic substance-based peat soil based on both Cfree and tissue residue in exposed clams. Of the tested BC-type materials, biochar appeared to be an ideal one in the remediation of pyrethroid-contaminated sediment. The predictive value of the PDMS chemometer approach to informing tissue residue was confirmed by a good agreement between the measured lipid-normalized concentrations of pyrethroids in clams and the lipid-based equilibrium concentrations calculated from Cfree via lipid-water partition coefficients. The quantitative inter-compartmental relationship underlying the laboratory system of sediment-pore water-PDMS-biota was also cross-validated by a mechanistically-based bioaccumulation model, thus confirming the validity of Cfree as a predictive intermediate to alert for tissue residue and guide sediment remediation. The present study revealed a great promise of sensing Cfree by polymer-based equilibrium sampling in predicting tissue residue of chemicals applied in mariculture against regulatory guidelines, and, in turn, informing remediation measures when needs arise. In situ demonstration is warranted in the future to ascertain the field applicability of this approach in real mariculture systems.

Dense Stereo Matching Method Based on Local Affine Model.

A new method for constructing an accurate disparity space image and performing an efficient cost aggregation in stereo matching based on local affine model is proposed in this paper. The key algorithm includes a new self-adapting dissimilarity measurement used for calculating the matching cost and a local affine model used in cost aggregation stage. Different from the traditional region-based methods, which try to change the matching window size or to calculate an adaptive weight to do the aggregation, the proposed method focuses on obtaining the efficient and accurate local affine model to aggregate the cost volume while preserving the disparity discontinuity. Moreover, the local affine model can be extended to the color space. Experimental results demonstrate that the proposed method is able to provide subpixel precision disparity maps compared with some state-of-the-art stereo matching methods.

Bayesian Stereo Matching Method Based on Edge Constraints.

A new global stereo matching method is presented that focuses on the handling of disparity, discontinuity and occlusion. The Bayesian approach is utilized for dense stereo matching problem formulated as a maximum a posteriori Markov Random Field (MAP-MRF) problem. In order to improve stereo matching performance, edges are incorporated into the Bayesian model as a soft constraint. Accelerated belief propagation is applied to obtain the maximum a posteriori estimates in the Markov random field. The proposed algorithm is evaluated using the Middlebury stereo benchmark. Our experimental results comparing with some state-of-the-art stereo matching methods demonstrate that the proposed method provides superior disparity maps with a subpixel precision.