• Energy Research
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Pine wood nematodes (PWN, Bursaphelenchus xylophilus) cause widespread mortality in pine forests via pine wilt disease (PWD). The rapid death of diseased trees, which destroys biodiversity and significantly affects forest carbon storage, leading to negative environmental and economic consequences, as forests are crucial to the global carbon cycle. The interactions among PWN, hosts, and vector insects are closely linked to climate change. Climate warming has exacerbated changes in the geographic distribution of host tree species and vector insects, thereby increasing the rate and extent of PWD transmission. These interactions increase the risk of pine infection and can have far-reaching consequences for the health and stability of entire forest ecosystems. However, the global effects of climate change on these interactions are poorly understood. To fill this research gap and predict the potential impacts of climate change on the distribution of PWNs and vector insects in pine forests, we used the biomod2 integrated model to forecast their potential geographic distributions by 2050, 2070, and 2090 under three greenhouse gas emission scenarios (SSP126, SSP245, and SSP585). We analysed vector dominance and risk zones and found that potentially suitable areas for PWNs could migrate to higher latitudes in the future. The dominant vector insects, Monochamus alternatus, Monochamus carolinensis, and Monochamus saltuarius, exhibited a high ecological niche similarity to PWNs and their populations should be controlled. Additionally, high-risk areas for abiotic factors (environmental similarity) and biotic factors (hosts and vectors) will greatly expand in North America and Europe. Areas already infested by PWN will become high-risk zones for the conversion of carbon sinks to carbon sources. The modeled changes in the spatial and temporal patterns of PWN, hosts, and vector insects in this study provide a reference for developing management and conservation strategies for ensuring PWN control and improving future forest health.

Pine wood nematodes (PWN, Bursaphelenchus xylophilus) cause widespread mortality in pine forests via pine wilt disease (PWD). The rapid death of diseased trees, which destroys biodiversity and significantly affects forest carbon storage, leading to negative environmental and economic consequences, as forests are crucial to the global carbon cycle. The interactions among PWN, hosts, and vector insects are closely linked to climate change. Climate warming has exacerbated changes in the geographic distribution of host tree species and vector insects, thereby increasing the rate and extent of PWD transmission. These interactions increase the risk of pine infection and can have far-reaching consequences for the health and stability of entire forest ecosystems. However, the global effects of climate change on these interactions are poorly understood. To fill this research gap and predict the potential impacts of climate change on the distribution of PWNs and vector insects in pine forests, we used the biomod2 integrated model to forecast their potential geographic distributions by 2050, 2070, and 2090 under three greenhouse gas emission scenarios (SSP126, SSP245, and SSP585). We analysed vector dominance and risk zones and found that potentially suitable areas for PWNs could migrate to higher latitudes in the future. The dominant vector insects, Monochamus alternatus, Monochamus carolinensis, and Monochamus saltuarius, exhibited a high ecological niche similarity to PWNs and their populations should be controlled. Additionally, high-risk areas for abiotic factors (environmental similarity) and biotic factors (hosts and vectors) will greatly expand in North America and Europe. Areas already infested by PWN will become high-risk zones for the conversion of carbon sinks to carbon sources. The modeled changes in the spatial and temporal patterns of PWN, hosts, and vector insects in this study provide a reference for developing management and conservation strategies for ensuring PWN control and improving future forest health.

In order to improve wind power prediction accuracy and increase the utilization of wind power, this study proposes a novel complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN)–variational modal decomposition (VMD)–gated recurrent unit (GRU) prediction model. With the goal of extracting feature information that existed in temporal series data, CEEMDAN and VMD decomposition are used to divide the raw wind data into several intrinsic modal function components. Furthermore, to reduce computational burden and enhance convergence speed, these intrinsic mode function (IMF) components are integrated and rebuilt via the results of sample entropy and K-means. Lastly, to ensure the completeness of the prediction outcomes, the final prediction results are synthesized through the superposition of all IMF components. The simulation results indicate that the proposed model is superior to other models in accuracy and robustness.

In order to improve wind power prediction accuracy and increase the utilization of wind power, this study proposes a novel complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN)–variational modal decomposition (VMD)–gated recurrent unit (GRU) prediction model. With the goal of extracting feature information that existed in temporal series data, CEEMDAN and VMD decomposition are used to divide the raw wind data into several intrinsic modal function components. Furthermore, to reduce computational burden and enhance convergence speed, these intrinsic mode function (IMF) components are integrated and rebuilt via the results of sample entropy and K-means. Lastly, to ensure the completeness of the prediction outcomes, the final prediction results are synthesized through the superposition of all IMF components. The simulation results indicate that the proposed model is superior to other models in accuracy and robustness.

Iron-based oxygen carriers (OCs) have received much attention due to their low costs, high mechanical strengths and high-temperature stabilities in the chemical looping gasification (CLG) of biomass, but their chemical reactivity is very ordinary. Converter steel slags (CSSs) are steelmaking wastes and rich in Fe2O3, CaO and MgO, which have good oxidative ability and good stability as well as catalytic effects on biomass gasification. Therefore, the composite OCs prepared by mechanically mixing CSSs with iron-based OCs are expected to be used to increase the hydrogen production in the CLG of biomass. In this study, the catalytic performance of CSS/Fe2O3 composite OCs prepared by mechanically mixing CSSs with iron-based OCs on the gasification of brewers’ spent grains (BSGs) were investigated in a tubular furnace experimental apparatus. The results showed that when the weight ratio of the CSSs in composite OCs was 0.5, the relative volume fraction of hydrogen reached the maximum value of 49.1%, the product gas yield was 0.85 Nm3/kg and the gasification efficiency was 64.05%. It could be found by X-ray diffraction patterns and scanning electron microscope characterizations that the addition of CSSs helped to form MgFe2O4, which are efficient catalysts for H2 production. Owing to the large and widely distributed surface pores of CSSs, mixing them with iron-based OCs was beneficial for catalytic steam reforming to produce hydrogen.