Quantifying terrestrial ecosystem carbon dynamics with mechanistically-based biogeochemistry models and in situ and remotely sensed data

Terrestrial ecosystem plays a critical role in the global carbon cycle and climate system. Therefore, it is important to accurately quantify the carbon dynamics of terrestrial ecosystem under future climatic change condition. This dissertation evaluates the regional carbon dynamics by using upscaling approach, mechanistically-based biogeochemistry models and in situ and remotely sensed data. The upscaling studies based on FLUXNET network has provided us the spatial and temporal pattern of the carbon fluxes but it fails to consider the atmospheric CO2 effect given its important physiological role in carbon assimilation. In the second chapter, we consider the effect of atmospheric CO2 using an artificial neural network (ANN) approach to upscale the AmeriFlux tower of net ecosystem exchange (NEE) and the derived gross primary productivity (GPP) to the conterminous United States. We found that atmospheric CO 2effect on GPP/NEE exhibited a great spatial and seasonal variability. Further analysis suggested that air temperature played an important role in determining the atmospheric CO2 effects on carbon fluxes. In addition, the simulation that did not consider atmospheric CO2 failed to detect ecosystem responses to droughts in part of the US in 2006. The study suggested that the spatially and temporally varied atmospheric CO2 concentrations should be factored into carbon quantification when scaling eddy flux data to a region. The process-based ecosystem models are useful tools to predicting future change in the terrestrial ecosystem. However, they suffer the great uncertainty induced by model structure and parameters. The carbon isotope (13C) discrimination by terrestrial plants, involves the biophysical and biogeochemistry processes and exhibits seasonal and spatial variations, which may provide additional constraints on model parameters. In the third chapter, we found that using foliar 13C composition data, model parameters were constrained to a relatively narrow space and the site-level model simulations were slightly better than that without the foliar 13C constraint. The model extrapolations with three stomatal schemes all showed that the estimation uncertainties of regional carbon fluxes were reduced by about 40%. In addition, tree ring data have great potentials in addressing the forest response to climatic changes compared with mechanistic model simulations, eddy flux measurement and manipulative experiments. In the fourth chapter, we collected the tree ring isotopic carbon data at 12 boreal forest sites to develop a linear regression model, and the model was extrapolated to the whole boreal region to obtain the water use efficiency (WUE) and GPP spatial and temporal variation from 1948 to 2010. Our results demonstrated that most of boreal regions except parts of Alaska showed a significant increasing WUE trend during the study period and the increasing magnitude was much higher than estimations from other land surface models. Our predicted GPP by the WUE definition algorithm was comparable with site observation, while for the revised light use efficiency algorithm, GPP estimation was higher than site observation as well as land surface model estimates. In addition, the increasing GPP trends estimated by two algorithms were similar with land surface model simulations

Global soil consumption of atmospheric carbon monoxide : an analysis using a process-based biogeochemistry model

Carbon monoxide (CO) plays an important role in controlling the oxidizing capacity of the atmosphere by reacting with OH radicals that affect atmospheric methane (CH4) dynamics. We develop a process-based biogeochemistry model to quantify the CO exchange between soils and the atmosphere with a 5 min internal time step at the global scale. The model is parameterized using the CO flux data from the field and laboratory experiments for 11 representative ecosystem types. The model is then extrapolated to global terrestrial ecosystems using monthly climate forcing data. Global soil gross consumption, gross production, and net flux of the atmospheric CO are estimated to be from -197 to -180, 34 to 36, and -163 to -145 TgCOyr(-1) (1 Tg = 10(12) g), respectively, when the model is driven with satellite-based atmospheric CO concentration data during 2000-2013. Tropical evergreen forest, savanna and deciduous forest areas are the largest sinks at 123 TgCOyr(-1). The soil CO gross consumption is sensitive to air temperature and atmospheric CO concentration, while the gross production is sensitive to soil organic carbon (SOC) stock and air temperature. By assuming that the spatially distributed atmospheric CO concentrations (similar to 128 ppbv) are not changing over time, the global mean CO net deposition velocity is estimated to be 0.16-0.19mms 1 during the 20th century. Under the future climate scenarios, the CO deposition velocity will increase at a rate of 0.0002-0.0013 mms 1 r(-1) during 2014-2100, reaching 0.20-0.30 mm s(-1) by the end of the 21st century, primarily due to the increasing temperature. Areas near the Equator, the eastern US, Europe and eastern Asia will be the largest sinks due to optimum soil moisture and high temperature. The annual global soil net flux of atmospheric CO is primarily controlled by air temperature, soil temperature, SOC and atmospheric CO concentrations, while its monthly variation is mainly determined by air temperature, precipitation, soil temperature and soil moisture.Peer reviewe

Relationship between Thermal Conductivity and Compressive Strength of Insulation Concrete: A Review

Developing insulation concrete with high strength is essential for the construction of energy saving buildings. This is important to achieve carbon neutrality in the modern building industry. This paper reviews the existing studies in the literature on insulation concrete. This paper aims to reveal the correlation between the thermal conductivity and strength of concrete and identify the most effective method to make insulation concrete with lower thermal conductivity but higher strength. The review is carried out from two perspectives, including the effects of different foaming methods and various lightweight aggregates. As for the foaming methods, the chemical and mechanical foaming methods are discussed. As for the lightweight aggregates, cenospheres, porous aggregates, aerogels, and phase change materials are assessed. It is clearly observed that the thermal conductivity and compressive strength of concrete can be fitted by a linear function. As for the foaming methods, chemical foaming using hydrogen peroxide is the most effective to produce concrete with relatively lower thermal conductivity and higher compressive strength. For concrete with lightweight aggregates, cenospheres are the best option. Finally, recommendations are made to develop concrete with lower thermal conductivity and higher strength

SYENet: A Simple Yet Effective Network for Multiple Low-Level Vision Tasks with Real-time Performance on Mobile Device

With the rapid development of AI hardware accelerators, applying deep learning-based algorithms to solve various low-level vision tasks on mobile devices has gradually become possible. However, two main problems still need to be solved: task-specific algorithms make it difficult to integrate them into a single neural network architecture, and large amounts of parameters make it difficult to achieve real-time inference. To tackle these problems, we propose a novel network, SYENet, with only $~$6K parameters, to handle multiple low-level vision tasks on mobile devices in a real-time manner. The SYENet consists of two asymmetrical branches with simple building blocks. To effectively connect the results by asymmetrical branches, a Quadratic Connection Unit(QCU) is proposed. Furthermore, to improve performance, a new Outlier-Aware Loss is proposed to process the image. The proposed method proves its superior performance with the best PSNR as compared with other networks in real-time applications such as Image Signal Processing(ISP), Low-Light Enhancement(LLE), and Super-Resolution(SR) with 2K60FPS throughput on Qualcomm 8 Gen 1 mobile SoC(System-on-Chip). Particularly, for ISP task, SYENet got the highest score in MAI 2022 Learned Smartphone ISP challenge

ZnCdS Dotted with Highly Dispersed Pt Supported on SiO2 Nanospheres Promoting Photocatalytic Hydrogen Evolution

[EN] The efficiency of solar hydrogen evolution closely depends on the fast transfer of charge carriers and the effective use of visible light. In this work, a novel photocatalyst SiO2/ZnCdS/Pt was successfully prepared to solve these two problems. An artistic structure of the photocatalyst was constructed and ZnCdS was successfully wrapped on the surface of SiO2 spheres with uniform Pt nanoparticles (NPs) in a size of 4.1 +/- 0.7 nm highly dispersed on the ZnCdS shell through the self-assembly method. Pt NPs can absorb the scattered light in the near field of SiO2 spheres. With the synergistic effect of SiO2 spheres and small highly dispersed Pt NPs, the absorption of visible light was significantly promoted. Meanwhile, the electron-hole recombination was also effectively inhibited, thus improving the photocatalytic activity. The hydrogen production activity of the highly efficient photocatalyst was as high as 8.3 mmol g(-1) h(-1) under visible light (lambda > 420 nm). The photocatalytic activity of SiO2/ZnCdS/Pt was 2.9 times higher than that of the ZnCdS/Pt photocatalyst.This work was supported by the National Natural Science Foundation of China (21976111), Shandong Provincial Natural Science Foundation (ZR2019MB052), and Large Instrument Open Foundation of Shandong Normal University (KFJJ2019004; KFJJ2021006).Liu, K.; Peng, L.; Zhen, P.; Chen, L.; Song, S.; García Gómez, H.; Sun, C. (2021). ZnCdS Dotted with Highly Dispersed Pt Supported on SiO2 Nanospheres Promoting Photocatalytic Hydrogen Evolution. The Journal of Physical Chemistry C. 125(27):14656-14665. https://doi.org/10.1021/acs.jpcc.1c0353514656146651252