Soil spatial information has traditionally been presented as polygon maps at coarse scales. Solving global and local issues, including food security, water regulation, land degradation, and climate change requires hig...Soil spatial information has traditionally been presented as polygon maps at coarse scales. Solving global and local issues, including food security, water regulation, land degradation, and climate change requires higher quality, more consistent and detailed soil information. Accurate prediction of soil variation over large and complex areas with limited samples remains a challenge, which is especially significant for China due to its vast land area which contains the most diverse soil landscapes in the world. Here, we integrated predictive soil mapping paradigm with adaptive depth function fitting, state-of-the-art ensemble machine learning and high-resolution soil-forming environment characterization in a highperformance parallel computing environment to generate 90-m resolution national gridded maps of nine soil properties(pH, organic carbon, nitrogen, phosphorus, potassium, cation exchange capacity, bulk density, coarse fragments, and thickness) at multiple depths across China. This was based on approximately5000 representative soil profiles collected in a recent national soil survey and a suite of detailed covariates to characterize soil-forming environments. The predictive accuracy ranged from very good to moderate(Model Efficiency Coefficients from 0.71 to 0.36) at 0–5 cm. The predictive accuracy for most soil properties declined with depth. Compared with previous soil maps, we achieved significantly more detailed and accurate predictions which could well represent soil variations across the territory and are a significant contribution to the GlobalSoilMap.net project. The relative importance of soil-forming factors in the predictions varied by specific soil property and depth, suggesting the complexity and non-stationarity of comprehensive multi-factor interactions in the process of soil development.展开更多
The present paper describes an optimization work to obtain the properties related to a pyrolysis process in the solid material such as density, specific heat, conductivity of virgin and char, heat of pyrolysis and kin...The present paper describes an optimization work to obtain the properties related to a pyrolysis process in the solid material such as density, specific heat, conductivity of virgin and char, heat of pyrolysis and kinetic parameters used for deciding pyrolysis rate. A repulsive particle swarm optimization algorithm is used to obtain the pyrolysis-related properties. In the previous study all properties obtained only using a cone calorimeter but in this paper both the cone calorimeter and thermo gravimetric analysis (TGA) are used for precisely optimizing the pyrolysis properties. In the TGA test a very small mass is heated up and conduction and heat capacity in the specimen is negligible so kinetic parameters can first be optimized. Other pyrolysis-related properties such as virgin/char specific heat and conductivity and char density are also optimized in the cone calorimeter test with the already decided parameters in the TGA test.展开更多
基金the National Key Basic Research Special Foundation of China(2008FY110600 and 2014FY110200)the National Natural Science Foundation of China(41930754 and42071072)+1 种基金the 2nd Comprehensive Scientific Survey of the Qinghai-Tibet Plateau(2019QZKK0306)the Project of “OneThree-Five”Strategic Planning&Frontier Sciences of the Institute of Soil Science,Chinese Academy of Sciences(ISSASIP1622)。
文摘Soil spatial information has traditionally been presented as polygon maps at coarse scales. Solving global and local issues, including food security, water regulation, land degradation, and climate change requires higher quality, more consistent and detailed soil information. Accurate prediction of soil variation over large and complex areas with limited samples remains a challenge, which is especially significant for China due to its vast land area which contains the most diverse soil landscapes in the world. Here, we integrated predictive soil mapping paradigm with adaptive depth function fitting, state-of-the-art ensemble machine learning and high-resolution soil-forming environment characterization in a highperformance parallel computing environment to generate 90-m resolution national gridded maps of nine soil properties(pH, organic carbon, nitrogen, phosphorus, potassium, cation exchange capacity, bulk density, coarse fragments, and thickness) at multiple depths across China. This was based on approximately5000 representative soil profiles collected in a recent national soil survey and a suite of detailed covariates to characterize soil-forming environments. The predictive accuracy ranged from very good to moderate(Model Efficiency Coefficients from 0.71 to 0.36) at 0–5 cm. The predictive accuracy for most soil properties declined with depth. Compared with previous soil maps, we achieved significantly more detailed and accurate predictions which could well represent soil variations across the territory and are a significant contribution to the GlobalSoilMap.net project. The relative importance of soil-forming factors in the predictions varied by specific soil property and depth, suggesting the complexity and non-stationarity of comprehensive multi-factor interactions in the process of soil development.
文摘The present paper describes an optimization work to obtain the properties related to a pyrolysis process in the solid material such as density, specific heat, conductivity of virgin and char, heat of pyrolysis and kinetic parameters used for deciding pyrolysis rate. A repulsive particle swarm optimization algorithm is used to obtain the pyrolysis-related properties. In the previous study all properties obtained only using a cone calorimeter but in this paper both the cone calorimeter and thermo gravimetric analysis (TGA) are used for precisely optimizing the pyrolysis properties. In the TGA test a very small mass is heated up and conduction and heat capacity in the specimen is negligible so kinetic parameters can first be optimized. Other pyrolysis-related properties such as virgin/char specific heat and conductivity and char density are also optimized in the cone calorimeter test with the already decided parameters in the TGA test.
