Global sensitivity analysis of outputs over rice-growth process in ORYZA model

Dynamic crop models usually have a complex structure and a large number of parameters. Those parameter values usually cannot be directly measured, and they vary with crop cultivars, environmental conditions and managements. Thus, parameter estimation and model calibration are always difficult issues for crop models. Therefore, the quantification of parameter sensitivity and the identification of influential parameters are very important and useful. In this work, late-season rice was simulated with meteorological data in Nanchang, China. Furthermore, we conducted a sensitivity analysis of 20 selected parameters in ORYZA_V3 using the Extended FAST method. We presented the sensitivity results for four model outputs (LAI, WAGT, WST and WSO) at four development stages and the results for yield. Meanwhile, we compared the differences among the sensitivity results for the model outputs simulated in cold, normal and hot years. The uncertainty of output variables derived from parameter variation and weather conditions were also quantified. We found that the development rates, RGRLMN and FLV0.5 had strong effects on all model outputs in all conditions, and parameters WGRMX and SPGF had relative high effects on yield in cold year. Only LAI was sensitive to ASLA. Those influential parameters had unequal effects on different outputs, and they had different effects at four development stages. With the interaction effects of parameter variation and different weather conditions, the uncertainty of model outputs varied significantly. However, the weather conditions had negligible effects on the identification of influential parameters, although they had slight effects on the ranks of the parameters' sensitivity for outputs in the panicle-formation phase and the grain-filling phase, including yield at maturity. The results suggested that the influential parameters should be recalibrated in priority and fine-tuned with higher accuracy during model calibration.     

Artificial neural network modeling of thin layer drying behavior of municipal sewage sludge

The back-propagation (BP) and generalized regression neural network models (GRNN) were investigated to predict the thin layer drying behavior in municipal sewage sludge during hot air forced convection. The accuracy of the BP model to predict the moisture content of the sewage sludge thin layer during hot air forced convective drying was far higher than that of the GRNN model. The GRNN models could automatically determine the best smoothing parameters, which were 0.6 and 0.3 for predicting the moisture content and average temperature, respectively. The model type for predicting the average temperature of the sewage sludge thin layer was selected for different sample groups by comparing their MSE values or R² values. The GRNN model was suitable for predicting the average temperature corresponding to the sample groups at hot air velocity of 0.6 m/s, and drying temperatures of 100 °C, 160 °C; hot air velocity of 1.4 m/s, and drying temperatures of 130 °C, 140 °C; hot air velocity of 2.0 m/s, and drying temperatures of 150 °C, 160 °C. The average temperature for the other sample groups was best predicted by the BP model.     

Computational study of CO2 methanation over two-dimensional molybdenum carbide catalysts

Two-dimensional molybdenum carbide (2D-Mo₂C) is thought to be promising for catalytic hydrogenation of CO₂ to CH₄, but little is known about its catalytic reaction mechanism. In this work, we investigate the hydrogenation of CO₂ to CH₄ on 2D-Mo₂C using density functional theory. Our calculations show that Mo on the surface can efficiently decompose CO₂ to CO and O, and also H₂ to H. The hydrogenation of CO produces CHO that is readily deoxygenated to CH, and CH is selectively hydrogenated to produce CH₄. Interestingly, the embedded Ir₁ on 2D-Mo₂C can act as a single-atom promoter to improve the performance of CO₂ methanation, while on the other hand maintaining its high selectivity for CH₄. This work provides insight into the mechanism of 2D-Mo₂C-catalyzed CO₂ methanation reactions and suggests a strategy to improve the performance of such catalysts through single-atom promoters.     

Prediction model for methanation reaction conditions based on a state transition simulated annealing algorithm optimized extreme learning machine

Methanation is the core process of synthetic natural gas, the performance of the entire reaction system depends on precise values of the reaction condition parameters. Accurate predictions of the CO conversion rate of the methanation reaction can eliminate time-consuming and complex steps in experiments and speed up the discovery of the best reaction conditions. However, the methanation reaction is an uncertain, highly complex, and highly nonlinear process. Thus, this paper proposes a machine learning prediction model for the methanation reaction to facilitate the subsequent search for optimal reaction conditions. The reaction temperature, pressure, hydrogen–carbon ratio, water vapor content, CO₂ content, and space velocity were selected as the condition variables. The CO conversion rate was the optimization objective. An extreme learning machine (ELM) was selected as a prediction model. Because the input weights and bias matrices of the ELM are randomly generated, an ELM based on a state transition simulated annealing (STASA-ELM) algorithm is proposed. The STASA algorithm was used to optimize the ELM to improve the accuracy and stability of the model. Five additional sets of experimental data were designed for the experiment, and the error between the experimental and predicted values was small. Thus, the STASA-ELM algorithm can accurately predict the conversion of CO for different values of reaction conditions.     

大气能见度与湍流同步测量系统的软硬件协同设计

为了探测和研究大气能见度与湍流特征,应考虑大气湍流气团运动与气溶胶粒子之间存在的复杂关系。但传统测量仪器只能单独测量能见度或者大气湍流,忽略了二者之间的相互作用。基于布格-朗伯定律与光强起伏原理,设计并实现了大气能见度与湍流同步测量系统。实测结果表明系统可用,与同行业已有仪器测量结果相对误差为6.7%。相对于传统仪器,其设计更为简捷,成本更加低廉。     

中国近地面PM2.5 质量浓度卫星遥感数据集比较及历史趋势分析

随着对大气污染问题的日益重视, 监测大气颗粒物质量浓度也成为了热门研究领域。针对现行两种流行算法 (基于模式模拟和基于机器学习) 产生的近地面PM2:5 质量浓度科学数据集进行了比较分析, 利用城市年均PM2:5 监测 数据定量评估两数据集的不确定性, 并通过空间自相关分析对两数据集的空间合理性进行了评价。同时, 还利用标准 差椭圆分析研究了2000–2018 年间主要污染区域(北京、天津、河北、河南、山西、山东等地) PM2:5 的时空演变趋 势。结果表明, 基于机器学习算法产生的数据集(CHAP) 具有较高的精度, 适用于区域性空气质量研究; 而基于模式模 拟算法产生的数据集(vanDonkelaarA) 具有合理的空间分布, 更适合于大尺度、长时间的污染趋势分析。由标准差椭 圆分析发现, 2000–2018 年研究区域标准差椭圆中心的位置整体向东北方向移动; 2013 年前PM2:5 分布范围及年均值 在波动中呈现整体上升的趋势, 随后显著下降, 造成PM2:5 浓度下降的主要因素是有效管控措施的实施。研究结果为 中国区域的细颗粒物污染研究的数据集选取提供了参考依据, 为大气细颗粒物污染的防控提供科学支撑。     

Impacts of tree and building shades on the urban heat island: Combining remote sensing, 3D digital city and spatial regression approaches

The continued increase in average and extreme temperatures around the globe is expected to strike urban communities more harshly because of the urban heat island (UHI). Devising natural and design-based solutions to stem the rising heat has become an important urban planning issue. Recent studies have examined the impacts of 2D/3D urban land-use structures on land surface temperature (LST), but with little attention to the shades cast by 3D objects, such as buildings and trees. It is, however, known that shades are particularly relevant for controlling summertime temperatures. This study examines the role of urban shades created by trees and buildings, focusing on the effects of shade extent and location on LST mitigation. A realistic 3D digital representation of urban and suburban landscapes, combined with detailed 2D land cover information, is developed. Shadows projected on horizontal and vertical surfaces are obtained through GIS analysis, and then quantified as independent variables explaining LST variations over grids of varying sizes with spatial regression models. The estimation results show that the shades on different 3D surfaces, including building rooftops, sun-facing façades, not-sun-facing façades, and on 2D surfaces including roadways, other paved covers, and grass, have cooling effects of varying impact, showing that shades clearly modify the thermal effects of urban built-up surfaces. Tree canopy volume has distinct effects on LST via evapotranspiration. One of the estimated models is used, after validation, to simulate the LST impacts of neighborhood scenarios involving additional greening. The findings illustrate how urban planners can use the proposed methodology to design 3D land-use solutions for effective heat mitigation.