Li BaoGuo

Multi-scale spatial structure of heavy metals in agricultural soils in Beijing

To effectively investigate the spatial variability of heavy metals in soil, produce a higher quality spatial distribution map, and identify the potential pollution sources of heavy metals, geostatistics was employed to evaluate the effect of scale on spatial variability of heavy metals in Beijing agricultural soils. The results revealed that spatial variability of Cr, Ni, Zn, and Hg was dependent on scale. Validation of the optimality of theoretical semivariance and comparative analysis of the estimation accuracy demonstrated that the multi-scale nested model can reveal the spatial structure of heavy metals effectively and improve the estimation accuracy better than the single-scale method, thereby enabling production a higher quality spatial interpolation map. Thus, the multi-scale kriging nested model is a useful tool for revealing spatial variability of heavy metals in soils, while the spatial distribution maps allow the identification of hot spots with high concentrations of heavy metals.

Monte Carlo simulation of solar radiation in maize canopies and its visualisation

Abstract The spatial distribution of solar radiation casts important influences on eco‐physiological functions of plant canopies. A simulation model of the three‐dimensional (3D) effect of direct and indirect solar radiation in real maize canopies is developed from measured 3D canopy structure meshes. The model includes two procedures: parallel direct light pass and diffusive indirect radiation pass. The former one is based on the Monte Carlo ray tracing algorithm. After enough ray casting, the radiation is obtained. Then, the diffusive indirect radiation pass uses a modified Monte Carlo radiosity model to evaluate the diffusion distribution. Results indicate that simulated sun fleck ratio is significantly consistent with the measured dataset.