Distributed process modeling for regional assessment of coastal vulnerability to sea-level rise

Sea-level rise involves increases in the coastal processes of inundation and erosion which are affected by a complex interplay of physical environmental parameters at the coast. Many assessments of coastal vulnerability to sea-level rise have been detailed and localised in extent. There is a need for regional assessment techniques which identify areas vulnerable to sea-level rise. Four physical environmental parameters – elevation, exposure, aspect and slope, are modeled on a regional scale for the Northern Spencer Gulf (NSG) study area using commonly available low-resolution elevation data of 10 m contour interval and GIS-based spatial modeling techniques. For comparison, the same parameters are modeled on a fine-scale for the False Bay area within the NSG using high-resolution elevation data. Physical environmental parameters on the two scales are statistically compared to coastal vulnerability classes as identified by Harvey et al. [1] using the Spearman rank-correlation test and stepwise linear regression. Coastal vulnerability is strongly correlated with elevation and exposure at both scales and this relationship is only slightly stronger for the high resolution False Bay data. The results of this study suggest that regional scale distributed coastal process modeling may be suitable as a first cut in assessing coastal vulnerability to sea-level rise in tide-dominated, sedimentary coastal regions. Distributed coastal process modeling provides a suitable basis for the assessment of coastal vulnerability to sea-level rise of sufficient accuracy for on-ground management and priority-setting on a regional scale.  

Apportionment of Recycle to Industrial Reuser and Consumer

Reusable products (e.g., packaging) feature a finite lifetime: After a number of trips, if not discarded by the consumer, they are sent for material recycling by the industrial reuser. A method for analytical determination of recycling of no longer reusable products by the industrial reuser is proposed. Industrial reuser recycling is shown to depend nonlinearly on the lifetime and on the reuse retention factor, which is dimensionless and ranges from zero to one. As the retention factor increases, the industrial reuser becomes the main recycler, while recycling from consumer waste recovery disappears and vice versa as the ratio tends to zero. This implies that for highly reusable packaging, post-consumer collection and recycling systems may be unnecessary, and recycling should be credited to the refilling/packaging industry. The total number of reuse trips and the annual reuse frequency have no impact on the industrial reuser recycling level. The results may serve for environmental monitoring of packaging products for apportionment of recycling credit and of reuse/recycling incentives to industry and to post-consumer recycling systems and for planning and sizing of packaging waste recovery facilities (collection, transportation, separation).  

智能算法及其在环境预警中的应用

智能算法具有学习非线性问题的能力,可有效优化环境模型结构与参数,是环境预警的重要工具。重点分析了遗传算法和人工神经网络的相关特征,并以太湖蓝藻水华预报预警为例,介绍其在提高环境模型精度中的应用。  

Deterministic and Stochastic Simulations for Solid Waste Collection Systems – A SWIM Approach

The SWIM model is the first systems model in Australia that deals with integrated waste management systems. The main modelling approach adopted is simulation, which is based on both deterministic and stochastic models for collection systems.These models are described in this paper, after a number of modelling approaches are reviewed. An example of the application of the SWIM model is given, and planned extensions to the SWIM model are briefly outlined.