Treating water contaminants via heterogeneously catalyzed reduction reaction is a subject of growing interest due to its good activity and superior selectivity compared to conventional technology, yielding products that are non-toxic or substantially less toxic. This article reviews the application of catalytic reduction as a progressive approach to treat different types of contaminants in water, which covers hydrodehalogenation for wastewater treatment and hydrogenation of nitrate/nitrite for groundwater remediation. For hydrodehalogenation, an overview of the existing treatment technologies is provided with an assessment of the advantages of catalytic reduction over the conventional methodologies. Catalyst design for feasible catalytic reactions is considered with a critical analysis of the pertinent literature. For hydrogenation, hydrogenation of nitrate/nitrite contaminants in water is mainly focused. Several important nitrate reduction catalysts are discussed relating to their preparation method and catalytic performance. In addition, novel approach of catalytic reduction using in situ synthesized H2 evolved from water splitting reaction is illustrated. Finally, the challenges and perspective for the extensive application of catalytic reduction technology in water treatment are discussed. This review provides key information to our community to apply catalytic reduction approach for water treatment.
Physically based numerical modelling follows from the basic understanding of the underlying mechanisms and is often represented by a set of (partial differential) equations. It is one of the main approaches in population dynamics modelling. The emphasis of the model introduced in this paper is on the simulation of short-term spatial and temporal dynamics of harmful algal bloom (HAB) events. Total suspended matter (TSM) concentration is one of the dominant factors for harmful algal bloom (HAB) prediction in North Sea. However, the modelling of suspended matter contains a high degree of uncertainty in this area. Therefore, this research aims to achieve a better estimation for the short-term prediction of harmful algal bloom development in both space and time by using spatially distributed TSM retrieved from remotely sensed images as physically based model inputs. In order to supply complete spatially covered datasets for the physically based model instrument: generic ecological model (GEM), this research retrieves TSM information from MERIS images by means of proper estimation techniques including biharmonic splines and self-learning cellular automata. A better estimation of HAB spatial pattern development is achieved by adding spatially distributed TSM data as inputs to original GEM model, and it proved that chlorophyll-a concentration in this area is very sensitive to TSM concentration. 相似文献