Harvesting of microalgae biomass from the phycoremediation process of greywater

The wide application of microalgae in the field of wastewater treatment and bioenergy source has improved research studies in the past years. Microalgae represent a good source of biomass and bio-products which are used in different medical and industrial activities, among them the production of high-valued products and biofuels. The present review focused on greywater treatment through the application of phycoremediation technique with microalgae and presented recent advances in technologies used for harvesting the microalgae biomass. The advantages and disadvantages of each method are discussed. The microbiological aspects of production, harvesting and utilization of microalgae biomass are viewed.     

Patterns of Spatial Autocorrelation in Stream Water Chemistry

Geostatistical models are typically based on symmetric straight-line distance, which fails to represent the spatial configuration, connectivity, directionality, and relative position of sites in a stream network. Freshwater ecologists have explored spatial patterns in stream networks using hydrologic distance measures and new geostatistical methodologies have recently been developed that enable directional hydrologic distance measures to be considered. The purpose of this study was to quantify patterns of spatial correlation in stream water chemistry using three distance measures: straight-line distance, symmetric hydrologic distance, and weighted asymmetric hydrologic distance. We used a dataset collected in Maryland, USA to develop both general linear models and geostatistical models (based on the three distance measures) for acid neutralizing capacity, conductivity, pH, nitrate, sulfate, temperature, dissolved oxygen, and dissolved organic carbon. The spatial AICC methodology allowed us to fit the autocorrelation and covariate parameters simultaneously and to select the model with the most support in the data. We used the universal kriging algorithm to generate geostatistical model predictions. We found that spatial correlation exists in stream chemistry data at a relatively coarse scale and that geostatistical models consistently improved the accuracy of model predictions. More than one distance measure performed well for most chemical response variables, but straight-line distance appears to be the most suitable distance measure for regional geostatistical modeling. It may be necessary to develop new survey designs that more fully capture spatial correlation at a variety of scales to improve the use of weighted asymmetric hydrologic distance measures in regional geostatistical models.