An efficient Chlorella sp.-Cupriavidus necator microcosm for phenol degradation and its cooperation mechanism.

A Chlorella sp.-Cupriavidus necator (C. necator) microcosm was artificially established for phenol degradation. The cooperation relationship between Chlorella sp. and C. necator was initially demonstrated, and then the effects of Chlorella sp./C. necator inoculation ratio, light intensity, temperature and pH on the performance of this microcosm were systematically evaluated and optimized. The optimal conditions for phenol degradation were as follows: a Chlorella sp./C. necator inoculation ratio of 1:1, a light intensity of 110 µmol m-2 s-1, a temperature in the range of 25-32 °C and a pH in the range of 5.5-7.5. Under optimal conditions, this microcosm could degrade phenol with a maximum concentration of 1200 mg L-1 within 60 h. It was found that only when the phenol concentration was reduced to the tolerance concentration of microalgae, that is, the last stage of phenol degradation, the cooperation effect could be generated, indicating that the tolerance of microalgae to phenol may be more important than its degradation performance. Comparative transcriptomic analysis was conducted to discuss the cooperation mechanism of this microcosm subject to high phenol concentrations. The up-regulation of genes involved in photosynthesis and carbon fixation of Chlorella sp. demonstrated the CO2 and O2 exchange between Chlorella sp. and C. necator and their cooperation relationship. This study suggests that this microcosm has great potential for the bioremediation of phenol contaminants.

Integrating modelling and smart sensors for environmental and human health.

Sensors are becoming ubiquitous in everyday life, generating data at an unprecedented rate and scale. However, models that assess impacts of human activities on environmental and human health, have typically been developed in contexts where data scarcity is the norm. Models are essential tools to understand processes, identify relationships, associations and causality, formalize stakeholder mental models, and to quantify the effects of prevention and interventions. They can help to explain data, as well as inform the deployment and location of sensors by identifying hotspots and areas of interest where data collection may achieve the best results. We identify a paradigm shift in how the integration of models and sensors can contribute to harnessing 'Big Data' and, more importantly, make the vital step from 'Big Data' to 'Big Information'. In this paper, we illustrate current developments and identify key research needs using human and environmental health challenges as an example.

Miniaturization of the flowing fluid electric conductivity logging technique.

An understanding of the hydraulic properties of the aquifer and the depth distribution of salts is critical for evaluating the potential of ground water for conjunctive water use and for maintaining suitable ground water quality in agricultural regions where ground water is used extensively for irrigation and drinking water. The electrical conductivity profiles recorded in a well using the flowing fluid electric conductivity (FEC) logging method can be analyzed to estimate interval-specific hydraulic conductivity and estimates of the salinity concentration with depth. However, operating irrigation wells commonly allow limited access, and the traditional equipment used for FEC logging cannot fit through the small access pipe intersecting the well. A modified, miniaturized FEC logging technique was developed for use in wells with limited access. In addition, a new method for injecting water over the entire screened interval of the well reduces the time required to perform FEC logging.